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Sample records for brain tumor segmentation

  1. Brain tumor segmentation with Deep Neural Networks.

    Science.gov (United States)

    Havaei, Mohammad; Davy, Axel; Warde-Farley, David; Biard, Antoine; Courville, Aaron; Bengio, Yoshua; Pal, Chris; Jodoin, Pierre-Marc; Larochelle, Hugo

    2017-01-01

    In this paper, we present a fully automatic brain tumor segmentation method based on Deep Neural Networks (DNNs). The proposed networks are tailored to glioblastomas (both low and high grade) pictured in MR images. By their very nature, these tumors can appear anywhere in the brain and have almost any kind of shape, size, and contrast. These reasons motivate our exploration of a machine learning solution that exploits a flexible, high capacity DNN while being extremely efficient. Here, we give a description of different model choices that we've found to be necessary for obtaining competitive performance. We explore in particular different architectures based on Convolutional Neural Networks (CNN), i.e. DNNs specifically adapted to image data. We present a novel CNN architecture which differs from those traditionally used in computer vision. Our CNN exploits both local features as well as more global contextual features simultaneously. Also, different from most traditional uses of CNNs, our networks use a final layer that is a convolutional implementation of a fully connected layer which allows a 40 fold speed up. We also describe a 2-phase training procedure that allows us to tackle difficulties related to the imbalance of tumor labels. Finally, we explore a cascade architecture in which the output of a basic CNN is treated as an additional source of information for a subsequent CNN. Results reported on the 2013 BRATS test data-set reveal that our architecture improves over the currently published state-of-the-art while being over 30 times faster. Copyright © 2016 Elsevier B.V. All rights reserved.

  2. Multifractal texture estimation for detection and segmentation of brain tumors.

    Science.gov (United States)

    Islam, Atiq; Reza, Syed M S; Iftekharuddin, Khan M

    2013-11-01

    A stochastic model for characterizing tumor texture in brain magnetic resonance (MR) images is proposed. The efficacy of the model is demonstrated in patient-independent brain tumor texture feature extraction and tumor segmentation in magnetic resonance images (MRIs). Due to complex appearance in MRI, brain tumor texture is formulated using a multiresolution-fractal model known as multifractional Brownian motion (mBm). Detailed mathematical derivation for mBm model and corresponding novel algorithm to extract spatially varying multifractal features are proposed. A multifractal feature-based brain tumor segmentation method is developed next. To evaluate efficacy, tumor segmentation performance using proposed multifractal feature is compared with that using Gabor-like multiscale texton feature. Furthermore, novel patient-independent tumor segmentation scheme is proposed by extending the well-known AdaBoost algorithm. The modification of AdaBoost algorithm involves assigning weights to component classifiers based on their ability to classify difficult samples and confidence in such classification. Experimental results for 14 patients with over 300 MRIs show the efficacy of the proposed technique in automatic segmentation of tumors in brain MRIs. Finally, comparison with other state-of-the art brain tumor segmentation works with publicly available low-grade glioma BRATS2012 dataset show that our segmentation results are more consistent and on the average outperforms these methods for the patients where ground truth is made available.

  3. [Tumor segmentation of brain MRI with adaptive bandwidth mean shift].

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    Hou, Xiaowen; Liu, Qi

    2014-10-01

    In order to get the adaptive bandwidth of mean shift to make the tumor segmentation of brain magnetic resonance imaging (MRI) to be more accurate, we in this paper present an advanced mean shift method. Firstly, we made use of the space characteristics of brain image to eliminate the impact on segmentation of skull; and then, based on the characteristics of spatial agglomeration of different tissues of brain (includes tumor), we applied edge points to get the optimal initial mean value and the respectively adaptive bandwidth, in order to improve the accuracy of tumor segmentation. The results of experiment showed that, contrast to the fixed bandwidth mean shift method, the method in this paper could segment the tumor more accurately.

  4. The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)

    DEFF Research Database (Denmark)

    Menze, Bjoern H.; Jakab, Andras; Bauer, Stefan

    2015-01-01

    In this paper we report the set-up and results of the Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) organized in conjunction with the MICCAI 2012 and 2013 conferences. Twenty state-of-the-art tumor segmentation algorithms were applied to a set of 65 multi-contrast MR scans of low......- and high-grade glioma patients – manually annotated by up to four raters – and to 65 comparable scans generated using tumor image simulation software. Quantitative evaluations revealed considerable disagreement between the human raters in segmenting various tumor sub-regions (Dice scores in the range 74...

  5. Multiscale CNNs for Brain Tumor Segmentation and Diagnosis

    Directory of Open Access Journals (Sweden)

    Liya Zhao

    2016-01-01

    Full Text Available Early brain tumor detection and diagnosis are critical to clinics. Thus segmentation of focused tumor area needs to be accurate, efficient, and robust. In this paper, we propose an automatic brain tumor segmentation method based on Convolutional Neural Networks (CNNs. Traditional CNNs focus only on local features and ignore global region features, which are both important for pixel classification and recognition. Besides, brain tumor can appear in any place of the brain and be any size and shape in patients. We design a three-stream framework named as multiscale CNNs which could automatically detect the optimum top-three scales of the image sizes and combine information from different scales of the regions around that pixel. Datasets provided by Multimodal Brain Tumor Image Segmentation Benchmark (BRATS organized by MICCAI 2013 are utilized for both training and testing. The designed multiscale CNNs framework also combines multimodal features from T1, T1-enhanced, T2, and FLAIR MRI images. By comparison with traditional CNNs and the best two methods in BRATS 2012 and 2013, our framework shows advances in brain tumor segmentation accuracy and robustness.

  6. Multiscale CNNs for Brain Tumor Segmentation and Diagnosis.

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    Zhao, Liya; Jia, Kebin

    2016-01-01

    Early brain tumor detection and diagnosis are critical to clinics. Thus segmentation of focused tumor area needs to be accurate, efficient, and robust. In this paper, we propose an automatic brain tumor segmentation method based on Convolutional Neural Networks (CNNs). Traditional CNNs focus only on local features and ignore global region features, which are both important for pixel classification and recognition. Besides, brain tumor can appear in any place of the brain and be any size and shape in patients. We design a three-stream framework named as multiscale CNNs which could automatically detect the optimum top-three scales of the image sizes and combine information from different scales of the regions around that pixel. Datasets provided by Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) organized by MICCAI 2013 are utilized for both training and testing. The designed multiscale CNNs framework also combines multimodal features from T1, T1-enhanced, T2, and FLAIR MRI images. By comparison with traditional CNNs and the best two methods in BRATS 2012 and 2013, our framework shows advances in brain tumor segmentation accuracy and robustness.

  7. Brain Tumor Segmentation Using Convolutional Neural Networks in MRI Images.

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    Pereira, Sergio; Pinto, Adriano; Alves, Victor; Silva, Carlos A

    2016-05-01

    Among brain tumors, gliomas are the most common and aggressive, leading to a very short life expectancy in their highest grade. Thus, treatment planning is a key stage to improve the quality of life of oncological patients. Magnetic resonance imaging (MRI) is a widely used imaging technique to assess these tumors, but the large amount of data produced by MRI prevents manual segmentation in a reasonable time, limiting the use of precise quantitative measurements in the clinical practice. So, automatic and reliable segmentation methods are required; however, the large spatial and structural variability among brain tumors make automatic segmentation a challenging problem. In this paper, we propose an automatic segmentation method based on Convolutional Neural Networks (CNN), exploring small 3 ×3 kernels. The use of small kernels allows designing a deeper architecture, besides having a positive effect against overfitting, given the fewer number of weights in the network. We also investigated the use of intensity normalization as a pre-processing step, which though not common in CNN-based segmentation methods, proved together with data augmentation to be very effective for brain tumor segmentation in MRI images. Our proposal was validated in the Brain Tumor Segmentation Challenge 2013 database (BRATS 2013), obtaining simultaneously the first position for the complete, core, and enhancing regions in Dice Similarity Coefficient metric (0.88, 0.83, 0.77) for the Challenge data set. Also, it obtained the overall first position by the online evaluation platform. We also participated in the on-site BRATS 2015 Challenge using the same model, obtaining the second place, with Dice Similarity Coefficient metric of 0.78, 0.65, and 0.75 for the complete, core, and enhancing regions, respectively.

  8. Multiresolution texture models for brain tumor segmentation in MRI.

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    Iftekharuddin, Khan M; Ahmed, Shaheen; Hossen, Jakir

    2011-01-01

    In this study we discuss different types of texture features such as Fractal Dimension (FD) and Multifractional Brownian Motion (mBm) for estimating random structures and varying appearance of brain tissues and tumors in magnetic resonance images (MRI). We use different selection techniques including KullBack - Leibler Divergence (KLD) for ranking different texture and intensity features. We then exploit graph cut, self organizing maps (SOM) and expectation maximization (EM) techniques to fuse selected features for brain tumors segmentation in multimodality T1, T2, and FLAIR MRI. We use different similarity metrics to evaluate quality and robustness of these selected features for tumor segmentation in MRI for real pediatric patients. We also demonstrate a non-patient-specific automated tumor prediction scheme by using improved AdaBoost classification based on these image features.

  9. Volumetric multimodality neural network for brain tumor segmentation

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    Silvana Castillo, Laura; Alexandra Daza, Laura; Carlos Rivera, Luis; Arbeláez, Pablo

    2017-11-01

    Brain lesion segmentation is one of the hardest tasks to be solved in computer vision with an emphasis on the medical field. We present a convolutional neural network that produces a semantic segmentation of brain tumors, capable of processing volumetric data along with information from multiple MRI modalities at the same time. This results in the ability to learn from small training datasets and highly imbalanced data. Our method is based on DeepMedic, the state of the art in brain lesion segmentation. We develop a new architecture with more convolutional layers, organized in three parallel pathways with different input resolution, and additional fully connected layers. We tested our method over the 2015 BraTS Challenge dataset, reaching an average dice coefficient of 84%, while the standard DeepMedic implementation reached 74%.

  10. Brain Tumor Segmentation Using a Generative Model with an RBM Prior on Tumor Shape

    DEFF Research Database (Denmark)

    Agn, Mikael; Puonti, Oula; Rosenschöld, Per Munck af

    2016-01-01

    In this paper, we present a fully automated generative method for brain tumor segmentation in multi-modal magnetic resonance images. The method is based on the type of generative model often used for segmenting healthy brain tissues, where tissues are modeled by Gaussian mixture models combined...... with a spatial atlas-based tissue prior. We extend this basic model with a tumor prior, which uses convolutional restricted Boltzmann machines (cRBMs) to model the shape of both tumor core and complete tumor, which includes edema and core. The cRBMs are trained on expert segmentations of training images, without...

  11. The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)

    Science.gov (United States)

    Jakab, Andras; Bauer, Stefan; Kalpathy-Cramer, Jayashree; Farahani, Keyvan; Kirby, Justin; Burren, Yuliya; Porz, Nicole; Slotboom, Johannes; Wiest, Roland; Lanczi, Levente; Gerstner, Elizabeth; Weber, Marc-André; Arbel, Tal; Avants, Brian B.; Ayache, Nicholas; Buendia, Patricia; Collins, D. Louis; Cordier, Nicolas; Corso, Jason J.; Criminisi, Antonio; Das, Tilak; Delingette, Hervé; Demiralp, Çağatay; Durst, Christopher R.; Dojat, Michel; Doyle, Senan; Festa, Joana; Forbes, Florence; Geremia, Ezequiel; Glocker, Ben; Golland, Polina; Guo, Xiaotao; Hamamci, Andac; Iftekharuddin, Khan M.; Jena, Raj; John, Nigel M.; Konukoglu, Ender; Lashkari, Danial; Mariz, José António; Meier, Raphael; Pereira, Sérgio; Precup, Doina; Price, Stephen J.; Raviv, Tammy Riklin; Reza, Syed M. S.; Ryan, Michael; Sarikaya, Duygu; Schwartz, Lawrence; Shin, Hoo-Chang; Shotton, Jamie; Silva, Carlos A.; Sousa, Nuno; Subbanna, Nagesh K.; Szekely, Gabor; Taylor, Thomas J.; Thomas, Owen M.; Tustison, Nicholas J.; Unal, Gozde; Vasseur, Flor; Wintermark, Max; Ye, Dong Hye; Zhao, Liang; Zhao, Binsheng; Zikic, Darko; Prastawa, Marcel; Reyes, Mauricio; Van Leemput, Koen

    2016-01-01

    In this paper we report the set-up and results of the Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) organized in conjunction with the MICCAI 2012 and 2013 conferences. Twenty state-of-the-art tumor segmentation algorithms were applied to a set of 65 multi-contrast MR scans of low- and high-grade glioma patients—manually annotated by up to four raters—and to 65 comparable scans generated using tumor image simulation software. Quantitative evaluations revealed considerable disagreement between the human raters in segmenting various tumor sub-regions (Dice scores in the range 74%–85%), illustrating the difficulty of this task. We found that different algorithms worked best for different sub-regions (reaching performance comparable to human inter-rater variability), but that no single algorithm ranked in the top for all sub-regions simultaneously. Fusing several good algorithms using a hierarchical majority vote yielded segmentations that consistently ranked above all individual algorithms, indicating remaining opportunities for further methodological improvements. The BRATS image data and manual annotations continue to be publicly available through an online evaluation system as an ongoing benchmarking resource. PMID:25494501

  12. Automatic metastatic brain tumor segmentation for stereotactic radiosurgery applications

    Science.gov (United States)

    Liu, Yan; Stojadinovic, Strahinja; Hrycushko, Brian; Wardak, Zabi; Lu, Weiguo; Yan, Yulong; Jiang, Steve B.; Timmerman, Robert; Abdulrahman, Ramzi; Nedzi, Lucien; Gu, Xuejun

    2016-12-01

    The objective of this study is to develop an automatic segmentation strategy for efficient and accurate metastatic brain tumor delineation on contrast-enhanced T1-weighted (T1c) magnetic resonance images (MRI) for stereotactic radiosurgery (SRS) applications. The proposed four-step automatic brain metastases segmentation strategy is comprised of pre-processing, initial contouring, contour evolution, and contour triage. First, T1c brain images are preprocessed to remove the skull. Second, an initial tumor contour is created using a multi-scaled adaptive threshold-based bounding box and a super-voxel clustering technique. Third, the initial contours are evolved to the tumor boundary using a regional active contour technique. Fourth, all detected false-positive contours are removed with geometric characterization. The segmentation process was validated on a realistic virtual phantom containing Gaussian or Rician noise. For each type of noise distribution, five different noise levels were tested. Twenty-one cases from the multimodal brain tumor image segmentation (BRATS) challenge dataset and fifteen clinical metastases cases were also included in validation. Segmentation performance was quantified by the Dice coefficient (DC), normalized mutual information (NMI), structural similarity (SSIM), Hausdorff distance (HD), mean value of surface-to-surface distance (MSSD) and standard deviation of surface-to-surface distance (SDSSD). In the numerical phantom study, the evaluation yielded a DC of 0.98  ±  0.01, an NMI of 0.97  ±  0.01, an SSIM of 0.999  ±  0.001, an HD of 2.2  ±  0.8 mm, an MSSD of 0.1  ±  0.1 mm, and an SDSSD of 0.3  ±  0.1 mm. The validation on the BRATS data resulted in a DC of 0.89  ±  0.08, which outperform the BRATS challenge algorithms. Evaluation on clinical datasets gave a DC of 0.86  ±  0.09, an NMI of 0.80  ±  0.11, an SSIM of 0.999  ±  0.001, an HD of 8

  13. Hybrid Clustering And Boundary Value Refinement for Tumor Segmentation using Brain MRI

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    Gupta, Anjali; Pahuja, Gunjan

    2017-08-01

    The method of brain tumor segmentation is the separation of tumor area from Brain Magnetic Resonance (MR) images. There are number of methods already exist for segmentation of brain tumor efficiently. However it’s tedious task to identify the brain tumor from MR images. The segmentation process is extraction of different tumor tissues such as active, tumor, necrosis, and edema from the normal brain tissues such as gray matter (GM), white matter (WM), as well as cerebrospinal fluid (CSF). As per the survey study, most of time the brain tumors are detected easily from brain MR image using region based approach but required level of accuracy, abnormalities classification is not predictable. The segmentation of brain tumor consists of many stages. Manually segmenting the tumor from brain MR images is very time consuming hence there exist many challenges in manual segmentation. In this research paper, our main goal is to present the hybrid clustering which consists of Fuzzy C-Means Clustering (for accurate tumor detection) and level set method(for handling complex shapes) for the detection of exact shape of tumor in minimal computational time. using this approach we observe that for a certain set of images 0.9412 sec of time is taken to detect tumor which is very less in comparison to recent existing algorithm i.e. Hybrid clustering (Fuzzy C-Means and K Means clustering).

  14. Patient-specific semi-supervised learning for postoperative brain tumor segmentation.

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    Meier, Raphael; Bauer, Stefan; Slotboom, Johannes; Wiest, Roland; Reyes, Mauricio

    2014-01-01

    In contrast to preoperative brain tumor segmentation, the problem of postoperative brain tumor segmentation has been rarely approached so far. We present a fully-automatic segmentation method using multimodal magnetic resonance image data and patient-specific semi-supervised learning. The idea behind our semi-supervised approach is to effectively fuse information from both pre- and postoperative image data of the same patient to improve segmentation of the postoperative image. We pose image segmentation as a classification problem and solve it by adopting a semi-supervised decision forest. The method is evaluated on a cohort of 10 high-grade glioma patients, with segmentation performance and computation time comparable or superior to a state-of-the-art brain tumor segmentation method. Moreover, our results confirm that the inclusion of preoperative MR images lead to a better performance regarding postoperative brain tumor segmentation.

  15. 3D variational brain tumor segmentation using Dirichlet priors on a clustered feature set.

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    Popuri, Karteek; Cobzas, Dana; Murtha, Albert; Jägersand, Martin

    2012-07-01

    Brain tumor segmentation is a required step before any radiation treatment or surgery. When performed manually, segmentation is time consuming and prone to human errors. Therefore, there have been significant efforts to automate the process. But, automatic tumor segmentation from MRI data is a particularly challenging task. Tumors have a large diversity in shape and appearance with intensities overlapping the normal brain tissues. In addition, an expanding tumor can also deflect and deform nearby tissue. In our work, we propose an automatic brain tumor segmentation method that addresses these last two difficult problems. We use the available MRI modalities (T1, T1c, T2) and their texture characteristics to construct a multidimensional feature set. Then, we extract clusters which provide a compact representation of the essential information in these features. The main idea in this work is to incorporate these clustered features into the 3D variational segmentation framework. In contrast to previous variational approaches, we propose a segmentation method that evolves the contour in a supervised fashion. The segmentation boundary is driven by the learned region statistics in the cluster space. We incorporate prior knowledge about the normal brain tissue appearance during the estimation of these region statistics. In particular, we use a Dirichlet prior that discourages the clusters from the normal brain region to be in the tumor region. This leads to a better disambiguation of the tumor from brain tissue. We evaluated the performance of our automatic segmentation method on 15 real MRI scans of brain tumor patients, with tumors that are inhomogeneous in appearance, small in size and in proximity to the major structures in the brain. Validation with the expert segmentation labels yielded encouraging results: Jaccard (58%), Precision (81%), Recall (67%), Hausdorff distance (24 mm). Using priors on the brain/tumor appearance, our proposed automatic 3D variational

  16. Performance analysis of unsupervised optimal fuzzy clustering algorithm for MRI brain tumor segmentation.

    Science.gov (United States)

    Blessy, S A Praylin Selva; Sulochana, C Helen

    2015-01-01

    Segmentation of brain tumor from Magnetic Resonance Imaging (MRI) becomes very complicated due to the structural complexities of human brain and the presence of intensity inhomogeneities. To propose a method that effectively segments brain tumor from MR images and to evaluate the performance of unsupervised optimal fuzzy clustering (UOFC) algorithm for segmentation of brain tumor from MR images. Segmentation is done by preprocessing the MR image to standardize intensity inhomogeneities followed by feature extraction, feature fusion and clustering. Different validation measures are used to evaluate the performance of the proposed method using different clustering algorithms. The proposed method using UOFC algorithm produces high sensitivity (96%) and low specificity (4%) compared to other clustering methods. Validation results clearly show that the proposed method with UOFC algorithm effectively segments brain tumor from MR images.

  17. Deep Learning and Texture-Based Semantic Label Fusion for Brain Tumor Segmentation.

    Science.gov (United States)

    Vidyaratne, L; Alam, M; Shboul, Z; Iftekharuddin, K M

    2018-01-01

    Brain tumor segmentation is a fundamental step in surgical treatment and therapy. Many hand-crafted and learning based methods have been proposed for automatic brain tumor segmentation from MRI. Studies have shown that these approaches have their inherent advantages and limitations. This work proposes a semantic label fusion algorithm by combining two representative state-of-the-art segmentation algorithms: texture based hand-crafted, and deep learning based methods to obtain robust tumor segmentation. We evaluate the proposed method using publicly available BRATS 2017 brain tumor segmentation challenge dataset. The results show that the proposed method offers improved segmentation by alleviating inherent weaknesses: extensive false positives in texture based method, and the false tumor tissue classification problem in deep learning method, respectively. Furthermore, we investigate the effect of patient's gender on the segmentation performance using a subset of validation dataset. Note the substantial improvement in brain tumor segmentation performance proposed in this work has recently enabled us to secure the first place by our group in overall patient survival prediction task at the BRATS 2017 challenge.

  18. A deep learning model integrating FCNNs and CRFs for brain tumor segmentation.

    Science.gov (United States)

    Zhao, Xiaomei; Wu, Yihong; Song, Guidong; Li, Zhenye; Zhang, Yazhuo; Fan, Yong

    2018-01-01

    Accurate and reliable brain tumor segmentation is a critical component in cancer diagnosis, treatment planning, and treatment outcome evaluation. Build upon successful deep learning techniques, a novel brain tumor segmentation method is developed by integrating fully convolutional neural networks (FCNNs) and Conditional Random Fields (CRFs) in a unified framework to obtain segmentation results with appearance and spatial consistency. We train a deep learning based segmentation model using 2D image patches and image slices in following steps: 1) training FCNNs using image patches; 2) training CRFs as Recurrent Neural Networks (CRF-RNN) using image slices with parameters of FCNNs fixed; and 3) fine-tuning the FCNNs and the CRF-RNN using image slices. Particularly, we train 3 segmentation models using 2D image patches and slices obtained in axial, coronal and sagittal views respectively, and combine them to segment brain tumors using a voting based fusion strategy. Our method could segment brain images slice-by-slice, much faster than those based on image patches. We have evaluated our method based on imaging data provided by the Multimodal Brain Tumor Image Segmentation Challenge (BRATS) 2013, BRATS 2015 and BRATS 2016. The experimental results have demonstrated that our method could build a segmentation model with Flair, T1c, and T2 scans and achieve competitive performance as those built with Flair, T1, T1c, and T2 scans. Copyright © 2017 Elsevier B.V. All rights reserved.

  19. Segmentation of tumors in magnetic resonance brain images using an interactive multiscale watershed algorithm

    DEFF Research Database (Denmark)

    Letteboer, Marloes M J; Olsen, Ole F; Dam, Erik B

    2004-01-01

    RATIONALE AND OBJECTIVE: This article presents the evaluation of an interactive multiscale watershed segmentation algorithm for segmenting tumors in magnetic resonance brain images of patients scheduled for neuronavigational procedures. MATERIALS AND METHODS: The watershed method is compared...... with manual delineation with respect to accuracy, repeatability, and efficiency. RESULTS: In the 20 patients included in this study, the measured volume of the tumors ranged from 2.7 to 81.9 cm(3). A comparison of the tumor volumes measured with watershed segmentation to the volumes measured with manual...

  20. Fast and robust brain tumor segmentation using level set method with multiple image information.

    Science.gov (United States)

    Lok, Ka Hei; Shi, Lin; Zhu, Xianlun; Wang, Defeng

    2017-01-01

    Brain tumor segmentation is a challenging task for its variation in intensity. The phenomenon is caused by the inhomogeneous content of tumor tissue and the choice of imaging modality. In 2010 Zhang developed the Selective Binary Gaussian Filtering Regularizing Level Set (SBGFRLS) model that combined the merits of edge-based and region-based segmentation. To improve the SBGFRLS method by modifying the singed pressure force (SPF) term with multiple image information and demonstrate effectiveness of proposed method on clinical images. In original SBGFRLS model, the contour evolution direction mainly depends on the SPF. By introducing a directional term in SPF, the metric could control the evolution direction. The SPF is altered by statistic values enclosed by the contour. This concept can be extended to jointly incorporate multiple image information. The new SPF term is expected to bring a solution for blur edge problem in brain tumor segmentation. The proposed method is validated with clinical images including pre- and post-contrast magnetic resonance images. The accuracy and robustness is compared with sensitivity, specificity, DICE similarity coefficient and Jaccard similarity index. Experimental results show improvement, in particular the increase of sensitivity at the same specificity, in segmenting all types of tumors except for the diffused tumor. The novel brain tumor segmentation method is clinical-oriented with fast, robust and accurate implementation and a minimal user interaction. The method effectively segmented homogeneously enhanced, non-enhanced, heterogeneously-enhanced, and ring-enhanced tumor under MR imaging. Though the method is limited by identifying edema and diffuse tumor, several possible solutions are suggested to turn the curve evolution into a fully functional clinical diagnosis tool.

  1. Brain tumor segmentation using holistically nested neural networks in MRI images.

    Science.gov (United States)

    Zhuge, Ying; Krauze, Andra V; Ning, Holly; Cheng, Jason Y; Arora, Barbara C; Camphausen, Kevin; Miller, Robert W

    2017-10-01

    Gliomas are rapidly progressive, neurologically devastating, largely fatal brain tumors. Magnetic resonance imaging (MRI) is a widely used technique employed in the diagnosis and management of gliomas in clinical practice. MRI is also the standard imaging modality used to delineate the brain tumor target as part of treatment planning for the administration of radiation therapy. Despite more than 20 yr of research and development, computational brain tumor segmentation in MRI images remains a challenging task. We are presenting a novel method of automatic image segmentation based on holistically nested neural networks that could be employed for brain tumor segmentation of MRI images. Two preprocessing techniques were applied to MRI images. The N4ITK method was employed for correction of bias field distortion. A novel landmark-based intensity normalization method was developed so that tissue types have a similar intensity scale in images of different subjects for the same MRI protocol. The holistically nested neural networks (HNN), which extend from the convolutional neural networks (CNN) with a deep supervision through an additional weighted-fusion output layer, was trained to learn the multiscale and multilevel hierarchical appearance representation of the brain tumor in MRI images and was subsequently applied to produce a prediction map of the brain tumor on test images. Finally, the brain tumor was obtained through an optimum thresholding on the prediction map. The proposed method was evaluated on both the Multimodal Brain Tumor Image Segmentation (BRATS) Benchmark 2013 training datasets, and clinical data from our institute. A dice similarity coefficient (DSC) and sensitivity of 0.78 and 0.81 were achieved on 20 BRATS 2013 training datasets with high-grade gliomas (HGG), based on a two-fold cross-validation. The HNN model built on the BRATS 2013 training data was applied to ten clinical datasets with HGG from a locally developed database. DSC and sensitivity of

  2. SU-C-BRA-06: Automatic Brain Tumor Segmentation for Stereotactic Radiosurgery Applications

    Energy Technology Data Exchange (ETDEWEB)

    Liu, Y; Stojadinovic, S; Jiang, S; Timmerman, R; Abdulrahman, R; Nedzi, L; Gu, X [UT Southwestern Medical Center, Dallas, TX (United States)

    2016-06-15

    Purpose: Stereotactic radiosurgery (SRS), which delivers a potent dose of highly conformal radiation to the target in a single fraction, requires accurate tumor delineation for treatment planning. We present an automatic segmentation strategy, that synergizes intensity histogram thresholding, super-voxel clustering, and level-set based contour evolving methods to efficiently and accurately delineate SRS brain tumors on contrast-enhance T1-weighted (T1c) Magnetic Resonance Images (MRI). Methods: The developed auto-segmentation strategy consists of three major steps. Firstly, tumor sites are localized through 2D slice intensity histogram scanning. Then, super voxels are obtained through clustering the corresponding voxels in 3D with reference to the similarity metrics composited from spatial distance and intensity difference. The combination of the above two could generate the initial contour surface. Finally, a localized region active contour model is utilized to evolve the surface to achieve the accurate delineation of the tumors. The developed method was evaluated on numerical phantom data, synthetic BRATS (Multimodal Brain Tumor Image Segmentation challenge) data, and clinical patients’ data. The auto-segmentation results were quantitatively evaluated by comparing to ground truths with both volume and surface similarity metrics. Results: DICE coefficient (DC) was performed as a quantitative metric to evaluate the auto-segmentation in the numerical phantom with 8 tumors. DCs are 0.999±0.001 without noise, 0.969±0.065 with Rician noise and 0.976±0.038 with Gaussian noise. DC, NMI (Normalized Mutual Information), SSIM (Structural Similarity) and Hausdorff distance (HD) were calculated as the metrics for the BRATS and patients’ data. Assessment of BRATS data across 25 tumor segmentation yield DC 0.886±0.078, NMI 0.817±0.108, SSIM 0.997±0.002, and HD 6.483±4.079mm. Evaluation on 8 patients with total 14 tumor sites yield DC 0.872±0.070, NMI 0.824±0

  3. SU-C-BRA-06: Automatic Brain Tumor Segmentation for Stereotactic Radiosurgery Applications

    International Nuclear Information System (INIS)

    Liu, Y; Stojadinovic, S; Jiang, S; Timmerman, R; Abdulrahman, R; Nedzi, L; Gu, X

    2016-01-01

    Purpose: Stereotactic radiosurgery (SRS), which delivers a potent dose of highly conformal radiation to the target in a single fraction, requires accurate tumor delineation for treatment planning. We present an automatic segmentation strategy, that synergizes intensity histogram thresholding, super-voxel clustering, and level-set based contour evolving methods to efficiently and accurately delineate SRS brain tumors on contrast-enhance T1-weighted (T1c) Magnetic Resonance Images (MRI). Methods: The developed auto-segmentation strategy consists of three major steps. Firstly, tumor sites are localized through 2D slice intensity histogram scanning. Then, super voxels are obtained through clustering the corresponding voxels in 3D with reference to the similarity metrics composited from spatial distance and intensity difference. The combination of the above two could generate the initial contour surface. Finally, a localized region active contour model is utilized to evolve the surface to achieve the accurate delineation of the tumors. The developed method was evaluated on numerical phantom data, synthetic BRATS (Multimodal Brain Tumor Image Segmentation challenge) data, and clinical patients’ data. The auto-segmentation results were quantitatively evaluated by comparing to ground truths with both volume and surface similarity metrics. Results: DICE coefficient (DC) was performed as a quantitative metric to evaluate the auto-segmentation in the numerical phantom with 8 tumors. DCs are 0.999±0.001 without noise, 0.969±0.065 with Rician noise and 0.976±0.038 with Gaussian noise. DC, NMI (Normalized Mutual Information), SSIM (Structural Similarity) and Hausdorff distance (HD) were calculated as the metrics for the BRATS and patients’ data. Assessment of BRATS data across 25 tumor segmentation yield DC 0.886±0.078, NMI 0.817±0.108, SSIM 0.997±0.002, and HD 6.483±4.079mm. Evaluation on 8 patients with total 14 tumor sites yield DC 0.872±0.070, NMI 0.824±0

  4. Method of image segmentation using a neural network. Application to MR imaging of brain tumors

    International Nuclear Information System (INIS)

    Engler, E.; Gautherie, M.

    1992-01-01

    An original method of numerical images segmentation has been developed. This method is based on pixel clustering using a formal neural network configurated by supervised learning of pre-classified examples. The method has been applied to series of MR images of brain tumors (gliomas) with a view to proceed with a 3D-extraction of the tumor volume. This study is part of a project on cancer thermotherapy including the development of a scan-focused ultrasound system of tumor heating and a 3D-numerical thermal model

  5. Automated brain tumor segmentation in magnetic resonance imaging based on sliding-window technique and symmetry analysis.

    Science.gov (United States)

    Lian, Yanyun; Song, Zhijian

    2014-01-01

    Brain tumor segmentation from magnetic resonance imaging (MRI) is an important step toward surgical planning, treatment planning, monitoring of therapy. However, manual tumor segmentation commonly used in clinic is time-consuming and challenging, and none of the existed automated methods are highly robust, reliable and efficient in clinic application. An accurate and automated tumor segmentation method has been developed for brain tumor segmentation that will provide reproducible and objective results close to manual segmentation results. Based on the symmetry of human brain, we employed sliding-window technique and correlation coefficient to locate the tumor position. At first, the image to be segmented was normalized, rotated, denoised, and bisected. Subsequently, through vertical and horizontal sliding-windows technique in turn, that is, two windows in the left and the right part of brain image moving simultaneously pixel by pixel in two parts of brain image, along with calculating of correlation coefficient of two windows, two windows with minimal correlation coefficient were obtained, and the window with bigger average gray value is the location of tumor and the pixel with biggest gray value is the locating point of tumor. At last, the segmentation threshold was decided by the average gray value of the pixels in the square with center at the locating point and 10 pixels of side length, and threshold segmentation and morphological operations were used to acquire the final tumor region. The method was evaluated on 3D FSPGR brain MR images of 10 patients. As a result, the average ratio of correct location was 93.4% for 575 slices containing tumor, the average Dice similarity coefficient was 0.77 for one scan, and the average time spent on one scan was 40 seconds. An fully automated, simple and efficient segmentation method for brain tumor is proposed and promising for future clinic use. Correlation coefficient is a new and effective feature for tumor location.

  6. Multimodal brain-tumor segmentation based on Dirichlet process mixture model with anisotropic diffusion and Markov random field prior.

    Science.gov (United States)

    Lu, Yisu; Jiang, Jun; Yang, Wei; Feng, Qianjin; Chen, Wufan

    2014-01-01

    Brain-tumor segmentation is an important clinical requirement for brain-tumor diagnosis and radiotherapy planning. It is well-known that the number of clusters is one of the most important parameters for automatic segmentation. However, it is difficult to define owing to the high diversity in appearance of tumor tissue among different patients and the ambiguous boundaries of lesions. In this study, a nonparametric mixture of Dirichlet process (MDP) model is applied to segment the tumor images, and the MDP segmentation can be performed without the initialization of the number of clusters. Because the classical MDP segmentation cannot be applied for real-time diagnosis, a new nonparametric segmentation algorithm combined with anisotropic diffusion and a Markov random field (MRF) smooth constraint is proposed in this study. Besides the segmentation of single modal brain-tumor images, we developed the algorithm to segment multimodal brain-tumor images by the magnetic resonance (MR) multimodal features and obtain the active tumor and edema in the same time. The proposed algorithm is evaluated using 32 multimodal MR glioma image sequences, and the segmentation results are compared with other approaches. The accuracy and computation time of our algorithm demonstrates very impressive performance and has a great potential for practical real-time clinical use.

  7. Brain Tumors

    Science.gov (United States)

    A brain tumor is a growth of abnormal cells in the tissues of the brain. Brain tumors can be benign, with no cancer cells, ... cancer cells that grow quickly. Some are primary brain tumors, which start in the brain. Others are ...

  8. Development of image-processing software for automatic segmentation of brain tumors in MR images

    Directory of Open Access Journals (Sweden)

    C Vijayakumar

    2011-01-01

    Full Text Available Most of the commercially available software for brain tumor segmentation have limited functionality and frequently lack the careful validation that is required for clinical studies. We have developed an image-analysis software package called ′Prometheus,′ which performs neural system-based segmentation operations on MR images using pre-trained information. The software also has the capability to improve its segmentation performance by using the training module of the neural system. The aim of this article is to present the design and modules of this software. The segmentation module of Prometheus can be used primarily for image analysis in MR images. Prometheus was validated against manual segmentation by a radiologist and its mean sensitivity and specificity was found to be 85.71±4.89% and 93.2±2.87%, respectively. Similarly, the mean segmentation accuracy and mean correspondence ratio was found to be 92.35±3.37% and 0.78±0.046, respectively.

  9. Deep learning for segmentation of brain tumors: Impact of cross-institutional training and testing.

    Science.gov (United States)

    AlBadawy, Ehab A; Saha, Ashirbani; Mazurowski, Maciej A

    2018-03-01

    Convolutional neural networks (CNNs) are commonly used for segmentation of brain tumors. In this work, we assess the effect of cross-institutional training on the performance of CNNs. We selected 44 glioblastoma (GBM) patients from two institutions in The Cancer Imaging Archive dataset. The images were manually annotated by outlining each tumor component to form ground truth. To automatically segment the tumors in each patient, we trained three CNNs: (a) one using data for patients from the same institution as the test data, (b) one using data for the patients from the other institution and (c) one using data for the patients from both of the institutions. The performance of the trained models was evaluated using Dice similarity coefficients as well as Average Hausdorff Distance between the ground truth and automatic segmentations. The 10-fold cross-validation scheme was used to compare the performance of different approaches. Performance of the model significantly decreased (P < 0.0001) when it was trained on data from a different institution (dice coefficients: 0.68 ± 0.19 and 0.59 ± 0.19) as compared to training with data from the same institution (dice coefficients: 0.72 ± 0.17 and 0.76 ± 0.12). This trend persisted for segmentation of the entire tumor as well as its individual components. There is a very strong effect of selecting data for training on performance of CNNs in a multi-institutional setting. Determination of the reasons behind this effect requires additional comprehensive investigation. © 2018 American Association of Physicists in Medicine.

  10. FLAIR lesion segmentation: Application in patients with brain tumors and acute ischemic stroke

    Energy Technology Data Exchange (ETDEWEB)

    Artzi, Moran, E-mail: artzimy@gmail.com [The Functional Brain Center, The Wohl Institute for Advanced Imaging, Tel Aviv Sourasky Medical Center, Tel Aviv (Israel); Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv (Israel); Aizenstein, Orna, E-mail: ornaaize@gmail.com [The Functional Brain Center, The Wohl Institute for Advanced Imaging, Tel Aviv Sourasky Medical Center, Tel Aviv (Israel); Jonas-Kimchi, Tali, E-mail: talijk@tlvmc.gov.il [Radiology Department, Tel Aviv Sourasky Medical Center, Tel Aviv (Israel); Myers, Vicki, E-mail: vicki_myers@hotmail.com [The Functional Brain Center, The Wohl Institute for Advanced Imaging, Tel Aviv Sourasky Medical Center, Tel Aviv (Israel); Hallevi, Hen, E-mail: hen.hallevi@gmail.com [Neurology Department, Tel Aviv Sourasky Medical Center, Tel Aviv (Israel); Ben Bashat, Dafna, E-mail: dafnab@tlvmc.gov.il [The Functional Brain Center, The Wohl Institute for Advanced Imaging, Tel Aviv Sourasky Medical Center, Tel Aviv (Israel); Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv (Israel)

    2013-09-15

    Background: Lesion size in fluid attenuation inversion recovery (FLAIR) images is an important clinical parameter for patient assessment and follow-up. Although manual delineation of lesion areas considered as ground truth, it is time-consuming, highly user-dependent and difficult to perform in areas of indistinct borders. In this study, an automatic methodology for FLAIR lesion segmentation is proposed, and its application in patients with brain tumors undergoing therapy; and in patients following stroke is demonstrated. Materials and methods: FLAIR lesion segmentation was performed in 57 magnetic resonance imaging (MRI) data sets obtained from 44 patients: 28 patients with primary brain tumors; 5 patients with recurrent-progressive glioblastoma (rGB) who were scanned longitudinally during anti-angiogenic therapy (18 MRI scans); and 11 patients following ischemic stroke. Results: FLAIR lesion segmentation was obtained in all patients. When compared to manual delineation, a high visual similarity was observed, with an absolute relative volume difference of 16.80% and 20.96% and a volumetric overlap error of 24.87% and 27.50% obtained for two raters: accepted values for automatic methods. Quantitative measurements of the segmented lesion volumes were in line with qualitative radiological assessment in four patients who received anti-anogiogenic drugs. In stroke patients the proposed methodology enabled identification of the ischemic lesion and differentiation from other FLAIR hyperintense areas, such as pre-existing disease. Conclusion: This study proposed a replicable methodology for FLAIR lesion detection and quantification and for discrimination between lesion of interest and pre-existing disease. Results from this study show the wide clinical applications of this methodology in research and clinical practice.

  11. FLAIR lesion segmentation: Application in patients with brain tumors and acute ischemic stroke

    International Nuclear Information System (INIS)

    Artzi, Moran; Aizenstein, Orna; Jonas-Kimchi, Tali; Myers, Vicki; Hallevi, Hen; Ben Bashat, Dafna

    2013-01-01

    Background: Lesion size in fluid attenuation inversion recovery (FLAIR) images is an important clinical parameter for patient assessment and follow-up. Although manual delineation of lesion areas considered as ground truth, it is time-consuming, highly user-dependent and difficult to perform in areas of indistinct borders. In this study, an automatic methodology for FLAIR lesion segmentation is proposed, and its application in patients with brain tumors undergoing therapy; and in patients following stroke is demonstrated. Materials and methods: FLAIR lesion segmentation was performed in 57 magnetic resonance imaging (MRI) data sets obtained from 44 patients: 28 patients with primary brain tumors; 5 patients with recurrent-progressive glioblastoma (rGB) who were scanned longitudinally during anti-angiogenic therapy (18 MRI scans); and 11 patients following ischemic stroke. Results: FLAIR lesion segmentation was obtained in all patients. When compared to manual delineation, a high visual similarity was observed, with an absolute relative volume difference of 16.80% and 20.96% and a volumetric overlap error of 24.87% and 27.50% obtained for two raters: accepted values for automatic methods. Quantitative measurements of the segmented lesion volumes were in line with qualitative radiological assessment in four patients who received anti-anogiogenic drugs. In stroke patients the proposed methodology enabled identification of the ischemic lesion and differentiation from other FLAIR hyperintense areas, such as pre-existing disease. Conclusion: This study proposed a replicable methodology for FLAIR lesion detection and quantification and for discrimination between lesion of interest and pre-existing disease. Results from this study show the wide clinical applications of this methodology in research and clinical practice

  12. Deep learning for segmentation of brain tumors: can we train with images from different institutions?

    Science.gov (United States)

    Paredes, David; Saha, Ashirbani; Mazurowski, Maciej A.

    2017-03-01

    Deep learning and convolutional neural networks (CNNs) in particular are increasingly popular tools for segmentation and classification of medical images. CNNs were shown to be successful for segmentation of brain tumors into multiple regions or labels. However, in the environment which fosters data-sharing and collection of multi-institutional datasets, a question arises: does training with data from another institution with potentially different imaging equipment, contrast protocol, and patient population impact the segmentation performance of the CNN? Our study presents preliminary data towards answering this question. Specifically, we used MRI data of glioblastoma (GBM) patients for two institutions present in The Cancer Imaging Archive. We performed a process of training and testing CNN multiple times such that half of the time the CNN was tested on data from the same institution that was used for training and half of the time it was tested on another institution, keeping the training and testing set size constant. We observed a decrease in performance as measured by Dice coefficient when the CNN was trained with data from a different institution as compared to training with data from the same institution. The changes in performance for the entire tumor and for four different labels within the tumor were: 0.72 to 0.65 (p=0.06), 0.61 to 0.58 (p=0.49), 0.54 to 0.51 (p=0.82), 0.31 to 0.24 (p<0.03), and 0.43 to 0.31(p<0.003) respectively. In summary, we found that while data across institutions can be used for development of CNNs, this might be associated with a decrease in performance.

  13. PCA based clustering for brain tumor segmentation of T1w MRI images.

    Science.gov (United States)

    Kaya, Irem Ersöz; Pehlivanlı, Ayça Çakmak; Sekizkardeş, Emine Gezmez; Ibrikci, Turgay

    2017-03-01

    Medical images are huge collections of information that are difficult to store and process consuming extensive computing time. Therefore, the reduction techniques are commonly used as a data pre-processing step to make the image data less complex so that a high-dimensional data can be identified by an appropriate low-dimensional representation. PCA is one of the most popular multivariate methods for data reduction. This paper is focused on T1-weighted MRI images clustering for brain tumor segmentation with dimension reduction by different common Principle Component Analysis (PCA) algorithms. Our primary aim is to present a comparison between different variations of PCA algorithms on MRIs for two cluster methods. Five most common PCA algorithms; namely the conventional PCA, Probabilistic Principal Component Analysis (PPCA), Expectation Maximization Based Principal Component Analysis (EM-PCA), Generalize Hebbian Algorithm (GHA), and Adaptive Principal Component Extraction (APEX) were applied to reduce dimensionality in advance of two clustering algorithms, K-Means and Fuzzy C-Means. In the study, the T1-weighted MRI images of the human brain with brain tumor were used for clustering. In addition to the original size of 512 lines and 512 pixels per line, three more different sizes, 256 × 256, 128 × 128 and 64 × 64, were included in the study to examine their effect on the methods. The obtained results were compared in terms of both the reconstruction errors and the Euclidean distance errors among the clustered images containing the same number of principle components. According to the findings, the PPCA obtained the best results among all others. Furthermore, the EM-PCA and the PPCA assisted K-Means algorithm to accomplish the best clustering performance in the majority as well as achieving significant results with both clustering algorithms for all size of T1w MRI images. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

  14. A Generative Probabilistic Model and Discriminative Extensions for Brain Lesion Segmentation - With Application to Tumor and Stroke

    DEFF Research Database (Denmark)

    Menze, Bjoern H.; Van Leemput, Koen; Lashkari, Danial

    2016-01-01

    We introduce a generative probabilistic model for segmentation of brain lesions in multi-dimensional images that generalizes the EM segmenter, a common approach for modelling brain images using Gaussian mixtures and a probabilistic tissue atlas that employs expectation-maximization (EM...... jointly from the image data. It delineates lesion areas individually in each channel, allowing for differences in lesion appearance across modalities, an important feature of many brain tumor imaging sequences. We also propose discriminative model extensions to map the output of the generative model...... patient scans, and multimodal brain images of patients with acute and subacute ischemic stroke. We find the generative model that has been designed for tumor lesions to generalize well to stroke images, and the extended discriminative-discriminative model to be one of the top ranking methods in the BRATS...

  15. Semi-automated brain tumor segmentation on multi-parametric MRI using regularized non-negative matrix factorization.

    Science.gov (United States)

    Sauwen, Nicolas; Acou, Marjan; Sima, Diana M; Veraart, Jelle; Maes, Frederik; Himmelreich, Uwe; Achten, Eric; Huffel, Sabine Van

    2017-05-04

    Segmentation of gliomas in multi-parametric (MP-)MR images is challenging due to their heterogeneous nature in terms of size, appearance and location. Manual tumor segmentation is a time-consuming task and clinical practice would benefit from (semi-) automated segmentation of the different tumor compartments. We present a semi-automated framework for brain tumor segmentation based on non-negative matrix factorization (NMF) that does not require prior training of the method. L1-regularization is incorporated into the NMF objective function to promote spatial consistency and sparseness of the tissue abundance maps. The pathological sources are initialized through user-defined voxel selection. Knowledge about the spatial location of the selected voxels is combined with tissue adjacency constraints in a post-processing step to enhance segmentation quality. The method is applied to an MP-MRI dataset of 21 high-grade glioma patients, including conventional, perfusion-weighted and diffusion-weighted MRI. To assess the effect of using MP-MRI data and the L1-regularization term, analyses are also run using only conventional MRI and without L1-regularization. Robustness against user input variability is verified by considering the statistical distribution of the segmentation results when repeatedly analyzing each patient's dataset with a different set of random seeding points. Using L1-regularized semi-automated NMF segmentation, mean Dice-scores of 65%, 74 and 80% are found for active tumor, the tumor core and the whole tumor region. Mean Hausdorff distances of 6.1 mm, 7.4 mm and 8.2 mm are found for active tumor, the tumor core and the whole tumor region. Lower Dice-scores and higher Hausdorff distances are found without L1-regularization and when only considering conventional MRI data. Based on the mean Dice-scores and Hausdorff distances, segmentation results are competitive with state-of-the-art in literature. Robust results were found for most patients, although

  16. A semi-automated volumetric software for segmentation and perfusion parameter quantification of brain tumors using 320-row multidetector computed tomography: a validation study

    Energy Technology Data Exchange (ETDEWEB)

    Chae, Soo Young; Suh, Sangil; Ryoo, Inseon; Park, Arim; Seol, Hae Young [Korea University Guro Hospital, Department of Radiology, Seoul (Korea, Republic of); Noh, Kyoung Jin [Soonchunhyang University, Department of Electronic Engineering, Asan (Korea, Republic of); Shim, Hackjoon [Toshiba Medical Systems Korea Co., Seoul (Korea, Republic of)

    2017-05-15

    We developed a semi-automated volumetric software, NPerfusion, to segment brain tumors and quantify perfusion parameters on whole-brain CT perfusion (WBCTP) images. The purpose of this study was to assess the feasibility of the software and to validate its performance compared with manual segmentation. Twenty-nine patients with pathologically proven brain tumors who underwent preoperative WBCTP between August 2012 and February 2015 were included. Three perfusion parameters, arterial flow (AF), equivalent blood volume (EBV), and Patlak flow (PF, which is a measure of permeability of capillaries), of brain tumors were generated by a commercial software and then quantified volumetrically by NPerfusion, which also semi-automatically segmented tumor boundaries. The quantification was validated by comparison with that of manual segmentation in terms of the concordance correlation coefficient and Bland-Altman analysis. With NPerfusion, we successfully performed segmentation and quantified whole volumetric perfusion parameters of all 29 brain tumors that showed consistent perfusion trends with previous studies. The validation of the perfusion parameter quantification exhibited almost perfect agreement with manual segmentation, with Lin concordance correlation coefficients (ρ {sub c}) for AF, EBV, and PF of 0.9988, 0.9994, and 0.9976, respectively. On Bland-Altman analysis, most differences between this software and manual segmentation on the commercial software were within the limit of agreement. NPerfusion successfully performs segmentation of brain tumors and calculates perfusion parameters of brain tumors. We validated this semi-automated segmentation software by comparing it with manual segmentation. NPerfusion can be used to calculate volumetric perfusion parameters of brain tumors from WBCTP. (orig.)

  17. A semi-automated volumetric software for segmentation and perfusion parameter quantification of brain tumors using 320-row multidetector computed tomography: a validation study.

    Science.gov (United States)

    Chae, Soo Young; Suh, Sangil; Ryoo, Inseon; Park, Arim; Noh, Kyoung Jin; Shim, Hackjoon; Seol, Hae Young

    2017-05-01

    We developed a semi-automated volumetric software, NPerfusion, to segment brain tumors and quantify perfusion parameters on whole-brain CT perfusion (WBCTP) images. The purpose of this study was to assess the feasibility of the software and to validate its performance compared with manual segmentation. Twenty-nine patients with pathologically proven brain tumors who underwent preoperative WBCTP between August 2012 and February 2015 were included. Three perfusion parameters, arterial flow (AF), equivalent blood volume (EBV), and Patlak flow (PF, which is a measure of permeability of capillaries), of brain tumors were generated by a commercial software and then quantified volumetrically by NPerfusion, which also semi-automatically segmented tumor boundaries. The quantification was validated by comparison with that of manual segmentation in terms of the concordance correlation coefficient and Bland-Altman analysis. With NPerfusion, we successfully performed segmentation and quantified whole volumetric perfusion parameters of all 29 brain tumors that showed consistent perfusion trends with previous studies. The validation of the perfusion parameter quantification exhibited almost perfect agreement with manual segmentation, with Lin concordance correlation coefficients (ρ c ) for AF, EBV, and PF of 0.9988, 0.9994, and 0.9976, respectively. On Bland-Altman analysis, most differences between this software and manual segmentation on the commercial software were within the limit of agreement. NPerfusion successfully performs segmentation of brain tumors and calculates perfusion parameters of brain tumors. We validated this semi-automated segmentation software by comparing it with manual segmentation. NPerfusion can be used to calculate volumetric perfusion parameters of brain tumors from WBCTP.

  18. A generative probabilistic model and discriminative extensions for brain lesion segmentation – with application to tumor and stroke

    Science.gov (United States)

    Menze, Bjoern H.; Van Leemput, Koen; Lashkari, Danial; Riklin-Raviv, Tammy; Geremia, Ezequiel; Alberts, Esther; Gruber, Philipp; Wegener, Susanne; Weber, Marc-André; Székely, Gabor; Ayache, Nicholas; Golland, Polina

    2016-01-01

    We introduce a generative probabilistic model for segmentation of brain lesions in multi-dimensional images that generalizes the EM segmenter, a common approach for modelling brain images using Gaussian mixtures and a probabilistic tissue atlas that employs expectation-maximization (EM) to estimate the label map for a new image. Our model augments the probabilistic atlas of the healthy tissues with a latent atlas of the lesion. We derive an estimation algorithm with closed-form EM update equations. The method extracts a latent atlas prior distribution and the lesion posterior distributions jointly from the image data. It delineates lesion areas individually in each channel, allowing for differences in lesion appearance across modalities, an important feature of many brain tumor imaging sequences. We also propose discriminative model extensions to map the output of the generative model to arbitrary labels with semantic and biological meaning, such as “tumor core” or “fluid-filled structure”, but without a one-to-one correspondence to the hypo-or hyper-intense lesion areas identified by the generative model. We test the approach in two image sets: the publicly available BRATS set of glioma patient scans, and multimodal brain images of patients with acute and subacute ischemic stroke. We find the generative model that has been designed for tumor lesions to generalize well to stroke images, and the generative-discriminative model to be one of the top ranking methods in the BRATS evaluation. PMID:26599702

  19. Automatic selection of localized region-based active contour models using image content analysis applied to brain tumor segmentation.

    Science.gov (United States)

    Ilunga-Mbuyamba, Elisee; Avina-Cervantes, Juan Gabriel; Cepeda-Negrete, Jonathan; Ibarra-Manzano, Mario Alberto; Chalopin, Claire

    2017-12-01

    Brain tumor segmentation is a routine process in a clinical setting and provides useful information for diagnosis and treatment planning. Manual segmentation, performed by physicians or radiologists, is a time-consuming task due to the large quantity of medical data generated presently. Hence, automatic segmentation methods are needed, and several approaches have been introduced in recent years including the Localized Region-based Active Contour Model (LRACM). There are many popular LRACM, but each of them presents strong and weak points. In this paper, the automatic selection of LRACM based on image content and its application on brain tumor segmentation is presented. Thereby, a framework to select one of three LRACM, i.e., Local Gaussian Distribution Fitting (LGDF), localized Chan-Vese (C-V) and Localized Active Contour Model with Background Intensity Compensation (LACM-BIC), is proposed. Twelve visual features are extracted to properly select the method that may process a given input image. The system is based on a supervised approach. Applied specifically to Magnetic Resonance Imaging (MRI) images, the experiments showed that the proposed system is able to correctly select the suitable LRACM to handle a specific image. Consequently, the selection framework achieves better accuracy performance than the three LRACM separately. Copyright © 2017 Elsevier Ltd. All rights reserved.

  20. Brain Tumor Symptoms

    Science.gov (United States)

    ... Brain Anatomy Brain Tumor Symptoms Headaches Seizures Memory Depression Mood Swings & Cognitive Changes Fatigue Other Symptoms Diagnosis Types of Tumors Risk Factors Brain Tumor Statistics Brain Tumor Dictionary Webinars Anytime Learning About Us ...

  1. Understanding Brain Tumors

    Science.gov (United States)

    ... to Know About Brain Tumors . What is a Brain Tumor? A brain tumor is an abnormal growth
 ... Tumors” from Frankly Speaking Frankly Speaking About Cancer: Brain Tumors Download the full book Questions to ask ...

  2. Neural - levelset shape detection segmentation of brain tumors in dynamic susceptibility contrast enhanced and diffusion weighted magnetic resonance images

    International Nuclear Information System (INIS)

    Vijayakumar, C.; Bhargava, Sunil; Gharpure, Damayanti Chandrashekhar

    2008-01-01

    A novel Neuro - level set shape detection algorithm is proposed and evaluated for segmentation and grading of brain tumours. The algorithm evaluates vascular and cellular information provided by dynamic contrast susceptibility magnetic resonance images and apparent diffusion coefficient maps. The proposed neural shape detection algorithm is based on the levels at algorithm (shape detection algorithm) and utilizes a neural block to provide the speed image for the level set methods. In this study, two different architectures of level set method have been implemented and their results are compared. The results show that the proposed Neuro-shape detection performs better in differentiating the tumor, edema, necrosis in reconstructed images of perfusion and diffusion weighted magnetic resonance images. (author)

  3. Pediatric Brain Tumor Foundation

    Science.gov (United States)

    ... navigate their brain tumor diagnosis. WATCH AND SHARE Brain tumors and their treatment can be deadly so ... Pediatric Central Nervous System Cancers Read more >> Pediatric Brain Tumor Foundation 302 Ridgefield Court, Asheville, NC 28806 ...

  4. Childhood Brain Tumors

    Science.gov (United States)

    Brain tumors are abnormal growths inside the skull. They are among the most common types of childhood ... still be serious. Malignant tumors are cancerous. Childhood brain and spinal cord tumors can cause headaches and ...

  5. Delineation and segmentation of cerebral tumors by mapping blood-brain barrier disruption with dynamic contrast-enhanced CT and tracer kinetics modeling-a feasibility study

    International Nuclear Information System (INIS)

    Bisdas, S.; Vogl, T.J.; Yang, X.; Koh, T.S.; Lim, C.C.T.

    2008-01-01

    Dynamic contrast-enhanced (DCE) imaging is a promising approach for in vivo assessment of tissue microcirculation. Twenty patients with clinical and routine computed tomography (CT) evidence of intracerebral neoplasm were examined with DCE-CT imaging. Using a distributed-parameter model for tracer kinetics modeling of DCE-CT data, voxel-level maps of cerebral blood flow (F), intravascular blood volume (v i ) and intravascular mean transit time (t 1 ) were generated. Permeability-surface area product (PS), extravascular extracellular blood volume (v e ) and extraction ratio (E) maps were also calculated to reveal pathologic locations of tracer extravasation, which are indicative of disruptions in the blood-brain barrier (BBB). All maps were visually assessed for quality of tumor delineation and measurement of tumor extent by two radiologists. Kappa (κ) coefficients and their 95% confidence intervals (CI) were calculated to determine the interobserver agreement for each DCE-CT map. There was a substantial agreement for the tumor delineation quality in the F, v e and t 1 maps. The agreement for the quality of the tumor delineation was excellent for the v i , PS and E maps. Concerning the measurement of tumor extent, excellent and nearly excellent agreement was achieved only for E and PS maps, respectively. According to these results, we performed a segmentation of the cerebral tumors on the base of the E maps. The interobserver agreement for the tumor extent quantification based on manual segmentation of tumor in the E maps vs. the computer-assisted segmentation was excellent (κ = 0.96, CI: 0.93-0.99). The interobserver agreement for the tumor extent quantification based on computer segmentation in the mean images and the E maps was substantial (κ = 0.52, CI: 0.42-0.59). This study illustrates the diagnostic usefulness of parametric maps associated with BBB disruption on a physiology-based approach and highlights the feasibility for automatic segmentation of

  6. Supervised machine learning-based classification scheme to segment the brainstem on MRI in multicenter brain tumor treatment context.

    Science.gov (United States)

    Dolz, Jose; Laprie, Anne; Ken, Soléakhéna; Leroy, Henri-Arthur; Reyns, Nicolas; Massoptier, Laurent; Vermandel, Maximilien

    2016-01-01

    To constrain the risk of severe toxicity in radiotherapy and radiosurgery, precise volume delineation of organs at risk is required. This task is still manually performed, which is time-consuming and prone to observer variability. To address these issues, and as alternative to atlas-based segmentation methods, machine learning techniques, such as support vector machines (SVM), have been recently presented to segment subcortical structures on magnetic resonance images (MRI). SVM is proposed to segment the brainstem on MRI in multicenter brain cancer context. A dataset composed by 14 adult brain MRI scans is used to evaluate its performance. In addition to spatial and probabilistic information, five different image intensity values (IIVs) configurations are evaluated as features to train the SVM classifier. Segmentation accuracy is evaluated by computing the Dice similarity coefficient (DSC), absolute volumes difference (AVD) and percentage volume difference between automatic and manual contours. Mean DSC for all proposed IIVs configurations ranged from 0.89 to 0.90. Mean AVD values were below 1.5 cm(3), where the value for best performing IIVs configuration was 0.85 cm(3), representing an absolute mean difference of 3.99% with respect to the manual segmented volumes. Results suggest consistent volume estimation and high spatial similarity with respect to expert delineations. The proposed approach outperformed presented methods to segment the brainstem, not only in volume similarity metrics, but also in segmentation time. Preliminary results showed that the approach might be promising for adoption in clinical use.

  7. Tumor Types: Understanding Brain Tumors

    Science.gov (United States)

    Search Menu Facebook Twitter YouTube Flickr Instagram LinkedIn Brain Tumor Information | News & Blog Our Mission Our History Mission Leadership & Staff Financials Careers News & Blog Contact Us Donate Now Our Impact Our Impact Recent News News & ...

  8. Brain Tumors - Multiple Languages

    Science.gov (United States)

    ... Supplements Videos & Tools You Are Here: Home → Multiple Languages → All Health Topics → Brain Tumors URL of this page: https://medlineplus.gov/ ... V W XYZ List of All Topics All Brain Tumors - Multiple Languages To use the sharing features on this page, ...

  9. Brain tissue segmentation using fuzzy clustering techniques.

    Science.gov (United States)

    Sucharitha, M; Geetha, K Parimala

    2015-01-01

    Medical image segmentation is an essential step for most consequent image analysis tasks. Medical images can be segmented manually, but the accuracy of image segmentation using the automated segmentation algorithms is more when compared with the manual calculations. In this paper, an automated segmentation and classification of tissues are proposed for MR brain images. To classify MR brain image into three segments such as Grey Matter (GM), White Matter (WM) and Cerebro-Spinal Fluid (CSF). Classification of brain into tissues helps to diagnose several diseases such as tumors, Alzheimer's disease, stroke, multiple sclerosis. An unsupervised clustering technique such as Fuzzy C-Means (FCM) algorithm has been widely used in segmenting the images. The spatial information is not fully utilized by the conventional clustering algorithm and hence it is not applicable for clustering a noisy image. We incorporate a method for image clustering called out as Reformulated Fuzzy Local information C-Means Clustering algorithm [RFLICM] which is a variant of traditional Clustering algorithm by considering both spatial and gray level information. In RFLICM, spatial distance is replaced by local coefficient of variation in a fuzzy manner. Experiments are conducted on brain images to validate the performance of the proposed technique in segmenting the medical images and the efficiency achieved in the presence of salt and pepper noise is 99.86%. Standard FCM, Fuzzy Local information C-means clustering algorithm [FLICM], Reformulated Fuzzy Local information C-means clustering algorithm [RFLICM] are compared to explore the accuracy of our proposed approach. Clustering results show that RFLICM segmentation method is appropriate for classifying tissues in brain MR image.

  10. Automated segmentation of ventricles from serial brain MRI for the quantification of volumetric changes associated with communicating hydrocephalus in patients with brain tumor

    Science.gov (United States)

    Pura, John A.; Hamilton, Allison M.; Vargish, Geoffrey A.; Butman, John A.; Linguraru, Marius George

    2011-03-01

    Accurate ventricle volume estimates could improve the understanding and diagnosis of postoperative communicating hydrocephalus. For this category of patients, associated changes in ventricle volume can be difficult to identify, particularly over short time intervals. We present an automated segmentation algorithm that evaluates ventricle size from serial brain MRI examination. The technique combines serial T1- weighted images to increase SNR and segments the means image to generate a ventricle template. After pre-processing, the segmentation is initiated by a fuzzy c-means clustering algorithm to find the seeds used in a combination of fast marching methods and geodesic active contours. Finally, the ventricle template is propagated onto the serial data via non-linear registration. Serial volume estimates were obtained in an automated robust and accurate manner from difficult data.

  11. Brain Tumors (For Parents)

    Science.gov (United States)

    ... different types of brain tumors. Some are cancerous (meaning they can spread to parts of the body ... of the face, trunk, arms, or legs slurred speech difficulty standing or walking poor coordination headache in ...

  12. Brain Tumors and Fatigue

    Science.gov (United States)

    ... can help calm the mind. Meditation, guided imagery, music therapy, and yoga are just a few worth investigating. Home Donor and Privacy Policies Find Resources Disclaimer Donate Subscribe Login American Brain Tumor Association 8550 W. Bryn Mawr Ave. Ste ...

  13. Epilepsy and brain tumors

    Science.gov (United States)

    ENGLOT, DARIO J.; CHANG, EDWARD F.; VECHT, CHARLES J.

    2016-01-01

    Seizures are common in patients with brain tumors, and epilepsy can significantly impact patient quality of life. Therefore, a thorough understanding of rates and predictors of seizures, and the likelihood of seizure freedom after resection, is critical in the treatment of brain tumors. Among all tumor types, seizures are most common with glioneuronal tumors (70–80%), particularly in patients with frontotemporal or insular lesions. Seizures are also common in individuals with glioma, with the highest rates of epilepsy (60–75%) observed in patients with low-grade gliomas located in superficial cortical or insular regions. Approximately 20–50% of patients with meningioma and 20–35% of those with brain metastases also suffer from seizures. After tumor resection, approximately 60–90% are rendered seizure-free, with most favorable seizure outcomes seen in individuals with glioneuronal tumors. Gross total resection, earlier surgical therapy, and a lack of generalized seizures are common predictors of a favorable seizure outcome. With regard to anticonvulsant medication selection, evidence-based guidelines for the treatment of focal epilepsy should be followed, and individual patient factors should also be considered, including patient age, sex, organ dysfunction, comorbidity, or cotherapy. As concomitant chemotherapy commonly forms an essential part of glioma treatment, enzyme-inducing anticonvulsants should be avoided when possible. Seizure freedom is the ultimate goal in the treatment of brain tumor patients with epilepsy, given the adverse effects of seizures on quality of life. PMID:26948360

  14. Aquaporins and Brain Tumors

    Directory of Open Access Journals (Sweden)

    Rosario Maugeri

    2016-06-01

    Full Text Available Brain primary tumors are among the most diverse and complex human cancers, and they are normally classified on the basis of the cell-type and/or the grade of malignancy (the most malignant being glioblastoma multiforme (GBM, grade IV. Glioma cells are able to migrate throughout the brain and to stimulate angiogenesis, by inducing brain capillary endothelial cell proliferation. This in turn causes loss of tight junctions and fragility of the blood–brain barrier, which becomes leaky. As a consequence, the most serious clinical complication of glioblastoma is the vasogenic brain edema. Both glioma cell migration and edema have been correlated with modification of the expression/localization of different isoforms of aquaporins (AQPs, a family of water channels, some of which are also involved in the transport of other small molecules, such as glycerol and urea. In this review, we discuss relationships among expression/localization of AQPs and brain tumors/edema, also focusing on the possible role of these molecules as both diagnostic biomarkers of cancer progression, and therapeutic targets. Finally, we will discuss the possibility that AQPs, together with other cancer promoting factors, can be exchanged among brain cells via extracellular vesicles (EVs.

  15. Image Denoising And Segmentation Approchto Detect Tumor From BRAINMRI Images

    Directory of Open Access Journals (Sweden)

    Shanta Rangaswamy

    2018-04-01

    Full Text Available The detection of the Brain Tumor is a challenging problem, due to the structure of the Tumor cells in the brain. This project presents a systematic method that enhances the detection of brain tumor cells and to analyze functional structures by training and classification of the samples in SVM and tumor cell segmentation of the sample using DWT algorithm. From the input MRI Images collected, first noise is removed from MRI images by applying wiener filtering technique. In image enhancement phase, all the color components of MRI Images will be converted into gray scale image and make the edges clear in the image to get better identification and improvised quality of the image. In the segmentation phase, DWT on MRI Image to segment the grey-scale image is performed. During the post-processing, classification of tumor is performed by using SVM classifier. Wiener Filter, DWT, SVM Segmentation strategies were used to find and group the tumor position in the MRI filtered picture respectively. An essential perception in this work is that multi arrange approach utilizes various leveled classification strategy which supports execution altogether. This technique diminishes the computational complexity quality in time and memory. This classification strategy works accurately on all images and have achieved the accuracy of 93%.

  16. Living with a Brain Tumor

    Science.gov (United States)

    ... Care Act Living with a Brain Tumor Understanding Emotions Talking About Your Brain Tumor Involving Family and Friends Returning To Work Physical Intimacy Health Insurance Options Financial & Medical Assistance ...

  17. [Markers of brain tumors].

    Science.gov (United States)

    Fumagalli, R; Pezzotta, S; Bernini, F; Racagni, G

    1984-05-19

    Biological markers of tumors are compounds or enzymatic activities measurable in body fluids. Their presence or concentration must be linked to tumoral growth. The markers of the central nervous system tumors are detected in CSF. Alpha-feto-protein, carcinoembryonic antigen, human chorionic gonadotropin, adenohypophyseal peptide hormones, enzymes, etc., have found some application in the early diagnosis of leptomeningeal metastasis. Other applications involve the early detection and recurrency of primary brain tumors, as well as the evaluation of efficacy of their therapy. The tests based on the CSF content of desmosterol and polyamines have been studied extensively. Their rationale is discussed and specificity, sensitivity, efficiency and predictive value are considered. Experimental results concerning a new possible biochemical marker, based on CSF concentration of cyclic adenosine monophosphate, are reported.

  18. FCM Clustering Algorithms for Segmentation of Brain MR Images

    Directory of Open Access Journals (Sweden)

    Yogita K. Dubey

    2016-01-01

    Full Text Available The study of brain disorders requires accurate tissue segmentation of magnetic resonance (MR brain images which is very important for detecting tumors, edema, and necrotic tissues. Segmentation of brain images, especially into three main tissue types: Cerebrospinal Fluid (CSF, Gray Matter (GM, and White Matter (WM, has important role in computer aided neurosurgery and diagnosis. Brain images mostly contain noise, intensity inhomogeneity, and weak boundaries. Therefore, accurate segmentation of brain images is still a challenging area of research. This paper presents a review of fuzzy c-means (FCM clustering algorithms for the segmentation of brain MR images. The review covers the detailed analysis of FCM based algorithms with intensity inhomogeneity correction and noise robustness. Different methods for the modification of standard fuzzy objective function with updating of membership and cluster centroid are also discussed.

  19. Epidemiological features of brain tumors

    Directory of Open Access Journals (Sweden)

    Živković Nenad

    2013-01-01

    Full Text Available Brain tumors account for 1.4% of all cancers and 2.4% of all cancer-related deaths. The incidence of brain tumors varies and it is higher in developed countries of Western Europe, North America, Australia and New Zealand. In Serbia, according to data from 2009, malignant brain tumors account for 2. 2 of all tumors, and from all cancer­related deaths, 3.2% is caused by malignant brain tumors. According to recent statistical reports, an overall incidence of brain tumors for benign and malignant tumors combined is 18.71 per 100,000 persons/year. The most common benign brain tumor in adults is meningioma, which is most present in women, and the most common malignant tumor is glioblastoma, which is most present in adult men. Due to high mortality, especially in patients diagnosed with glioblastoma and significant brain tumor morbidity, there is a constant interest in understanding its etiology in order to possibly prevent tumor occurrence in future and enable more efficient treatment strategies for this fatal brain disease. Despite the continuously growing number of epidemiological studies on possible factors of tumor incidence, the etiology remains unclear. The only established environmental risk factor of gliomas is ionizing radiation exposure. Exposure to radiofrequency electromagnetic fields via cell phone use has gained a lot of attention as a potential risk factor of brain tumor development. However, studies have been inconsistent and inconclusive, so more definite results are still expected.

  20. Brain's tumor image processing using shearlet transform

    Science.gov (United States)

    Cadena, Luis; Espinosa, Nikolai; Cadena, Franklin; Korneeva, Anna; Kruglyakov, Alexey; Legalov, Alexander; Romanenko, Alexey; Zotin, Alexander

    2017-09-01

    Brain tumor detection is well known research area for medical and computer scientists. In last decades there has been much research done on tumor detection, segmentation, and classification. Medical imaging plays a central role in the diagnosis of brain tumors and nowadays uses methods non-invasive, high-resolution techniques, especially magnetic resonance imaging and computed tomography scans. Edge detection is a fundamental tool in image processing, particularly in the areas of feature detection and feature extraction, which aim at identifying points in a digital image at which the image has discontinuities. Shearlets is the most successful frameworks for the efficient representation of multidimensional data, capturing edges and other anisotropic features which frequently dominate multidimensional phenomena. The paper proposes an improved brain tumor detection method by automatically detecting tumor location in MR images, its features are extracted by new shearlet transform.

  1. Notch Signaling and Brain Tumors

    DEFF Research Database (Denmark)

    Stockhausen, Marie; Kristoffersen, Karina; Poulsen, Hans Skovgaard

    2011-01-01

    Human brain tumors are a heterogenous group of neoplasms occurring inside the cranium and the central spinal cord. In adults and children, astrocytic glioma and medulloblastoma are the most common subtypes of primary brain tumors. These tumor types are thought to arise from cells in which Notch s...

  2. Human brain tumors

    International Nuclear Information System (INIS)

    Heindel, W.; Luyten, P.R.; Herholz, K.; Marien, J.H.; Kugel, H.; Bunke, J.; Heiss, W.D.; Hollander, J.A. den.

    1990-01-01

    It has been postulated that malignant tumors show increased anaerobic glycolysis. Areas of increased glycolysis could be identified by detection of increased glucose uptake and lactate production. The purpose of this paper is to investigate whether the most active parts in human brain tumors can be localized by correlating findings of proton (H-1) spectroscopic imaging and fluorodeoxyglucose (FDG) positron emission tomography (PET). Localized H-1 MR spectroscopy was performed with a clinical 1.5-T whole-body MR system. In 15 patients with CH-1 gliomas, the spatial distribution of choline-containing compounds, creatine, N-acetyl aspartate (NAA), and lactate was displayed as spectroscopic images. Those metabolite maps were correlated with conventional MR images and, in five cases, with corresponding PET sections

  3. Segmentation of liver tumors on CT images

    International Nuclear Information System (INIS)

    Pescia, D.

    2011-01-01

    This thesis is dedicated to 3D segmentation of liver tumors in CT images. This is a task of great clinical interest since it allows physicians benefiting from reproducible and reliable methods for segmenting such lesions. Accurate segmentation would indeed help them during the evaluation of the lesions, the choice of treatment and treatment planning. Such a complex segmentation task should cope with three main scientific challenges: (i) the highly variable shape of the structures being sought, (ii) their similarity of appearance compared with their surrounding medium and finally (iii) the low signal to noise ratio being observed in these images. This problem is addressed in a clinical context through a two step approach, consisting of the segmentation of the entire liver envelope, before segmenting the tumors which are present within the envelope. We begin by proposing an atlas-based approach for computing pathological liver envelopes. Initially images are pre-processed to compute the envelopes that wrap around binary masks in an attempt to obtain liver envelopes from estimated segmentation of healthy liver parenchyma. A new statistical atlas is then introduced and used to segmentation through its diffeomorphic registration to the new image. This segmentation is achieved through the combination of image matching costs as well as spatial and appearance prior using a multi-scale approach with MRF. The second step of our approach is dedicated to lesions segmentation contained within the envelopes using a combination of machine learning techniques and graph based methods. First, an appropriate feature space is considered that involves texture descriptors being determined through filtering using various scales and orientations. Then, state of the art machine learning techniques are used to determine the most relevant features, as well as the hyper plane that separates the feature space of tumoral voxels to the ones corresponding to healthy tissues. Segmentation is then

  4. Imaging of brain tumors

    International Nuclear Information System (INIS)

    Gaensler, E.H.L.

    1995-01-01

    The contents are diagnostic approaches, general features of tumors -hydrocephalus, edema, attenuation and/or intensity value, hemorrhage, fat, contrast enhancement, intra-axial supratentorial tumors - tumors of glial origin, oligodendrogliomas, ependymomas, subependymomas, subependymal giant cell astrocytomas, choroid plexus papilloma; midline tumors - colloid cysts, craniopharyngiomas; pineal region tumors and miscellaneous tumors i.e. primary intracerebral lymphoma, primitive neuroectodermal tumors, hemangioblastomas; extraaxial tumors - meningiomas; nerve sheath tumors -schwannomas, epidermoids, dermoids, lipomas, arachnoid cysts; metastatic tumors (8 refs.)

  5. Anisotropic Diffusion based Brain MRI Segmentation and 3D Reconstruction

    Directory of Open Access Journals (Sweden)

    M. Arfan Jaffar

    2012-06-01

    Full Text Available In medical field visualization of the organs is very imperative for accurate diagnosis and treatment of any disease. Brain tumor diagnosis and surgery also required impressive 3D visualization of the brain to the radiologist. Detection and 3D reconstruction of brain tumors from MRI is a computationally time consuming and error-prone task. Proposed system detects and presents a 3D visualization model of the brain and tumor inside which greatly helps the radiologist to effectively diagnose and analyze the brain tumor. We proposed a multi-phase segmentation and visualization technique which overcomes the many problems of 3D volume segmentation methods like lake of fine details. In this system segmentation is done in three different phases which reduces the error chances. The system finds contours for skull, brain and tumor. These contours are stacked over and two novel methods are used to find the 3D visualization models. The results of these techniques, particularly of interpolation based, are impressive. Proposed system is tested against publically available data set [41] and MRI datasets available from MRI aamp; CT center Rawalpindi, Pakistan [42].

  6. Brain Tumor Epidemiology Consortium (BTEC)

    Science.gov (United States)

    The Brain Tumor Epidemiology Consortium is an open scientific forum organized to foster the development of multi-center, international and inter-disciplinary collaborations that will lead to a better understanding of the etiology, outcomes, and prevention of brain tumors.

  7. Mechanism of brain tumor headache.

    Science.gov (United States)

    Taylor, Lynne P

    2014-04-01

    Headaches occur commonly in all patients, including those who have brain tumors. Using the search terms "headache and brain tumors," "intracranial neoplasms and headache," "facial pain and brain tumors," "brain neoplasms/pathology," and "headache/etiology," we reviewed the literature from the past 78 years on the proposed mechanisms of brain tumor headache, beginning with the work of Penfield. Most of what we know about the mechanisms of brain tumor associated headache come from neurosurgical observations from intra-operative dural and blood vessel stimulation as well as intra-operative observations and anecdotal information about resolution of headache symptoms with various tumor-directed therapies. There is an increasing overlap between the primary and secondary headaches and they may actually share a similar biological mechanism. While there can be some criticism that the experimental work with dural and arterial stimulation produced head pain and not actual headache, when considered with the clinical observations about headache type, coupled with improvement after treatment of the primary tumor, we believe that traction on these structures, coupled with increased intracranial pressure, is clearly part of the genesis of brain tumor headache and may also involve peripheral sensitization with neurogenic inflammation as well as a component of central sensitization through trigeminovascular afferents on the meninges and cranial vessels. © 2014 American Headache Society.

  8. Notch Signaling and Brain Tumors

    DEFF Research Database (Denmark)

    Stockhausen, Marie; Kristoffersen, Karina; Poulsen, Hans Skovgaard

    2011-01-01

    Human brain tumors are a heterogenous group of neoplasms occurring inside the cranium and the central spinal cord. In adults and children, astrocytic glioma and medulloblastoma are the most common subtypes of primary brain tumors. These tumor types are thought to arise from cells in which Notch...... signaling plays a fundamental role during development. Recent findings have shown that Notch signaling is dysregulated, and contributes to the malignant potential of these tumors. Growing evidence point towards an important role for cancer stem cells in the initiation and maintenance of glioma...

  9. Brain and Spinal Tumors

    Science.gov (United States)

    ... vessels. Also under investigation are ways to improve drug delivery to the tumor and to prevent the side- ... vessels. Also under investigation are ways to improve drug delivery to the tumor and to prevent the side- ...

  10. A generative model for segmentation of tumor and organs-at-risk for radiation therapy planning of glioblastoma patients

    DEFF Research Database (Denmark)

    Agn, Mikael; Law, Ian; Munck Af Rosenschöld, Per

    2016-01-01

    We present a fully automated generative method for simultaneous brain tumor and organs-at-risk segmentation in multi-modal magnetic resonance images. The method combines an existing whole-brain segmentation technique with a spatial tumor prior, which uses convolutional restricted Boltzmann machin...

  11. Brain tumor classification of microscopy images using deep residual learning

    Science.gov (United States)

    Ishikawa, Yota; Washiya, Kiyotada; Aoki, Kota; Nagahashi, Hiroshi

    2016-12-01

    The crisis rate of brain tumor is about one point four in ten thousands. In general, cytotechnologists take charge of cytologic diagnosis. However, the number of cytotechnologists who can diagnose brain tumors is not sufficient, because of the necessity of highly specialized skill. Computer-Aided Diagnosis by computational image analysis may dissolve the shortage of experts and support objective pathological examinations. Our purpose is to support a diagnosis from a microscopy image of brain cortex and to identify brain tumor by medical image processing. In this study, we analyze Astrocytes that is a type of glia cell of central nerve system. It is not easy for an expert to discriminate brain tumor correctly since the difference between astrocytes and low grade astrocytoma (tumors formed from Astrocyte) is very slight. In this study, we present a novel method to segment cell regions robustly using BING objectness estimation and to classify brain tumors using deep convolutional neural networks (CNNs) constructed by deep residual learning. BING is a fast object detection method and we use pretrained BING model to detect brain cells. After that, we apply a sequence of post-processing like Voronoi diagram, binarization, watershed transform to obtain fine segmentation. For classification using CNNs, a usual way of data argumentation is applied to brain cells database. Experimental results showed 98.5% accuracy of classification and 98.2% accuracy of segmentation.

  12. Notch Signaling and Brain Tumors

    DEFF Research Database (Denmark)

    Stockhausen, Marie; Kristoffersen, Karina; Poulsen, Hans Skovgaard

    2011-01-01

    Human brain tumors are a heterogenous group of neoplasms occurring inside the cranium and the central spinal cord. In adults and children, astrocytic glioma and medulloblastoma are the most common subtypes of primary brain tumors. These tumor types are thought to arise from cells in which Notch...... signaling plays a fundamental role during development. Recent findings have shown that Notch signaling is dysregulated, and contributes to the malignant potential of these tumors. Growing evidence point towards an important role for cancer stem cells in the initiation and maintenance of glioma...... and medulloblastoma. In this chapter we will cover the present findings of Notch signaling in human glioma and medulloblastoma and try to create an overall picture of its relevance in the pathogenesis of these tumors....

  13. Efficacy of texture, shape, and intensity feature fusion for posterior-fossa tumor segmentation in MRI.

    Science.gov (United States)

    Ahmed, Shaheen; Iftekharuddin, Khan M; Vossough, Arastoo

    2011-03-01

    Our previous works suggest that fractal texture feature is useful to detect pediatric brain tumor in multimodal MRI. In this study, we systematically investigate efficacy of using several different image features such as intensity, fractal texture, and level-set shape in segmentation of posterior-fossa (PF) tumor for pediatric patients. We explore effectiveness of using four different feature selection and three different segmentation techniques, respectively, to discriminate tumor regions from normal tissue in multimodal brain MRI. We further study the selective fusion of these features for improved PF tumor segmentation. Our result suggests that Kullback-Leibler divergence measure for feature ranking and selection and the expectation maximization algorithm for feature fusion and tumor segmentation offer the best results for the patient data in this study. We show that for T1 and fluid attenuation inversion recovery (FLAIR) MRI modalities, the best PF tumor segmentation is obtained using the texture feature such as multifractional Brownian motion (mBm) while that for T2 MRI is obtained by fusing level-set shape with intensity features. In multimodality fused MRI (T1, T2, and FLAIR), mBm feature offers the best PF tumor segmentation performance. We use different similarity metrics to evaluate quality and robustness of these selected features for PF tumor segmentation in MRI for ten pediatric patients.

  14. An Ensemble of 2D Convolutional Neural Networks for Tumor Segmentation

    DEFF Research Database (Denmark)

    Lyksborg, Mark; Puonti, Oula; Agn, Mikael

    2015-01-01

    Accurate tumor segmentation plays an important role in radiosurgery planning and the assessment of radiotherapy treatment efficacy. In this paper we propose a method combining an ensemble of 2D convolutional neural networks for doing a volumetric segmentation of magnetic resonance images. The seg......Accurate tumor segmentation plays an important role in radiosurgery planning and the assessment of radiotherapy treatment efficacy. In this paper we propose a method combining an ensemble of 2D convolutional neural networks for doing a volumetric segmentation of magnetic resonance images....... The segmentation is done in three steps; first the full tumor region, is segmented from the background by a voxel-wise merging of the decisions of three networks learned from three orthogonal planes, next the segmentation is refined using a cellular automaton-based seed growing method known as growcut. Finally......, within-tumor sub-regions are segmented using an additional ensemble of networks trained for the task. We demonstrate the method on the MICCAI Brain Tumor Segmentation Challenge dataset of 2014, and show improved segmentation accuracy compared to an axially trained 2D network and an ensemble segmentation...

  15. Biological Markers in Pediatric Brain Tumors

    NARCIS (Netherlands)

    J.M. de Bont (Judith Maria)

    2008-01-01

    textabstractThe most common solid tumors in children are brain tumors1. Yearly, approximately 2-2.5 per 100,000 children of <15 years of age are diagnosed with a brain tumor1. Despite improved survival rates, brain tumors in children are still the second leading cause of death due to cancer in

  16. Segmentation and Visualisation of Human Brain Structures

    Energy Technology Data Exchange (ETDEWEB)

    Hult, Roger

    2003-10-01

    In this thesis the focus is mainly on the development of segmentation techniques for human brain structures and of the visualisation of such structures. The images of the brain are both anatomical images (magnet resonance imaging (MRI) and autoradiography) and functional images that show blood flow (functional magnetic imaging (fMRI), positron emission tomography (PET), and single photon emission tomography (SPECT)). When working with anatomical images, the structures segmented are visible as different parts of the brain, e.g. the brain cortex, the hippocampus, or the amygdala. In functional images, the activity or the blood flow that be seen. Grey-level morphology methods are used in the segmentations to make tissue types in the images more homogenous and minimise difficulties with connections to outside tissue. A method for automatic histogram thresholding is also used. Furthermore, there are binary operations such as logic operation between masks and binary morphology operations. The visualisation of the segmented structures uses either surface rendering or volume rendering. For the visualisation of thin structures, surface rendering is the better choice since otherwise some voxels might be missed. It is possible to display activation from a functional image on the surface of a segmented cortex. A new method for autoradiographic images has been developed, which integrates registration, background compensation, and automatic thresholding to get faster and more reliable results than the standard techniques give.

  17. Pediatric brain tumors; Kindliche Hirntumoren

    Energy Technology Data Exchange (ETDEWEB)

    Reith, W.; Bodea, S. [Universitaetsklinikum des Saarlandes, Klinik fuer Diagnostische und Interventionelle Neuroradiologie, Homburg/Saar (Germany); Muehl-Benninghaus, R.

    2017-09-15

    Brain tumors differ between children and adults both in histology and localization. Malignant gliomas and meningiomas predominate in adults while medulloblastomas and low-grade astrocytomas are the most frequent brain tumors in children. More than one half (50-70%) of pediatric brain tumors have an infratentorial location but only approximately 30% in adults. Brain tumors can be recognized in sonography, cranial computed tomography (CCT) and magnetic resonance imaging (MRI) by their space-consuming character and by their divergent density and intensity in comparison to normal brain parenchyma. They can grow extrusively, even infiltrate the parenchyma or originate from it. Besides clinical symptoms and diagnostics this article describes the most common pediatric brain tumors, i.e. astrocytoma, medulloblastoma, brainstem glioma, craniopharyngioma, neurofibromatosis and ganglioglioma. The most important imaging criteria are outlined. (orig.) [German] Sowohl Histologie als auch Lokalisation von Hirntumoren unterscheiden sich bei Kindern und Erwachsenen. Waehrend maligne Gliome und Meningeome bei Erwachsenen vorherrschen, kommen bei Kindern ueberwiegend Medulloblastome und niedriggradige Astrozytome vor. Mehr als die Haelfte (50-70 %) aller kindlichen Hirntumoren sind infratentoriell lokalisiert, dagegen sind es bei Erwachsenen nur etwa 30 %. Im Ultraschall, in der kranialen CT (CCT) oder MRT koennen Hirntumoren durch ihren raumfordernden Charakter und ihrer zum normalen Parenchym abweichenden Dichte oder Signalintensitaet erkannt werden. Sie koennen verdraengend wachsen, z. T. auch das Parenchym infiltrieren oder von diesem ausgehen. Neben der klinischen Symptomatik und Diagnostik werden im vorliegenden Artikel die haeufigsten kindlichen Hirntumoren, das Astrozytom, Medulloblastom, Hirnstammgliom, Kraniopharyngeom, die Neurofibromatose und das Gangliogliom beschrieben. Die wichtigsten bildgebende Kriterien werden dargestellt. (orig.)

  18. Computational Modeling of Medical Images of Brain Tumor Patients for Optimized Radiation Therapy Planning

    DEFF Research Database (Denmark)

    Agn, Mikael

    In brain tumor radiation therapy, the aim is to maximize the delivered radiation dose to the targeted tumor and at the same time minimize the dose to sensitive healthy structures – so-called organs-at-risk (OARs). When planning a radiation therapy session, the tumor and the OARs therefore need......, a need for automated methods that can segment both brain tumors and OARs. However, there is a noticeable lack in the literature of methods that simultaneously segment both types of structures. To automatically segment medical images of brain tumor patients is difficult because brain tumors vary greatly...... in size, shape, appearance and location within the brain. Furthermore, healthy structures surrounding a tumor are pushed and deformed by the so-called mass effect of the tumor. Moreover, medical imaging techniques often result in imaging artifacts and varying intensity across imaging centers. The goal...

  19. Brain tumors and syndromes in children

    NARCIS (Netherlands)

    Bleeker, Fonnet E.; Hopman, Saskia M. J.; Merks, Johannes H. M.; Aalfs, Cora M.; Hennekam, Raoul C. M.

    2014-01-01

    (Brain) tumors are usually a disorder of aged individuals. If a brain tumor occurs in a child, there is a possible genetic susceptibility for this. Such genetic susceptibilities often show other signs and symptoms. Therefore, every child with a brain tumor should be carefully evaluated for the

  20. Multidimensional Brain MRI segmentation using graph cuts

    International Nuclear Information System (INIS)

    Lecoeur, Jeremy

    2010-01-01

    This thesis deals with the segmentation of multimodal brain MRIs by graph cuts method. First, we propose a method that utilizes three MRI modalities by merging them. The border information given by the spectral gradient is then challenged by a region information, given by the seeds selected by the user, using a graph cut algorithm. Then, we propose three enhancements of this method. The first consists in finding an optimal spectral space because the spectral gradient is based on natural images and then inadequate for multimodal medical images. This results in a learning based segmentation method. We then explore the automation of the graph cut method. Here, the various pieces of information usually given by the user are inferred from a robust expectation-maximization algorithm. We show the performance of these two enhanced versions on multiple sclerosis lesions. Finally, we integrate atlases for the automatic segmentation of deep brain structures. These three new techniques show the adaptability of our method to various problems. Our different segmentation methods are better than most of nowadays techniques, speaking of computation time or segmentation accuracy. (authors)

  1. Pathological classification of brain tumors.

    Science.gov (United States)

    Pollo, B

    2012-04-01

    The tumors of the central nervous system are classified according to the last international classification published by World Health Organization. The Classification of Tumors of the Central Nervous System was done on 2007, based on morphological features, growth pattern and molecular profile of neoplastic cells, defining malignancy grade. The neuropathological diagnosis and the grading of each histotype are based on identification of histopathological criteria and immunohistochemical data. The histopathology, also consisting of findings with prognostic or predictive relevance, plays a critical role in the diagnosis and treatment of brain tumors. The recent progresses on radiological, pathological, immunohistochemical, molecular and genetic diagnosis improved the characterization of brain tumors. Molecular and genetic profiles may identify different tumor subtypes varying in biological and clinical behavior. To investigate new therapeutic approaches is important to study the molecular pathways that lead the processes of proliferation, invasion, angiogenesis, anaplastic transformation. Different molecular biomarkers were identified by genetic studies and some of these are used in neuro-oncology for the evaluation of glioma patients, in particular combined deletions of the chromosome arms 1p and 19q in oligodendroglial tumors, methylation status of the O-6 methylguanine- DNA methyltransferase gene promoter and alterations in the epidermal growth factor receptor pathway in adult malignant gliomas, isocitrate dehydrogenase 1 (IDH1) and IDH2 gene mutations in diffuse gliomas, as well as BRAF status in pilocytic astrocytomas. The prognostic evaluation and the therapeutic strategies for patients depend on synthesis of clinical, pathological and biological data: histological diagnosis, malignancy grade, gene-molecular profile, radiological pictures, surgical resection and clinical findings (age, tumor location, "performance status").

  2. Therapy of malignant brain tumors

    International Nuclear Information System (INIS)

    Jellinger, K.

    1987-01-01

    The tumors of the brain claim for a separate position in scientific medicine regarding biology, morphology, features of clinical manifestation, diagnostics and therapy. During the past years due to rapid progress in medical biotechnics the situation of the neuroclinician in front of brain tumors has been dramatically changed. The prerequisites for early and accurate diagnosis as well as for successful treatment also of malignant neoplasms have increased and remarkably improved. At the same time the information necessary for an appropriate pragmatic use of the available cognitive methods and therapeutic means increased along the same scale. These facts necessitate the preparation of publications in which the state of the art is presented in possible completeness, systematic order and proper dis-posability for rational management and therapeutic strategies. The primary aim of the present book is to serve these purposes. With 8 chapters, two of them are indexed for INIS, the collective of competent authors deal on the biology, pathology and immunology of malignant brain tumors of adults and of children including relevant basic and recent data of experimental research; further on the available methods of therapy: neurosurgery, radiology and chemotherapy, the fundamental principals of their efficacy and the differing models of single respective combined application, in comprehensive critical form. 111 figs

  3. Radiosurgery for metastatic brain tumors

    International Nuclear Information System (INIS)

    Serizawa, Toru

    2009-01-01

    Stereotactic radiosurgery (SRS) precisely delivers high-dose radiation to a small target (usually less than 3-4 cm in diameter), in a single session with steep dose-fall, employing various radiation methods. SRS provides good tumor control for small brain metastases from various primary cancers, with minimal untoward effects on surrounding normal brain. This excellent tumor control prevents neurological death and maintains good activity of daily life. Although surgery with whole-brain radiation therapy (WBRT) remains an important option for patients with a solitary brain metastasis, SRS with or without WBRT should be considered in patients with a limited number of small tumors and a good prognosis. Many reports, as well as both retrospective and prospective reviews, have shown WBRT before or after SRS to improve local control and reduce new distant lesion emergence. However, upfront WBRT does not improve survival. There are two major delivery techniques, Gamma Knife (GK; Elekta AB, Stockholm, Sweden) SRS and linear accelerator (LINIAC)-based SRS. They are based on quite different concepts, and have different techniques and clinical applications. These differences complicate the discussion of the limitations of and indications for SRS and the necessity for prophylactic WBRT. This review discusses numerous aspects of SRS, its value as compared with other treatment modalities, the necessity for prophylactic WBRT with SRS, the limitations of and indications for SRS, and the difference between GK and LINIAC SRS, based on the literature and our experience, and proposes a new strategy for the treatment of brain metastases in view of the available clinical data and experience. (author)

  4. Efficacy of texture, shape, and intensity features for robust posterior-fossa tumor segmentation in MRI

    Science.gov (United States)

    Ahmed, S.; Iftekharuddin, K. M.; Ogg, R. J.; Laningham, F. H.

    2009-02-01

    Our previous works suggest that fractal-based texture features are very useful for detection, segmentation and classification of posterior-fossa (PF) pediatric brain tumor in multimodality MRI. In this work, we investigate and compare efficacy of our texture features such as fractal and multifractional Brownian motion (mBm), and intensity along with another useful level-set based shape feature in PF tumor segmentation. We study feature selection and ranking using Kullback -Leibler Divergence (KLD) and subsequent tumor segmentation; all in an integrated Expectation Maximization (EM) framework. We study the efficacy of all four features in both multimodality as well as disparate MRI modalities such as T1, T2 and FLAIR. Both KLD feature plots and information theoretic entropy measure suggest that mBm feature offers the maximum separation between tumor and non-tumor tissues in T1 and FLAIR MRI modalities. The same metrics show that intensity feature offers the maximum separation between tumor and non-tumor tissue in T2 MRI modality. The efficacies of these features are further validated in segmenting PF tumor using both single modality and multimodality MRI for six pediatric patients with over 520 real MR images.

  5. Anisotropic Diffusion based Brain MRI Segmentation and 3D Reconstruction

    OpenAIRE

    M. Arfan Jaffar; Sultan Zia; Ghaznafar Latif; AnwarM. Mirza; Irfan Mehmood; Naveed Ejaz; Sung Wook Baik

    2012-01-01

    In medical field visualization of the organs is very imperative for accurate diagnosis and treatment of any disease. Brain tumor diagnosis and surgery also required impressive 3D visualization of the brain to the radiologist. Detection and 3D reconstruction of brain tumors from MRI is a computationally time consuming and error-prone task. Proposed system detects and presents a 3D visualization model of the brain and tumor inside which greatly helps the radiologist to effectively diagnose and ...

  6. Endothelial cell marker PAL-E reactivity in brain tumor, developing brain, and brain disease

    NARCIS (Netherlands)

    Leenstra, S.; Troost, D.; Das, P. K.; Claessen, N.; Becker, A. E.; Bosch, D. A.

    1993-01-01

    The endothelial cell marker PAL-E is not reactive to vessels in the normal brain. The present study concerns the PAL-E reactivity in brain tumors in contrast to normal brain and nonneoplastic brain disease. A total of 122 specimens were examined: brain tumors (n = 94), nonneoplastic brain disease (n

  7. Bleomycin treatment of brain tumors: an evaluation

    DEFF Research Database (Denmark)

    Linnert, Mette; Gehl, Julie

    2009-01-01

    Bleomycin has been used in the treatment of brain tumors for over 30 years. Currently, we are evaluating electrochemotherapy (the use of electric pulses to enhance uptake of bleomycin) for patients with secondary brain tumors. We, therefore, reviewed the literature with specific reference...... to the tolerability and toxicity of bleomycin. Using the keywords 'brain' and 'bleomycin', a database search without date restriction was performed and over 500 articles were found. Twenty-five articles were used for this study based on relevance determined by: (i) clinical studies, (ii) use of bleomycin, and (iii......) direct injection into brain tissue or cysts. There were two main indications for the use of bleomycin directly into the brain: (i) cystic tumors in the form of craniopharyngiomas and (ii) solid brain tumors such as glioblastomas and astrocytomas. The most frequent adverse effects reported were transient...

  8. Targeted Brain Tumor Treatment: Current Perspectives

    Directory of Open Access Journals (Sweden)

    Ningaraj N.S

    2007-01-01

    Full Text Available Brain tumor is associated with poor prognosis. The treatment option is severely limited for a patient with brain tumor, despite great advances in understanding the etiology and molecular biology of brain tumors that have lead to breakthroughs in developing pharmaceutical strategies, and ongoing NCI/Pharma-sponsored clinical trials. We reviewed the literature on molecular targeted agents in preclinical and clinical studies in brain tumor for the past decade, and observed that the molecular targeting in brain tumors is complex. This is because no single gene or protein can be affected by single molecular agent, requiring the use of combination molecular therapy with cytotoxic agents. In this review, we briefly discuss the potential molecular targets, and the challenges of targeted brain tumor treatment. For example, glial tumors are associated with over-expression of calcium-dependent potassium (KCa channels, and high grade glioma express specific KCa channel gene (gBK splice variants, and mutant epidermal growth factor receptors (EGFRvIII. These specific genes are promising targets for molecular targeted treatment in brain tumors. In addition, drugs like Avastin and Gleevec target the molecular targets such as vascular endothelial cell growth factor receptor, platelet-derived growth factor receptors, and BRC-ABL/Akt. Recent discovery of non-coding RNA, specifically microRNAs could be used as potential targeted drugs. Finally, we discuss the role of anti-cancer drug delivery to brain tumors by breaching the blood-brain tumor barrier. This non-invasive strategy is particularly useful as novel molecules and humanized monoclonal antibodies that target receptor tyrosine kinase receptors are rapidly being developed.

  9. Targeted Brain Tumor Treatment-Current Perspectives

    Directory of Open Access Journals (Sweden)

    N.S. Ningaraj

    2007-01-01

    Full Text Available Brain tumor is associated with poor prognosis. The treatment option is severely limited for a patient with brain tumor, despite great advances in understanding the etiology and molecular biology of brain tumors that have lead to breakthroughs in developing pharmaceutical strategies, and ongoing NCI/Pharma-sponsored clinical trials. We reviewed the literature on molecular targeted agents in preclinical and clinical studies in brain tumor for the past decade, and observed that the molecular targeting in brain tumors is complex. This is because no single gene or protein can be affected by single molecular agent, requiring the use of combination molecular therapy with cytotoxic agents. In this review, we briefly discuss the potential molecular targets, and the challenges of targeted brain tumor treatment. For example, glial tumors are associated with over-expression of calcium-dependent potassium (K Ca channels, and high grade glioma express specific K Ca channel gene (gBK splice variants, and mutant epidermal growth factor receptors (EGFRvIII. These specific genes are promising targets for molecular targeted treatment in brain tumors. In addition, drugs like Avastin and Gleevec target the molecular targets such as vascular endothelial cell growth factor receptor, platelet-derived growth factor receptors, and BRC-ABL/Akt. Recent discovery of non-coding RNA, specifically microRNAs could be used as potential targeted drugs. Finally, we discuss the role of anti-cancer drug delivery to brain tumors by breaching the blood-brain tumor barrier. This non-invasive strategy is particularly useful as novel molecules and humanized monoclonal antibodies that target receptor tyrosine kinase receptors are rapidly being developed.

  10. Image guided surgery versus conventional brain tumor and craniotomy localization.

    Science.gov (United States)

    Mahvash, Mehran; Boettcher, Ioannis; Petridis, Athanasios K; Besharati Tabrizi, Leila

    2017-02-01

    Accurate brain lesion and craniotomy localization is an essential step in neurosurgical procedures. Image guided techniques transfer the information of neuroimaging about brain lesion localization to the patient. A critical view is necessary to find out how safe and reliable it is to transfer this information to the patient's head without using image guided systems. The aim of this study was to investigate the value of image guided brain lesion and craniotomy localization compared to conventional methods. A new developed test was performed with 10 neurosurgeons from different clinics. The first task was to perform the conventional tumor localization, planning of craniotomy and skin incision using the MRI dataset of a patient with a left temporal brain tumor. Second, the neurosurgeons were asked to plan the craniotomy and skin incision using MRI based 3D visualization with the exact localization of the segmented brain tumor. Both plans of each neurosurgeon were compared and analyzed according to the calculated brain tumor localization, location, shape and size of craniotomy. All neurosurgeons changed the craniotomy localization and skin incision in the second part of the task using the image guided tumor visualization. The mean error (±standard deviation) of tumor localization of the conventional planning was 11.45±5.09 mm in the anterior-posterior (AP) and 12±7.91 mm in the superior-inferior (SI) direction. The mean error of the craniotomy localization using conventional planning was 10.18±6.09 mm in the AP and 10.75±8.18 mm in the SI direction. The craniotomy size was significantly larger using conventional planning of the craniotomy (P=0.035). Conventional brain tumor and craniotomy localization leads more frequently to errors and oversized craniotomy. Image guided surgery can reduce these errors and increase the safety and orientation for preoperative planning.

  11. Heuristically improved Bayesian segmentation of brain MR images ...

    African Journals Online (AJOL)

    Heuristically improved Bayesian segmentation of brain MR images. ... or even the most prevalent task in medical image processing is image segmentation. Among them, brain MR images suffer ... show that our algorithm performs well in comparison with the one implemented in SPM. It can be concluded that incorporating ...

  12. heuristically improved bayesian segmentation of brain mr images

    African Journals Online (AJOL)

    Aging Using ANN Based MR Brain Image Segmentation. Proceedings of the International Conference on Frontiers of. Intelligent Computing: Theory and Applications (FICTA) 2013,. Springer. Wang, J., J. Kong, et al. (2008). "A modified FCM algorithm for MRI brain image segmentation using both local and non-local spatial.

  13. Brain tumor stem cell dancing.

    Science.gov (United States)

    Bozzuto, Giuseppina; Toccacieli, Laura; Mazzoleni, Stefania; Frustagli, Gianluca; Chistolini, Pietro; Galli, Rossella; Molinari, Agnese

    2014-01-01

    Issues regarding cancer stem cell (CSC) movement are important in neurosphere biology as cell-cell or cell-environment interactions may have significant impacts on CSC differentiation and contribute to the heterogeneity of the neurosphere. Despite the growing body of literature data on the biology of brain tumor stem cells, floating CSC-derived neurospheres have been scarcely characterized from a morphological and ultrastructural point of view. Here we report a morphological and ultrastructural characterization performed by live imaging and scanning electron microscopy. Glioblastoma multiforme (GBM) CSC-derived neurospheres are heterogeneous and are constituted by cells, morphologically different, capable of forming highly dynamic structures. These dynamic structures are regulated by not serendipitous cell-cell interactions, and they synchronously pulsate following a cyclic course made of "fast" and "slow" alternate phases. Autocrine/paracrine non canonical Wnt signalling appears to be correlated with the association status of neurospheres. The results obtained suggest that GBM CSCs can behave both as independents cells and as "social" cells, highly interactive with other members of its species, giving rise to a sort of "multicellular organism".

  14. MRI Segmentation of the Human Brain: Challenges, Methods, and Applications

    Science.gov (United States)

    Despotović, Ivana

    2015-01-01

    Image segmentation is one of the most important tasks in medical image analysis and is often the first and the most critical step in many clinical applications. In brain MRI analysis, image segmentation is commonly used for measuring and visualizing the brain's anatomical structures, for analyzing brain changes, for delineating pathological regions, and for surgical planning and image-guided interventions. In the last few decades, various segmentation techniques of different accuracy and degree of complexity have been developed and reported in the literature. In this paper we review the most popular methods commonly used for brain MRI segmentation. We highlight differences between them and discuss their capabilities, advantages, and limitations. To address the complexity and challenges of the brain MRI segmentation problem, we first introduce the basic concepts of image segmentation. Then, we explain different MRI preprocessing steps including image registration, bias field correction, and removal of nonbrain tissue. Finally, after reviewing different brain MRI segmentation methods, we discuss the validation problem in brain MRI segmentation. PMID:25945121

  15. Asymptomatic brain tumor detected at brain check-up

    Energy Technology Data Exchange (ETDEWEB)

    Onizuka, Masanari; Suyama, Kazuhiko; Shibayama, Akira; Hiura, Tsuyoshi; Horie, Nobutaka; Miyazaki, Hisaya [Sankoukai Miyazaki Hospital, Isahaya, Nagasaki (Japan)

    2001-09-01

    Brain check-up was performed in 4000 healthy subjects who underwent medical and radiological examinations for possible brain diseases in our hospital from April 1996 to March 2000. Magnetic resonance imaging revealed 11 brain tumors which consisted of six meningiomas, three pituitary adenomas, one astrocytoma, and one epidermoid cyst. The detection rate of incidental brain tumor in our hospital was 0.3%. Nine patients underwent surgery, with one case of morbidity due to postoperative transient oculomotor nerve paresis. The widespread use of brain check-up may increasingly detect asymptomatic brain tumors. Surgical indications for such lesions remain unclear, and the strategy for treatment should be determined with consideration of the patient's wishes. (author)

  16. Asymptomatic brain tumor detected at brain check-up

    International Nuclear Information System (INIS)

    Onizuka, Masanari; Suyama, Kazuhiko; Shibayama, Akira; Hiura, Tsuyoshi; Horie, Nobutaka; Miyazaki, Hisaya

    2001-01-01

    Brain check-up was performed in 4000 healthy subjects who underwent medical and radiological examinations for possible brain diseases in our hospital from April 1996 to March 2000. Magnetic resonance imaging revealed 11 brain tumors which consisted of six meningiomas, three pituitary adenomas, one astrocytoma, and one epidermoid cyst. The detection rate of incidental brain tumor in our hospital was 0.3%. Nine patients underwent surgery, with one case of morbidity due to postoperative transient oculomotor nerve paresis. The widespread use of brain check-up may increasingly detect asymptomatic brain tumors. Surgical indications for such lesions remain unclear, and the strategy for treatment should be determined with consideration of the patient's wishes. (author)

  17. Segmentation of brain parenchyma using bilateral filtering and region growing.

    Science.gov (United States)

    Hwang, Jinyoung; Han, Yeji; Park, HyunWook

    2007-01-01

    When the non-diffusion weighted images (non-DWIs) and the diffusion weighted images (DWIs) are acquired by a fast imaging sequence, they suffer from several artifacts such as N/2 ghost, subject motion, eddy current, etc. These artifacts act as a noise in the background area of the human brain. To extract the brain region from the noisy background, brain parenchyma segmentation has been used. Several segmentation methods presented so far cannot address this problem well. In this study, we propose a novel segmentation method of brain contour in non-DWIs using bilateral filtering, which can reduce the background noise while edge-preserving, and region growing. We compare the segmentation results from various methods, and the proposed method shows better segmentation results than those from other schemes.

  18. Brain MR image segmentation using NAMS in pseudo-color.

    Science.gov (United States)

    Li, Hua; Chen, Chuanbo; Fang, Shaohong; Zhao, Shengrong

    2017-12-01

    Image segmentation plays a crucial role in various biomedical applications. In general, the segmentation of brain Magnetic Resonance (MR) images is mainly used to represent the image with several homogeneous regions instead of pixels for surgical analyzing and planning. This paper proposes a new approach for segmenting MR brain images by using pseudo-color based segmentation with Non-symmetry and Anti-packing Model with Squares (NAMS). First of all, the NAMS model is presented. The model can represent the image with sub-patterns to keep the image content and largely reduce the data redundancy. Second, the key idea is proposed that convert the original gray-scale brain MR image into a pseudo-colored image and then segment the pseudo-colored image with NAMS model. The pseudo-colored image can enhance the color contrast in different tissues in brain MR images, which can improve the precision of segmentation as well as directly visual perceptional distinction. Experimental results indicate that compared with other brain MR image segmentation methods, the proposed NAMS based pseudo-color segmentation method performs more excellent in not only segmenting precisely but also saving storage.

  19. MRI Segmentation of the Human Brain: Challenges, Methods, and Applications

    Directory of Open Access Journals (Sweden)

    Ivana Despotović

    2015-01-01

    Full Text Available Image segmentation is one of the most important tasks in medical image analysis and is often the first and the most critical step in many clinical applications. In brain MRI analysis, image segmentation is commonly used for measuring and visualizing the brain’s anatomical structures, for analyzing brain changes, for delineating pathological regions, and for surgical planning and image-guided interventions. In the last few decades, various segmentation techniques of different accuracy and degree of complexity have been developed and reported in the literature. In this paper we review the most popular methods commonly used for brain MRI segmentation. We highlight differences between them and discuss their capabilities, advantages, and limitations. To address the complexity and challenges of the brain MRI segmentation problem, we first introduce the basic concepts of image segmentation. Then, we explain different MRI preprocessing steps including image registration, bias field correction, and removal of nonbrain tissue. Finally, after reviewing different brain MRI segmentation methods, we discuss the validation problem in brain MRI segmentation.

  20. Fluorescent Nanoparticle Uptake for Brain Tumor Visualization

    Directory of Open Access Journals (Sweden)

    Rachel Tréhin

    2006-04-01

    Full Text Available Accurate delineation of tumor margins is vital to the successful surgical resection of brain tumors. We have previously developed a multimodal nanoparticle CLIO-Cy5.5, which is detectable by both magnetic resonance imaging and fluorescence, to assist in intraoperatively visualizing tumor boundaries. Here we examined the accuracy of tumor margin determination of orthotopic tumors implanted in hosts with differing immune responses to the tumor. Using a nonuser-based signal intensity method applied to fluorescent micrographs of 9L gliosarcoma green fluorescent protein (GFP tumors, mean overestimations of 2 and 24 µm were obtained using Cy5.5 fluorescence, compared to the true tumor margin determined by GFP fluorescence, in nude mice and rats, respectively. To resolve which cells internalized the nanoparticle and to quantitate degree of uptake, tumors were disaggregated and cells were analyzed by flow cytometry and fluorescence microscopy. Nanoparticle uptake was seen in both CD11b+ cells (representing activated microglia and macrophages and tumor cells in both animal models by both methods. CD11b+ cells were predominantly found at the tumor margin in both hosts, but were more pronounced at the margin in the rat model. Additional metastatic (CT26 colon and primary (Gli36 glioma brain tumor models likewise demonstrated that the nanoparticle was internalized both by tumor cells and by host cells. Together, these observations suggest that fluorescent nanoparticles provide an accurate method of tumor margin estimation based on a combination of tumor cell and host cell uptake for primary and metastatic tumors in animal model systems and offer potential for clinical translation.

  1. Radionuclidr diagnosis of brain tumors, brain inflammatory and traumatic lesions

    International Nuclear Information System (INIS)

    Badmaev, K.N.; Mel'kishev, V.F.; Dement'ev, E.V.; Svetlova, N.L.

    1982-01-01

    A complex of problems of radionuclide diagnosis of central nervous system diseases including tumors, traumas, vascular lessons, inflammatory processes is considered. The principles, technique and results of radionuclide xintigraphy of a tumor, depending on its localization are given. Radioindication of brain tumours in the operation is given

  2. Multiparametric MR assessment of pediatric brain tumors

    International Nuclear Information System (INIS)

    Tzika, A.A.; Astrakas, L.G.; Zarifi, M.K.; Petridou, N.; Young-Poussaint, T.; Goumnerova, L.; Black, P.McL.; Zurakowski, D.; Anthony, D.C.

    2003-01-01

    MR assessment of pediatric brain tumors has expanded to include physiologic information related to cellular metabolites, hemodynamic and diffusion parameters. The purpose of this study was to investigate the relationship between MR and proton MR spectroscopic imaging in children with primary brain tumors. Twenty-one patients (mean age 9 years) with histologically verified brain tumors underwent conventional MR imaging, hemodynamic MR imaging (HMRI) and proton MR spectroscopic imaging (MRSI). Fourteen patients also had diffusion-weighted MR imaging (DWMRI). Metabolic indices including choline-containing compounds (Cho), total creatine (tCr) and lipids/lactate (L) were derived by proton MRSI, relative cerebral blood volume (rCBV) by HMRI, and apparent tissue water diffusion coefficients (ADC) by DWMRI. Variables were examined by linear regression and correlation as well as by ANOVA. Cho (suggestive of tumor cellularity and proliferative activity) correlated positively with rCBV, while the relationship between Cho and ADC (suggestive of cellular density) was inverse (P<0.001). The relationship between rCBV and ADC was also inverse (P=0.004). Cho and lipids (suggestive of necrosis and/or apoptosis) were not significantly correlated (P=0.51). A positive relationship was found between lipids and ADC (P=0.002). The relationships between Cho, rCBV, ADC and lipids signify that tumor physiology is influenced by the tumor's physical and chemical environment. Normalized Cho and lipids distinguished high-grade from low-grade tumors (P<0.05). Multiparametric MR imaging using MRSI, HMRI and DWMRI enhances assessment of brain tumors in children and improves our understanding of tumor physiology while promising to distinguish higher- from lower-malignancy tumors, a distinction that is particularly clinically important among inoperable tumors. (orig.)

  3. Multiparametric MR assessment of pediatric brain tumors

    Energy Technology Data Exchange (ETDEWEB)

    Tzika, A.A. [Department of Radiology, Children' s Hospital, Harvard Medical School, Boston, MA 02114 (United States); NMR Surgical Laboratory, Massachusetts General Hospital and Shriners Burns Institute, Harvard Medical School, 51 Blossom Street, Boston, MA 02114 (United States); Astrakas, L.G.; Zarifi, M.K.; Petridou, N.; Young-Poussaint, T. [Department of Radiology, Children' s Hospital, Harvard Medical School, Boston, MA 02114 (United States); Goumnerova, L.; Black, P.McL. [Department of Neurosurgery, Children' s Hospital, Harvard Medical School, Boston, MA 02114 (United States); Zurakowski, D. [Department of Biostatistics, Children' s Hospital, Harvard Medical School, Boston, MA 02114 (United States); Anthony, D.C. [Department of Pathology, Children' s Hospital, Harvard Medical School, Boston, MA 02114 (United States)

    2003-01-01

    MR assessment of pediatric brain tumors has expanded to include physiologic information related to cellular metabolites, hemodynamic and diffusion parameters. The purpose of this study was to investigate the relationship between MR and proton MR spectroscopic imaging in children with primary brain tumors. Twenty-one patients (mean age 9 years) with histologically verified brain tumors underwent conventional MR imaging, hemodynamic MR imaging (HMRI) and proton MR spectroscopic imaging (MRSI). Fourteen patients also had diffusion-weighted MR imaging (DWMRI). Metabolic indices including choline-containing compounds (Cho), total creatine (tCr) and lipids/lactate (L) were derived by proton MRSI, relative cerebral blood volume (rCBV) by HMRI, and apparent tissue water diffusion coefficients (ADC) by DWMRI. Variables were examined by linear regression and correlation as well as by ANOVA. Cho (suggestive of tumor cellularity and proliferative activity) correlated positively with rCBV, while the relationship between Cho and ADC (suggestive of cellular density) was inverse (P<0.001). The relationship between rCBV and ADC was also inverse (P=0.004). Cho and lipids (suggestive of necrosis and/or apoptosis) were not significantly correlated (P=0.51). A positive relationship was found between lipids and ADC (P=0.002). The relationships between Cho, rCBV, ADC and lipids signify that tumor physiology is influenced by the tumor's physical and chemical environment. Normalized Cho and lipids distinguished high-grade from low-grade tumors (P<0.05). Multiparametric MR imaging using MRSI, HMRI and DWMRI enhances assessment of brain tumors in children and improves our understanding of tumor physiology while promising to distinguish higher- from lower-malignancy tumors, a distinction that is particularly clinically important among inoperable tumors. (orig.)

  4. Psychosocial profile of pediatric brain tumor survivors with neurocognitive complaints

    NARCIS (Netherlands)

    de Ruiter, Marieke Anna; Schouten-van Meeteren, Antoinette Yvonne Narda; van Vuurden, Dannis Gilbert; Maurice-Stam, Heleen; Gidding, Corrie; Beek, Laura Rachel; Granzen, Bernd; Oosterlaan, Jaap; Grootenhuis, Martha Alexandra

    2016-01-01

    With more children surviving a brain tumor, neurocognitive consequences of the tumor and its treatment become apparent, which could affect psychosocial functioning. The present study therefore aimed to assess psychosocial functioning of pediatric brain tumor survivors (PBTS) in detail. Psychosocial

  5. Proton MRS imaging in pediatric brain tumors

    Energy Technology Data Exchange (ETDEWEB)

    Zarifi, Maria [Aghia Sophia Children' s Hospital, Department of Radiology, Athens (Greece); Tzika, A.A. [Harvard Medical School, Department of Surgery, Massachusetts General Hospital, Boston, MA (United States); Shriners Burn Hospital, Boston, MA (United States)

    2016-06-15

    Magnetic resonance (MR) techniques offer a noninvasive, non-irradiating yet sensitive approach to diagnosing and monitoring pediatric brain tumors. Proton MR spectroscopy (MRS), as an adjunct to MRI, is being more widely applied to monitor the metabolic aspects of brain cancer. In vivo MRS biomarkers represent a promising advance and may influence treatment choice at both initial diagnosis and follow-up, given the inherent difficulties of sequential biopsies to monitor therapeutic response. When combined with anatomical or other types of imaging, MRS provides unique information regarding biochemistry in inoperable brain tumors and can complement neuropathological data, guide biopsies and enhance insight into therapeutic options. The combination of noninvasively acquired prognostic information and the high-resolution anatomical imaging provided by conventional MRI is expected to surpass molecular analysis and DNA microarray gene profiling, both of which, although promising, depend on invasive biopsy. This review focuses on recent data in the field of MRS in children with brain tumors. (orig.)

  6. [Brain tumor immunotherapy: Illusion or hope?

    Science.gov (United States)

    Migliorini, Denis; Dutoit, Valérie; Walker, Paul R; Dietrich, Pierre-Yves

    2017-05-01

    Immunotherapy has proven efficient for many tumors and is now part of standard of care in many indications. What is the picture for brain tumors? The recent development of anti-CTLA-4 and PD1 immune checkpoint inhibitors, which have the ability to restore T lymphocytes activity, has gathered enthusiasm and is now paving the way towards more complex models of immune system manipulation. These models include, among others, vaccination and adoptive T cell transfer technologies. Complementary to those strategies, molecules capable of reshaping the immune tumor microenvironment are currently being investigated in early phase trials. Indeed, the tumor bed is hostile to anti-tumor immune responses due to many escape mechanisms, and this is particularly true in the context of brain tumors, a master in eliciting immunosuppressive cells and molecules. The goal of this review is to describe the hopes and challenges of brain tumors immunotherapy and to propose an inventory of the current clinical research with specific focus on the therapies targeting the tumor microenvironment. Copyright © 2017 Société Française du Cancer. Published by Elsevier Masson SAS. All rights reserved.

  7. Histopathological studies on the irradiated brain tumors

    International Nuclear Information System (INIS)

    Narita, Tadao

    1980-01-01

    Of 43 cases of irradiated brain tumor, histological findings showed extensive necrosis or disappearance of the neoplasm, considered to be attributable to radiation treatment, in 30 (70%). Extensive necrosis of the tumor in areas exposed to radiation was found in 16 treated cases (37.2%). The histopathology of massive necrosis was that of simple coagulative necrosis, sometimes with marked vascular alterations and extravasation of fibrinoid material into the necrotic tissue. Necrosis was almost always incomplete, and foci of residual tumors were found at the periphery of the tumors. The terminal picture in cases of massive necrosis was often that of widespread intra- and extracranial metastasis. Almost complete disappearance of the tumor was observed in some cases with subsequent diffuse degenerative changes in the brain parenchyma exposed to radiation. In 5 cases of irradiated tumors, autopsy findings suggested that the growth of the primary tumor might have been restricted. And in 5 cases tumor cytology revealed the marked presence of a large number of multinucleated, bizarre giant cells with evidence of degeneration in both the cytoplasm and the nucleus. Multifocal necrosis of the brain, with axonal swelling and sponginess of the tissue, was observed in two patients following combined radiation and antineoplastic chemotherapy. Diffuse loss and degeneration of nerve cells of the cerebral cortex in pseudo-laminar fashion was observed in 7 patients with or without bilateral necrosis of the globus pallidus. Histological findings revealed typical anoxic encephalopathy. (J.P.N.)

  8. PET imaging for brain tumor diagnostics.

    Science.gov (United States)

    Suchorska, Bogdana; Tonn, Joerg C; Jansen, Nathalie L

    2014-12-01

    Brain tumors differ in histology, biology, prognosis and treatment options. Although structural magnetic resonance is still the gold standard for morphological tumor characterization, molecular imaging has gained an increasing importance in assessment of tumor activity and malignancy. Amino acid PET is frequently used for surgery and biopsy planning as well as therapy monitoring in suspected primary brain tumors as well as metastatic lesions, whereas 18F-fluorodeoxyglucose (18F-FDG) remains the tracer of choice for evaluation of patients with primary central nervous system lymphoma. Application of somatostatin receptor ligands has improved tumor delineation in skull base meningioma and concurrently opened up new treatment possibilities in recurrent or surgically not assessable tumors.Recent development focuses on the implementation of hybrid PET/MRI as well as on the development of new tracers targeting tumor hypoxia, enzymes involved in neoplastic metabolic pathways and the combination of PET tracers with therapeutic agents. Implementation of molecular imaging in the clinical routine continues to improve management in patients with brain tumors. However, more prospective large sample studies are needed to validate the additional informative value of PET.

  9. Mandatory chromosomal segment balance in aneuploid tumor cells

    Directory of Open Access Journals (Sweden)

    Li Lung Maria

    2007-01-01

    Full Text Available Abstract Background Euploid chromosome balance is vitally important for normal development, but is profoundly changed in many tumors. Is each tumor dependent on its own structurally and numerically changed chromosome complement that has evolved during its development and progression? We have previously shown that normal chromosome 3 transfer into the KH39 renal cell carcinoma line and into the Hone1 nasopharyngeal carcinoma line inhibited their tumorigenicity. The aim of the present study was to distinguish between a qualitative and a quantitative model of this suppression. According to the former, a damaged or deleted tumor suppressor gene would be restored by the transfer of a normal chromosome. If so, suppression would be released only when the corresponding sequences of the exogenous normal chromosome are lost or inactivated. According to the alternative quantitative model, the tumor cell would not tolerate an increased dosage of the relevant gene or segment. If so, either a normal cell derived, or, a tumor derived endogenous segment could be lost. Methods Fluorescence in Situ Hybridization based methods, as well as analysis of polymorphic microsatellite markers were used to follow chromosome 3 constitution changes in monochromosomal hybrids. Results In both tumor lines with introduced supernumerary chromosomes 3, the copy number of 3p21 or the entire 3p tended to fall back to the original level during both in vitro and in vivo growth. An exogenous, normal cell derived, or an endogenous, tumor derived, chromosome segment was lost with similar probability. Identification of the lost versus retained segments showed that the intolerance for increased copy number was particularly strong for 3p14-p21, and weaker for other 3p regions. Gains in copy number were, on the other hand, well tolerated in the long arm and particularly the 3q26-q27 region. Conclusion The inability of the cell to tolerate an experimentally imposed gain in 3p14-p21 in

  10. Probabilistic brain tissue segmentation in neonatal magnetic resonance imaging

    NARCIS (Netherlands)

    Anbeek, Petronella; Vincken, Koen L.; Groenendaal, Floris; Koeman, Annemieke; Van Osch, Matthias J. P.; Van der Grond, Jeroen

    A fully automated method has been developed for segmentation of four different structures in the neonatal brain: white matter (WM), central gray matter (CEGM), cortical gray matter (COGM), and cerebrospinal fluid (CSF). The segmentation algorithm is based on information from T2-weighted (T2-w) and

  11. Tumor segmentation of whole-body magnetic resonance imaging in neurofibromatosis type 1 patients: tumor burden correlates

    Energy Technology Data Exchange (ETDEWEB)

    Heffler, Michael A.; Xi, Yin; Chhabra, Avneesh [University of Texas Southwestern Medical Center, Department of Radiology, Dallas, TX (United States); Le, Lu Q. [University of Texas Southwestern Medical Center, Department of Dermatology, Dallas, TX (United States)

    2017-01-15

    Segmentation of whole-body MRI (WBMRI) to assess the feasibility, quantitate the total tumor volume (tumor burden) in patients with neurofibromatosis type 1 (NF1) and examine associations with demographic, disease-related and anthropomorphic features. A consecutive series of patients with NF1 underwent WBMRI and were reviewed for tumors. Tumors were segmented using a semiautomated software-based tool. Tumors were classified as superficial or deep and discrete or plexiform. Segmentation times were recorded. Segmentation yielded the quantity and tumor burden of superficial, internal and plexiform tumors. Correlations between segmentation data and demographic, disease-related and anthropomorphic features were examined. Fifteen patients were evaluated (42.3 ± 13.6 years, 10 female, 5 male). Segmentation times were a median of 30 min and yielded 2,328 tumors (1,582 superficial, 746 internal and 23 plexiform). One tumor was malignant. Tumor counts ranged from 14 to 397. Tumor burden ranged from 6.95 cm3 to 571 cm3. Individual tumor volume ranged from 0.0120 cm3 to 298 cm3. Significant correlation was found between the total volume of superficial tumors and height (ρ = 0.5966, p < 0.02). Male patients had higher overall tumor burdens (p < 0.05) and higher superficial tumor burden (p < 0.03). Patients with negative family history had more tumors (p < 0.05). Segmentation of WBMRI in patients with NF1 is feasible and elucidates meaningful relationships among disease phenotype, anthropomorphic and demographic features. (orig.)

  12. Tumor segmentation of whole-body magnetic resonance imaging in neurofibromatosis type 1 patients: tumor burden correlates

    International Nuclear Information System (INIS)

    Heffler, Michael A.; Xi, Yin; Chhabra, Avneesh; Le, Lu Q.

    2017-01-01

    Segmentation of whole-body MRI (WBMRI) to assess the feasibility, quantitate the total tumor volume (tumor burden) in patients with neurofibromatosis type 1 (NF1) and examine associations with demographic, disease-related and anthropomorphic features. A consecutive series of patients with NF1 underwent WBMRI and were reviewed for tumors. Tumors were segmented using a semiautomated software-based tool. Tumors were classified as superficial or deep and discrete or plexiform. Segmentation times were recorded. Segmentation yielded the quantity and tumor burden of superficial, internal and plexiform tumors. Correlations between segmentation data and demographic, disease-related and anthropomorphic features were examined. Fifteen patients were evaluated (42.3 ± 13.6 years, 10 female, 5 male). Segmentation times were a median of 30 min and yielded 2,328 tumors (1,582 superficial, 746 internal and 23 plexiform). One tumor was malignant. Tumor counts ranged from 14 to 397. Tumor burden ranged from 6.95 cm3 to 571 cm3. Individual tumor volume ranged from 0.0120 cm3 to 298 cm3. Significant correlation was found between the total volume of superficial tumors and height (ρ = 0.5966, p < 0.02). Male patients had higher overall tumor burdens (p < 0.05) and higher superficial tumor burden (p < 0.03). Patients with negative family history had more tumors (p < 0.05). Segmentation of WBMRI in patients with NF1 is feasible and elucidates meaningful relationships among disease phenotype, anthropomorphic and demographic features. (orig.)

  13. Unusual radiological characteristics of teratoid/rhabdoid brain tumor ...

    African Journals Online (AJOL)

    We report a case of atypical teratoid rhabdoid brain tumor for 4 months old male child, who presented with unusual radiological findings, that can be confused with other brain tumors ,so we high light these unusual imaging features to aid in making correct diagnosis. Keywords: atypical teratoid–rhabdoid tumor, brain tumor, ...

  14. Unsupervised Neural Techniques Applied to MR Brain Image Segmentation

    Directory of Open Access Journals (Sweden)

    A. Ortiz

    2012-01-01

    Full Text Available The primary goal of brain image segmentation is to partition a given brain image into different regions representing anatomical structures. Magnetic resonance image (MRI segmentation is especially interesting, since accurate segmentation in white matter, grey matter and cerebrospinal fluid provides a way to identify many brain disorders such as dementia, schizophrenia or Alzheimer’s disease (AD. Then, image segmentation results in a very interesting tool for neuroanatomical analyses. In this paper we show three alternatives to MR brain image segmentation algorithms, with the Self-Organizing Map (SOM as the core of the algorithms. The procedures devised do not use any a priori knowledge about voxel class assignment, and results in fully-unsupervised methods for MRI segmentation, making it possible to automatically discover different tissue classes. Our algorithm has been tested using the images from the Internet Brain Image Repository (IBSR outperforming existing methods, providing values for the average overlap metric of 0.7 for the white and grey matter and 0.45 for the cerebrospinal fluid. Furthermore, it also provides good results for high-resolution MR images provided by the Nuclear Medicine Service of the “Virgen de las Nieves” Hospital (Granada, Spain.

  15. A fast stochastic framework for automatic MR brain images segmentation.

    Directory of Open Access Journals (Sweden)

    Marwa Ismail

    Full Text Available This paper introduces a new framework for the segmentation of different brain structures (white matter, gray matter, and cerebrospinal fluid from 3D MR brain images at different life stages. The proposed segmentation framework is based on a shape prior built using a subset of co-aligned training images that is adapted during the segmentation process based on first- and second-order visual appearance characteristics of MR images. These characteristics are described using voxel-wise image intensities and their spatial interaction features. To more accurately model the empirical grey level distribution of the brain signals, we use a linear combination of discrete Gaussians (LCDG model having positive and negative components. To accurately account for the large inhomogeneity in infant MRIs, a higher-order Markov-Gibbs Random Field (MGRF spatial interaction model that integrates third- and fourth- order families with a traditional second-order model is proposed. The proposed approach was tested and evaluated on 102 3D MR brain scans using three metrics: the Dice coefficient, the 95-percentile modified Hausdorff distance, and the absolute brain volume difference. Experimental results show better segmentation of MR brain images compared to current open source segmentation tools.

  16. A Novel Approach for MRI Brain Images Segmentation

    OpenAIRE

    Abo-Eleneen Z. A; Gamil Abdel-Azim

    2013-01-01

    Segmentation of brain from magnetic resonance (MR) images has important applications in neuroimaging, in particular it facilitates in extracting different brain tissues such as cerebrospinal fluids, white matter and gray matter. That helps in determining the volume of the tissues in three-dimensional brain MR images, which yields in analyzing many neural disorders such as epilepsy and Alzheimer disease. The Fisher information is a measure of the fluctuations in the observations. In a sense, ...

  17. Neuroimaging Outcome Correlation of Brain Tumors

    OpenAIRE

    J Gordon Millichap

    1998-01-01

    Serial analysis of imaging (thallium-201 [201TI) single-photon emission CT and MRI) examinations was correlated with clinical and histological characteristics of brain tumors in 75 patients monitored for 1 day to 3.9 years (mean, 1.39 years) at the Children’s Hospital, Harvard Medical School, Boston.

  18. Robust generative asymmetric GMM for brain MR image segmentation.

    Science.gov (United States)

    Ji, Zexuan; Xia, Yong; Zheng, Yuhui

    2017-11-01

    Accurate segmentation of brain tissues from magnetic resonance (MR) images based on the unsupervised statistical models such as Gaussian mixture model (GMM) has been widely studied during last decades. However, most GMM based segmentation methods suffer from limited accuracy due to the influences of noise and intensity inhomogeneity in brain MR images. To further improve the accuracy for brain MR image segmentation, this paper presents a Robust Generative Asymmetric GMM (RGAGMM) for simultaneous brain MR image segmentation and intensity inhomogeneity correction. First, we develop an asymmetric distribution to fit the data shapes, and thus construct a spatial constrained asymmetric model. Then, we incorporate two pseudo-likelihood quantities and bias field estimation into the model's log-likelihood, aiming to exploit the neighboring priors of within-cluster and between-cluster and to alleviate the impact of intensity inhomogeneity, respectively. Finally, an expectation maximization algorithm is derived to iteratively maximize the approximation of the data log-likelihood function to overcome the intensity inhomogeneity in the image and segment the brain MR images simultaneously. To demonstrate the performances of the proposed algorithm, we first applied the proposed algorithm to a synthetic brain MR image to show the intermediate illustrations and the estimated distribution of the proposed algorithm. The next group of experiments is carried out in clinical 3T-weighted brain MR images which contain quite serious intensity inhomogeneity and noise. Then we quantitatively compare our algorithm to state-of-the-art segmentation approaches by using Dice coefficient (DC) on benchmark images obtained from IBSR and BrainWeb with different level of noise and intensity inhomogeneity. The comparison results on various brain MR images demonstrate the superior performances of the proposed algorithm in dealing with the noise and intensity inhomogeneity. In this paper, the RGAGMM

  19. Brain tumors in childhood; Hirntumoren im Kindesalter

    Energy Technology Data Exchange (ETDEWEB)

    Sinzig, M.; Gasser, J.; Hausegger, K.A. [Landeskrankenhaus Klagenfurt, Kinderradiologie RZI, Klagenfurt (Austria); Jauk, B. [Landeskrankenhaus Klagenfurt, Abt. fuer Kinder- und Jugendheilkunde, Klagenfurt (Austria)

    2008-10-15

    Central nervous system (CNS) tumors are the most common solid neoplasms in childhood and the second most common malignancies after leukemia in the pediatric age group. Supratentorial tumors are more common in children younger than 2 years old and in adolescents, whereas in patients between 2 and 12 years of age brain tumors originating in the posterior fossa dominate. This implies a relationship between the type of tumor, its location and the age of the patient, which has to be considered in differential diagnoses. Medulloblastoma represents the most common malignant brain tumor in childhood. In the posterior fossa medulloblastomas are approximately as frequent as astrocytomas. Supratentorial astrocytomas are by far the main tumor type. In this report some typical CNS neoplasms in children are discussed and their neuroradiological features are demonstrated. (orig.) [German] Hirntumoren sind die haeufigsten soliden Tumoren des Kindesalters und repraesentieren nach den Leukaemien die zweithaeufigsten malignen Erkrankungen bei Kindern. Waehrend bei Kleinkindern und Adoleszenten supratentorielle Hirntumoren ueberwiegen, ist bei Patienten zwischen 2 und 12 Jahren haeufiger die hintere Schaedelgrube Ursprungsort dieser Malignome. Daraus geht hervor, dass gewisse Tumortypen eine gewisse Alterspraedilektion aufweisen, was neben der radiologischen Morphologie der Raumforderung fuer differenzialdiagnostische Ueberlegungen ueberaus hilfreich sein kann. Das Medulloblastom ist das haeufigste ZNS-Malignom des Kindesalters und repraesentiert zusammen mit zerebellaeren Astrozytomen auch den haeufigsten Tumortyp der hinteren Schaedelgrube. Supratentoriell stehen die Astrozytome ganz im Vordergrund. In dieser Arbeit werden einige typische kindliche infra- und supratentorielle Hirntumoren diskutiert und ihre neuroradiologischen Merkmale dargestellt. (orig.)

  20. A survey of MRI-based medical image analysis for brain tumor studies

    International Nuclear Information System (INIS)

    Bauer, Stefan; Nolte, Lutz-P; Reyes, Mauricio; Wiest, Roland

    2013-01-01

    MRI-based medical image analysis for brain tumor studies is gaining attention in recent times due to an increased need for efficient and objective evaluation of large amounts of data. While the pioneering approaches applying automated methods for the analysis of brain tumor images date back almost two decades, the current methods are becoming more mature and coming closer to routine clinical application. This review aims to provide a comprehensive overview by giving a brief introduction to brain tumors and imaging of brain tumors first. Then, we review the state of the art in segmentation, registration and modeling related to tumor-bearing brain images with a focus on gliomas. The objective in the segmentation is outlining the tumor including its sub-compartments and surrounding tissues, while the main challenge in registration and modeling is the handling of morphological changes caused by the tumor. The qualities of different approaches are discussed with a focus on methods that can be applied on standard clinical imaging protocols. Finally, a critical assessment of the current state is performed and future developments and trends are addressed, giving special attention to recent developments in radiological tumor assessment guidelines. (topical review)

  1. A survey of MRI-based medical image analysis for brain tumor studies

    Science.gov (United States)

    Bauer, Stefan; Wiest, Roland; Nolte, Lutz-P.; Reyes, Mauricio

    2013-07-01

    MRI-based medical image analysis for brain tumor studies is gaining attention in recent times due to an increased need for efficient and objective evaluation of large amounts of data. While the pioneering approaches applying automated methods for the analysis of brain tumor images date back almost two decades, the current methods are becoming more mature and coming closer to routine clinical application. This review aims to provide a comprehensive overview by giving a brief introduction to brain tumors and imaging of brain tumors first. Then, we review the state of the art in segmentation, registration and modeling related to tumor-bearing brain images with a focus on gliomas. The objective in the segmentation is outlining the tumor including its sub-compartments and surrounding tissues, while the main challenge in registration and modeling is the handling of morphological changes caused by the tumor. The qualities of different approaches are discussed with a focus on methods that can be applied on standard clinical imaging protocols. Finally, a critical assessment of the current state is performed and future developments and trends are addressed, giving special attention to recent developments in radiological tumor assessment guidelines.

  2. Delayed radiation necrosis of the brain simulating a brain tumor

    International Nuclear Information System (INIS)

    Ikeda, Hiroya; Kanai, Nobuhiro; Kamikawa, Kiyoo

    1976-01-01

    Two cases of delayed radiation necrosis of the brain are reported. Case 1 was a 50-year-old man who had right hemiparesis and disorientation 26 months after Linac irradiation (5,000 rad), preceded by an operation for right maxillar carcinoma. A left carotid angiogram demonstrated a left temporal mass lesion, extending to the frontal lobe. Case 2 was a 41-year-old man who had previously had an operation for right intraorbital plasmocytoma, followed by two Co irradiations (6,400 rad, and 5,000 rad). He had the signs and symptoms of intracranial hypertension 36 months after his last irradiation. A left carotid angiogram demonstrated a left temporal mass lesion. Both cases were treated by administration of steroid hormone (which alleviated the signs and symptoms) and by temporal lobectomy. Microscopic examinations showed necrosis of the brain tissues associated with hyaline degeneration of blood vessel walls and perivascular cell infiltration. The signs and symptoms of intracranial hypertension subsided postoperatively. Thirteen other cases the same as ours were collected from literature. They showed the signs and symptoms simulating a brain tumor (like a metastatic brain tumor) after irradiation to extracranial malignant tumors. Diagnosis of radiation necrosis was made by operation or autopsy. A follow-up for a long time is necessary, because the pathological changes in the brain may be progressive and extending in some cases, although decompressive operations for mass lesions give excellent results. (auth.)

  3. Tumor sterilization dose and radiation induced change of the brain tissue in radiotherapy of brain tumors

    International Nuclear Information System (INIS)

    Yoshii, Yoshihiko; Maki, Yutaka; Takano, Shingo

    1987-01-01

    Ninety-seven patients with brain tumors (38 gliomas, 26 brain metastases, 18 sellar tumors, 15 others) were treated by cobalt gamma ray or proton radiotherapy. In this study, normal brain injury due to radiation was analysed in terms of time-dose-fractionation (TDF), nominal standard dose (NSD) by the Ellis formula and NeuNSD by a modification in which the N exponent was -0.44 and the T exponent was -0.06. Their calculated doses were analysed in relationship to the normal brain radiation induced change (RIC) and the tumor sterilization dose. All brain tumors with an exception of many patients with brain metastases were received a surgical extirpation subtotally or partially prior to radiotherapy. And all patients with glioma and brain metastasis received also immuno-chemotherapy in the usual manner during radiotherapy. The calculated dose expressed by NeuNSD and TDF showed a significant relationship between a therapeutic dose and a postradiation time in terms of the appearance of RIC. It was suggested that RIC was caused by a dose over 800 in NeuNSD and a dose over 70 in TDF. Furthermore, it was suggested that an aged patient and a patient who had the vulnerable brain tissue to radiation exposure in the irradiated field had the high risk of RIC. On the other hand, our results suggested that the tumor sterilization dose should be over 1,536 NeuNSD and the irradiated method should be further considered in addition to the radiobiological concepts for various brain tumors. (author)

  4. Neonatal Brain Tissue Classification with Morphological Adaptation and Unified Segmentation

    Directory of Open Access Journals (Sweden)

    Richard eBeare

    2016-03-01

    Full Text Available Measuring the distribution of brain tissue types (tissue classification in neonates is necessary for studying typical and atypical brain development, such as that associated with preterm birth, and may provide biomarkers for neurodevelopmental outcomes. Compared with magnetic resonance images of adults, neonatal images present specific challenges that require the development of specialized, population-specific methods. This paper introduces MANTiS (Morphologically Adaptive Neonatal Tissue Segmentation, which extends the unified segmentation approach to tissue classification implemented in Statistical Parametric Mapping (SPM software to neonates. MANTiS utilizes a combination of unified segmentation, template adaptation via morphological segmentation tools and topological filtering, to segment the neonatal brain into eight tissue classes: cortical gray matter, white matter, deep nuclear gray matter, cerebellum, brainstem, cerebrospinal fluid (CSF, hippocampus and amygdala. We evaluated the performance of MANTiS using two independent datasets. The first dataset, provided by the NeoBrainS12 challenge, consisted of coronal T2-weighted images of preterm infants (born ≤30 weeks’ gestation acquired at 30 weeks’ corrected gestational age (n= 5, coronal T2-weighted images of preterm infants acquired at 40 weeks’ corrected gestational age (n= 5 and axial T2-weighted images of preterm infants acquired at 40 weeks’ corrected gestational age (n= 5. The second dataset, provided by the Washington University NeuroDevelopmental Research (WUNDeR group, consisted of T2-weighted images of preterm infants (born <30 weeks’ gestation acquired shortly after birth (n= 12, preterm infants acquired at term-equivalent age (n= 12, and healthy term-born infants (born ≥38 weeks’ gestation acquired within the first nine days of life (n= 12. For the NeoBrainS12 dataset, mean Dice scores comparing MANTiS with manual segmentations were all above 0.7, except for

  5. AUTOMATIC LIVER TUMOR SEGMENTATION ON COMPUTED TOMOGRAPHY FOR PATIENT TREATMENT PLANNING AND MONITORING

    OpenAIRE

    Moghbel, Mehrdad; Syamsiah, Mashohor; Rozi, Mahmud; Saripan, M. Iqbal Bin

    2016-01-01

    Segmentation of liver tumors from Computed Tomography (CT) and tumor burden analysis play an important role in the choice of therapeutic strategies for liver diseases and treatment monitoring. In this paper, a new segmentation method for liver tumors from contrast-enhanced CT imaging is proposed.

  6. Interstitial photonic radiosurgery for brain tumors

    Energy Technology Data Exchange (ETDEWEB)

    Kubo, Osami; Muragaki, Yoshihiro; Iseki, Hiroshi; Hori, Tomokatsu; Takakura, Kintomo [Tokyo Women' s Medical Coll. (Japan). Neurological Inst.

    1999-12-01

    The photon radiosurgery system (PRS) is a developed of the Photo-electron Corp. of Walham, Mass. The heart of this system is a thin needle, 3 mm in diameter and 100 mm long, from whose tip low-energy X-ray photon are isotropically emitted. This apparatus is a compact radiosurgery system that irradiates soft X ray from the tip of its small probe (weight of the machine=1.9 Kg). The PRS can be used either with a stereotactic frame or during a craniotomy as interstitial radiotherapy. The PRS is able to irradiate 15 Gy at the portion of 1.5 cm from the center for about 20 minutes and avoid severe damage to surrounding normal brain because of steep dose distribution curve. Because this system emits low-energy photons, almost the x-rays are attenuated in the patient. For a treatment of this system, dose rates outside the patient are close to background radiation levels. No special shielding of the patient or health care personnel is required. Basic examination of this system was done. C 6 cell line of Glioma was irradiated by PRS in vitro. A majority of tumor cells were died after 24 hrs. This time we estimated the effect of the PRS for brain tumors. We underwent the PRS to 72 patients from June 1995 to May 1999. Sixty-eight patients underwent intraoperative irradiation after removal and 4 patients had interstitial irradiation after stereotactic biopsy. All 16 cases of primary anaplastic astrocytomas survived and demonstrated good Karnofski performance scale. Median survival tomes of 17 primary cases of glioblastoma is 14 month. Two cases of malignant lymphoma showed complete remission in CT scan 24 hours after intraoperative radiosurgery using PRS and 2 cases of germ cell tumor demonstrated dramatic decrease of tumor size in a short period. There was no definite newly neurological deficit. The intraoperative radiosurgery using PRS is useful adjuvant therapy for brain tumors. (author)

  7. Calcium-activated potassium channels mediated blood-brain tumor barrier opening in a rat metastatic brain tumor model

    Directory of Open Access Journals (Sweden)

    Ong John M

    2007-03-01

    Full Text Available Abstract Background The blood-brain tumor barrier (BTB impedes the delivery of therapeutic agents to brain tumors. While adequate delivery of drugs occurs in systemic tumors, the BTB limits delivery of anti-tumor agents into brain metastases. Results In this study, we examined the function and regulation of calcium-activated potassium (KCa channels in a rat metastatic brain tumor model. We showed that intravenous infusion of NS1619, a KCa channel agonist, and bradykinin selectively enhanced BTB permeability in brain tumors, but not in normal brain. Iberiotoxin, a KCa channel antagonist, significantly attenuated NS1619-induced BTB permeability increase. We found KCa channels and bradykinin type 2 receptors (B2R expressed in cultured human metastatic brain tumor cells (CRL-5904, non-small cell lung cancer, metastasized to brain, human brain microvessel endothelial cells (HBMEC and human lung cancer brain metastasis tissues. Potentiometric assays demonstrated the activity of KCa channels in metastatic brain tumor cells and HBMEC. Furthermore, we detected higher expression of KCa channels in the metastatic brain tumor tissue and tumor capillary endothelia as compared to normal brain tissue. Co-culture of metastatic brain tumor cells and brain microvessel endothelial cells showed an upregulation of KCa channels, which may contribute to the overexpression of KCa channels in tumor microvessels and selectivity of BTB opening. Conclusion These findings suggest that KCa channels in metastatic brain tumors may serve as an effective target for biochemical modulation of BTB permeability to enhance selective delivery of chemotherapeutic drugs to metastatic brain tumors.

  8. A comparison of neural network and fuzzy clustering techniques in segmenting magnetic resonance images of the brain

    Science.gov (United States)

    Hall, Lawrence O.; Bensaid, Amine M.; Clarke, Laurence P.; Velthuizen, Robert P.; Silbiger, Martin S.; Bezdek, James C.

    1992-01-01

    Magnetic resonance (MR) brain section images are segmented and then synthetically colored to give visual representations of the original data with three approaches: the literal and approximate fuzzy c-means unsupervised clustering algorithms and a supervised computational neural network, a dynamic multilayered perception trained with the cascade correlation learning algorithm. Initial clinical results are presented on both normal volunteers and selected patients with brain tumors surrounded by edema. Supervised and unsupervised segmentation techniques provide broadly similar results. Unsupervised fuzzy algorithms were visually observed to show better segmentation when compared with raw image data for volunteer studies. However, for a more complex segmentation problem with tumor/edema or cerebrospinal fluid boundary, where the tissues have similar MR relaxation behavior, inconsistency in rating among experts was observed.

  9. [Isolation and identification of brain tumor stem cells from human brain neuroepithelial tumors].

    Science.gov (United States)

    Fang, Jia-sheng; Deng, Yong-wen; Li, Ming-chu; Chen, Feng-Hua; Wang, Yan-jin; Lu, Ming; Fang, Fang; Wu, Jun; Yang, Zhuan-yi; Zhou, Xang-yang; Wang, Fei; Chen, Cheng

    2007-01-30

    To establish a simplified culture system for the isolation of brain tumor stem cells (BTSCs) from the tumors of human neuroepithelial tissue, to observe the growth and differentiation pattern of BTSCs, and to investigate their expression of the specific markers. Twenty-six patients with brain neuroepithelial tumors underwent tumor resection. Two pieces of tumor tissues were taken from each tumor to be dissociated, triturated into single cells in sterile DMEM-F12 medium, and then filtered. The tumor cells were seeded at a concentration of 200,000 viable cells per mL into serum-free DMEM-F12 medium simply supplemented with B27, human basic fibroblast growth factor (20 microg/L), human epidermal growth factor (20 microg /L), insulin (4 U/L), L-glutamine, penicillin and streptomycin. After the primary brain tumor spheres (BTSs) were generated, they were triturated again and passed in fresh medium. Limiting dilution assay was performed to observe the monoclone formation. 5-bromodeoxyuridine (BrdU) incorporation test was performed to observe the proliferation of the BTS. The BTSCs were cultured in mitogen-free DMEM-F12 medium supplemented with 10% fetal bovine serum to observe their differentiation. Immunocytochemistry was used to examine the expression of CD133 and nestin, specific markers of BTSC, and the rate of CD133 positive cells. Only a minority of subsets of cells from the tumors of neuroepithelial tissue had the capacity to survive, proliferate, and generate free-floating neurosphere-like BTSs in the simplified serum-free medium. These cells attached to the poly-L-lysine coated coverslips in the serum-supplemented medium and differentiated. The BTSCs were CD133 and nestin positive. The rate of CD133 positive cells in the tumor specimens was (21 +/- 6.2)% - (38 +/- 7.0)%. A new simplified culture system for the isolation of BTSCs is established. The tumors of human neuroepithelial tissue contain CD133 and nestin positive tumor stem cells which can be isolated

  10. Targeting Malignant Brain Tumors with Antibodies

    Directory of Open Access Journals (Sweden)

    Rok Razpotnik

    2017-09-01

    Full Text Available Antibodies have been shown to be a potent therapeutic tool. However, their use for targeting brain diseases, including neurodegenerative diseases and brain cancers, has been limited, particularly because the blood–brain barrier (BBB makes brain tissue hard to access by conventional antibody-targeting strategies. In this review, we summarize new antibody therapeutic approaches to target brain tumors, especially malignant gliomas, as well as their potential drawbacks. Many different brain delivery platforms for antibodies have been studied such as liposomes, nanoparticle-based systems, cell-penetrating peptides (CPPs, and cell-based approaches. We have already shown the successful delivery of single-chain fragment variable (scFv with CPP as a linker between two variable domains in the brain. Antibodies normally face poor penetration through the BBB, with some variants sufficiently passing the barrier on their own. A “Trojan horse” method allows passage of biomolecules, such as antibodies, through the BBB by receptor-mediated transcytosis (RMT. Such examples of therapeutic antibodies are the bispecific antibodies where one binding specificity recognizes and binds a BBB receptor, enabling RMT and where a second binding specificity recognizes an antigen as a therapeutic target. On the other hand, cell-based systems such as stem cells (SCs are a promising delivery system because of their tumor tropism and ability to cross the BBB. Genetically engineered SCs can be used in gene therapy, where they express anti-tumor drugs, including antibodies. Different types and sources of SCs have been studied for the delivery of therapeutics to the brain; both mesenchymal stem cells (MSCs and neural stem cells (NSCs show great potential. Following the success in treatment of leukemias and lymphomas, the adoptive T-cell therapies, especially the chimeric antigen receptor-T cells (CAR-Ts, are making their way into glioma treatment as another type of cell

  11. MR findings of metastatic brain tumors

    Energy Technology Data Exchange (ETDEWEB)

    Ahn, Joong Mo; Chang, Kee Hyun; Han, Moon Hee; Cha, Sang Hoon; Ryoo, Jae Wook [Seoul National University College of Medicine, Seoul (Korea, Republic of)

    1993-05-15

    The purpose of this study is to describe the magnetic resonance imaging (MR) findings of metastatic brain tumors with emphasis on the signal intensities of the lesion on MR. Thirty four patients with intracranial metastases were studies with MR imaging. The diagnosis was established on the basis of either brain biopsy or combination of brain MR findings and the presence of primary tumors. The primary tumors include lung cancer (n=18), breast cancer (n=3), stomach cancer (n=3), rectal cancer (n=1), renal cell carcinoma (n=1), hepatocellular carcinoma (n=1), ovarian cancer (n=1), thyroid cancer (n=1), melanoma (n=1) and unknown primary sites (n=4). The parenchymal lesion were solitary in 35% (12/34) and multiple in 65% (22/34). The size of lesions was variable, ranging from several millimetes to 5 cm in diameter. The corticomedullar junction of the cerebral hemispheres was the most common location of the lesions (68%). The signal intensity of solid portion of the lesions was usually either isointense (44%) or hypointense (29%) on T1-weighted images, whereas it appeared in isointense (47%), hypointense (8%) or hypointense (11%) on protion density-weighted or T2-weighted images. The remaining cases showed mixed signal intensities. The enhancement patterns were variable including nodular (<1 cm) (6%), homogeneous (19%), heterogeneous (10%), ring-like enhancement (22%) or mixed pattern (43%). The size of surrounding edema was larger than the tumor diameter in 76%. In conclusion, although there are no specific MR findings of intracranial metastasis except multiplicity, intracranial metastasis should be included in differential diagnosis with high priority, when a solitary mass showing isointensity on body T1- and T2-weighted images with massive surrounding edema, especially in the corticomedullary junction of the cerebral hemispheres is encountered.

  12. MR findings of metastatic brain tumors

    International Nuclear Information System (INIS)

    Ahn, Joong Mo; Chang, Kee Hyun; Han, Moon Hee; Cha, Sang Hoon; Ryoo, Jae Wook

    1993-01-01

    The purpose of this study is to describe the magnetic resonance imaging (MR) findings of metastatic brain tumors with emphasis on the signal intensities of the lesion on MR. Thirty four patients with intracranial metastases were studies with MR imaging. The diagnosis was established on the basis of either brain biopsy or combination of brain MR findings and the presence of primary tumors. The primary tumors include lung cancer (n=18), breast cancer (n=3), stomach cancer (n=3), rectal cancer (n=1), renal cell carcinoma (n=1), hepatocellular carcinoma (n=1), ovarian cancer (n=1), thyroid cancer (n=1), melanoma (n=1) and unknown primary sites (n=4). The parenchymal lesion were solitary in 35% (12/34) and multiple in 65% (22/34). The size of lesions was variable, ranging from several millimetes to 5 cm in diameter. The corticomedullar junction of the cerebral hemispheres was the most common location of the lesions (68%). The signal intensity of solid portion of the lesions was usually either isointense (44%) or hypointense (29%) on T1-weighted images, whereas it appeared in isointense (47%), hypointense (8%) or hypointense (11%) on protion density-weighted or T2-weighted images. The remaining cases showed mixed signal intensities. The enhancement patterns were variable including nodular (<1 cm) (6%), homogeneous (19%), heterogeneous (10%), ring-like enhancement (22%) or mixed pattern (43%). The size of surrounding edema was larger than the tumor diameter in 76%. In conclusion, although there are no specific MR findings of intracranial metastasis except multiplicity, intracranial metastasis should be included in differential diagnosis with high priority, when a solitary mass showing isointensity on body T1- and T2-weighted images with massive surrounding edema, especially in the corticomedullary junction of the cerebral hemispheres is encountered

  13. Primary brain tumors, neural stem cell, and brain tumor cancer cells: where is the link?

    Science.gov (United States)

    Germano, Isabelle; Swiss, Victoria; Casaccia, Patrizia

    2010-01-01

    The discovery of brain tumor-derived cells (BTSC) with the properties of stem cells has led to the formulation of the hypothesis that neural stem cells could be the cell of origin of primary brain tumors (PBT). In this review we present the most common molecular changes in PBT, define the criteria of identification of BTSC and discuss the similarities between the characteristics of these cells and those of the endogenous population of neural stem cells (NPCs) residing in germinal areas of the adult brain. Finally, we propose possible mechanisms of cancer initiation and progression and suggest a model of tumor initiation that includes intrinsic changes of resident NSC and potential changes in the microenvironment defining the niche where the NSC reside. PMID:20045420

  14. Brain Tumor Database, a free relational database for collection and analysis of brain tumor patient information.

    Science.gov (United States)

    Bergamino, Maurizio; Hamilton, David J; Castelletti, Lara; Barletta, Laura; Castellan, Lucio

    2015-03-01

    In this study, we describe the development and utilization of a relational database designed to manage the clinical and radiological data of patients with brain tumors. The Brain Tumor Database was implemented using MySQL v.5.0, while the graphical user interface was created using PHP and HTML, thus making it easily accessible through a web browser. This web-based approach allows for multiple institutions to potentially access the database. The BT Database can record brain tumor patient information (e.g. clinical features, anatomical attributes, and radiological characteristics) and be used for clinical and research purposes. Analytic tools to automatically generate statistics and different plots are provided. The BT Database is a free and powerful user-friendly tool with a wide range of possible clinical and research applications in neurology and neurosurgery. The BT Database graphical user interface source code and manual are freely available at http://tumorsdatabase.altervista.org. © The Author(s) 2013.

  15. Application of Image Processing Algorithms for Brain Tumor Analysis in 2D and 3D Leading to Tumor’s Positioning in Skull: Overview

    Directory of Open Access Journals (Sweden)

    AYESHA AMIR SIDDIQI

    2017-01-01

    Full Text Available Segmentation of brain tumors has been found challenging throughout in the field of image processing. Different algorithms have been applied to the segmentation of solid or cystic tumors individually but little work has been done for solid cum cystic tumor. The papers reviewed in this article only deal with the case study of patients suffering from solid cum cystic brain tumor as this type of tumor is rarely found for the purpose of research. The research work conducted so far on this topic has been reviewed. The study begins with 2D (Two Dimensional segmentation of tumor using MATLAB. It is then extended to study of slices of tumor and its volume calculation using open source software named 3D Slicer which represents the tumor in 3D. This software can intake the 2D slices and process them to give a combined 3D view. Various techniques are available in the software. According to the particular requirement an appropriate algorithm can be chosen. This paper gives a promising hierarchy for volume calculation of tumor and the three dimensional view. Further we can also find the position of tumor in the skull using the same software. This piece of work is a valuable guideline for the researchers interested in segmentation and three dimensional representations of different areas of human body. The models extracted out using the given algorithms can also be treated for matching and comparison of any future research. This will also aid surgeons and physicians in efficient analysis and reporting techniques.

  16. Primary brain tumor presenting as intracranial hemorrhage

    International Nuclear Information System (INIS)

    Tsunoda, Shigeru; Sakaki, Toshisuke; Miyamoto, Seiji; Kyoi, Kikuo; Utsumi, Shozaburo; Kamada, Kitaro; Inui, Shoji; Masuda, Akio.

    1989-01-01

    Ten cases of primary brain tumor presenting as intracranial hemorrhage were studied in terms of the radiological and histological findings. The cases having hemorrhage in the tumor, as established through CT or histologically, were excluded if their onsets were not sudden due to intracranial hemorrhages. The results obtained may be summarized as follows: 1) From an anatomical point of view, cerebral subcortical hemorrhages account for 80%; hemorrhages in the cerebellopontine angle, 10%, and hemorrhages in the basal ganglia, 10%. 2) Plain CT findings showed perifocal low-density areas within 24 hours after onset in all 10 cases. 3) Enhanced CT findings showed enhanced areas in 4 or 6 cases. 4) Angiographic findings revealed abnormalities besides the mass effect in 5 of the 10 cases. 4) Angiographic findings revealed abnormalities besides the mass effect in 5 of the 10 cases. 5) From a histological point of view, glioblastomas account for 30%; malignant astrocytomas, 20%; astrocytomas, 20%; malignant ependymomas, 10%; hemangioblastoma, 10%, and transitional meningiomas, 10%. In conclusion, a perifocal low-density area on CT within 24 hours after onset is the most meaningful indication of intracranial hemorrhage originating from a brain tumor. A histological 'perinuclear halo' in an astrocytoma as an artifact due to hemorrhage may often be misleading in diagnosing mixed oligo-astrocytomas. (author)

  17. Targeting Nanomedicine to Brain Tumors: Latest Progress and Achievements.

    Science.gov (United States)

    Van't Root, Moniek; Lowik, Clemens; Mezzanotte, Laura

    2017-01-01

    Targeting nanomedicine to brain tumors is hampered by the heterogeneity of brain tumors and the blood brain barrier. These represent the main reasons of unsuccessful treatments. Nanomedicine based approaches hold promise for improved brain tissue distribution of drugs and delivery of combination therapies. In this review, we describe the recent advancements and latest achievements in the use of nanocarriers, virus and cell-derived nanoparticles for targeted therapy of brain tumors. We provide successful examples of nanomedicine based approaches for direct targeting of receptors expressed in brain tumor cells or modulation of pathways involved in cell survival as well as approaches for indirect targeting of cells in the tumor stroma and immunotherapies. Although the field is at its infancy, clinical trials involving nanomedicine based approaches for brain tumors are ongoing and many others will start in the near future. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

  18. Sub-cortical brain structure segmentation using F-CNN's

    OpenAIRE

    Shakeri, Mahsa; Tsogkas, Stavros; Ferrante, Enzo; Lippe, Sarah; Kadoury, Samuel; Paragios, Nikos; Kokkinos, Iasonas

    2016-01-01

    International audience; In this paper we propose a deep learning approach for segmenting sub-cortical structures of the human brain in Magnetic Resonance (MR) image data. We draw inspiration from a state-of-the-art Fully-Convolutional Neural Network (F-CNN) architecture for semantic segmentation of objects in natural images, and adapt it to our task. Unlike previous CNN-based methods that operate on image patches, our model is applied on a full blown 2D image, without any alignment or registr...

  19. Automatic segmentation of meningioma from non-contrasted brain MRI integrating fuzzy clustering and region growing

    Directory of Open Access Journals (Sweden)

    Liao Chun-Chih

    2011-08-01

    Full Text Available Abstract Background In recent years, magnetic resonance imaging (MRI has become important in brain tumor diagnosis. Using this modality, physicians can locate specific pathologies by analyzing differences in tissue character presented in different types of MR images. This paper uses an algorithm integrating fuzzy-c-mean (FCM and region growing techniques for automated tumor image segmentation from patients with menigioma. Only non-contrasted T1 and T2 -weighted MR images are included in the analysis. The study's aims are to correctly locate tumors in the images, and to detect those situated in the midline position of the brain. Methods The study used non-contrasted T1- and T2-weighted MR images from 29 patients with menigioma. After FCM clustering, 32 groups of images from each patient group were put through the region-growing procedure for pixels aggregation. Later, using knowledge-based information, the system selected tumor-containing images from these groups and merged them into one tumor image. An alternative semi-supervised method was added at this stage for comparison with the automatic method. Finally, the tumor image was optimized by a morphology operator. Results from automatic segmentation were compared to the "ground truth" (GT on a pixel level. Overall data were then evaluated using a quantified system. Results The quantified parameters, including the "percent match" (PM and "correlation ratio" (CR, suggested a high match between GT and the present study's system, as well as a fair level of correspondence. The results were compatible with those from other related studies. The system successfully detected all of the tumors situated at the midline of brain. Six cases failed in the automatic group. One also failed in the semi-supervised alternative. The remaining five cases presented noticeable edema inside the brain. In the 23 successful cases, the PM and CR values in the two groups were highly related. Conclusions Results indicated

  20. Automatic liver tumor segmentation on computed tomography for patient treatment planning and monitoring.

    Science.gov (United States)

    Moghbel, Mehrdad; Mashohor, Syamsiah; Mahmud, Rozi; Saripan, M Iqbal Bin

    2016-01-01

    Segmentation of liver tumors from Computed Tomography (CT) and tumor burden analysis play an important role in the choice of therapeutic strategies for liver diseases and treatment monitoring. In this paper, a new segmentation method for liver tumors from contrast-enhanced CT imaging is proposed. As manual segmentation of tumors for liver treatment planning is both labor intensive and time-consuming, a highly accurate automatic tumor segmentation is desired. The proposed framework is fully automatic requiring no user interaction. The proposed segmentation evaluated on real-world clinical data from patients is based on a hybrid method integrating cuckoo optimization and fuzzy c-means algorithm with random walkers algorithm. The accuracy of the proposed method was validated using a clinical liver dataset containing one of the highest numbers of tumors utilized for liver tumor segmentation containing 127 tumors in total with further validation of the results by a consultant radiologist. The proposed method was able to achieve one of the highest accuracies reported in the literature for liver tumor segmentation compared to other segmentation methods with a mean overlap error of 22.78 % and dice similarity coefficient of 0.75 in 3Dircadb dataset and a mean overlap error of 15.61 % and dice similarity coefficient of 0.81 in MIDAS dataset. The proposed method was able to outperform most other tumor segmentation methods reported in the literature while representing an overlap error improvement of 6 % compared to one of the best performing automatic methods in the literature. The proposed framework was able to provide consistently accurate results considering the number of tumors and the variations in tumor contrast enhancements and tumor appearances while the tumor burden was estimated with a mean error of 0.84 % in 3Dircadb dataset.

  1. Automatic liver tumor segmentation on computed tomography for patient treatment planning and monitoring

    Science.gov (United States)

    Moghbel, Mehrdad; Mashohor, Syamsiah; Mahmud, Rozi; Saripan, M. Iqbal Bin

    2016-01-01

    Segmentation of liver tumors from Computed Tomography (CT) and tumor burden analysis play an important role in the choice of therapeutic strategies for liver diseases and treatment monitoring. In this paper, a new segmentation method for liver tumors from contrast-enhanced CT imaging is proposed. As manual segmentation of tumors for liver treatment planning is both labor intensive and time-consuming, a highly accurate automatic tumor segmentation is desired. The proposed framework is fully automatic requiring no user interaction. The proposed segmentation evaluated on real-world clinical data from patients is based on a hybrid method integrating cuckoo optimization and fuzzy c-means algorithm with random walkers algorithm. The accuracy of the proposed method was validated using a clinical liver dataset containing one of the highest numbers of tumors utilized for liver tumor segmentation containing 127 tumors in total with further validation of the results by a consultant radiologist. The proposed method was able to achieve one of the highest accuracies reported in the literature for liver tumor segmentation compared to other segmentation methods with a mean overlap error of 22.78 % and dice similarity coefficient of 0.75 in 3Dircadb dataset and a mean overlap error of 15.61 % and dice similarity coefficient of 0.81 in MIDAS dataset. The proposed method was able to outperform most other tumor segmentation methods reported in the literature while representing an overlap error improvement of 6 % compared to one of the best performing automatic methods in the literature. The proposed framework was able to provide consistently accurate results considering the number of tumors and the variations in tumor contrast enhancements and tumor appearances while the tumor burden was estimated with a mean error of 0.84 % in 3Dircadb dataset. PMID:27540353

  2. Intraoperative MRI in pediatric brain tumors

    International Nuclear Information System (INIS)

    Choudhri, Asim F.; Siddiqui, Adeel; Klimo, Paul; Boop, Frederick A.

    2015-01-01

    Intraoperative magnetic resonance imaging (iMRI) has emerged as an important tool in guiding the surgical management of children with brain tumors. Recent advances have allowed utilization of high field strength systems, including 3-tesla MRI, resulting in diagnostic-quality scans that can be performed while the child is on the operating table. By providing information about the possible presence of residual tumor, it allows the neurosurgeon to both identify and resect any remaining tumor that is thought to be safely accessible. By fusing the newly obtained images with the surgical guidance software, the images have the added value of aiding in navigation to any residual tumor. This is important because parenchyma often shifts during surgery. It also gives the neurosurgeon insight into whether any immediate postoperative complications have occurred. If any complications have occurred, the child is already in the operating room and precious minutes lost in transport and communications are saved. In this article we review the three main approaches to an iMRI system design. We discuss the possible roles for iMRI during intraoperative planning and provide guidance to help radiologists and neurosurgeons alike in the collaborative management of these children. (orig.)

  3. Photodynamic Therapy for Malignant Brain Tumors.

    Science.gov (United States)

    Akimoto, Jiro

    2016-01-01

    Photodynamic therapy (PDT) using talaporfin sodium together with a semiconductor laser was approved in Japan in October 2003 as a less invasive therapy for early-stage lung cancer. The author believes that the principle of PDT would be applicable for controlling the invading front of malignant brain tumors and verified its efficacy through experiments using glioma cell lines and glioma xenograft models. An investigator-initiated clinical study was jointly conducted with Tokyo Women's Medical University with the support of the Japan Medical Association. Patient enrollment was started in May 2009 and a total of 27 patients were enrolled by March 2012. Of 22 patients included in efficacy analysis, 13 patients with newly diagnosed glioblastoma showed progression-free survival of 12 months, progression-free survival at the site of laser irradiation of 20 months, 1-year survival of 100%, and overall survival of 24.8 months. In addition, the safety analysis of the 27 patients showed that adverse events directly related to PDT were mild. PDT was approved in Japan for health insurance coverage as a new intraoperative therapy with the indication for malignant brain tumors in September 2013. Currently, the post-marketing investigation in the accumulated patients has been conducted, and the preparation of guidelines, holding training courses, and dissemination of information on the safe implementation of PDT using web sites and videos, have been promoted. PDT is expected to be a breakthrough for the treatment of malignant glioma as a tumor cell-selective less invasive therapy for the infiltrated functional brain area.

  4. Multimodal navigated skull base tumor resection using image-based vascular and cranial nerve segmentation: A prospective pilot study.

    Science.gov (United States)

    Dolati, Parviz; Gokoglu, Abdulkerim; Eichberg, Daniel; Zamani, Amir; Golby, Alexandra; Al-Mefty, Ossama

    2015-01-01

    Skull base tumors frequently encase or invade adjacent normal neurovascular structures. For this reason, optimal tumor resection with incomplete knowledge of patient anatomy remains a challenge. To determine the accuracy and utility of image-based preoperative segmentation in skull base tumor resections, we performed a prospective study. Ten patients with skull base tumors underwent preoperative 3T magnetic resonance imaging, which included thin section three-dimensional (3D) space T2, 3D time of flight, and magnetization-prepared rapid acquisition gradient echo sequences. Imaging sequences were loaded in the neuronavigation system for segmentation and preoperative planning. Five different neurovascular landmarks were identified in each case and measured for accuracy using the neuronavigation system. Each segmented neurovascular element was validated by manual placement of the navigation probe, and errors of localization were measured. Strong correspondence between image-based segmentation and microscopic view was found at the surface of the tumor and tumor-normal brain interfaces in all cases. The accuracy of the measurements was 0.45 ± 0.21 mm (mean ± standard deviation). This information reassured the surgeon and prevented vascular injury intraoperatively. Preoperative segmentation of the related cranial nerves was possible in 80% of cases and helped the surgeon localize involved cranial nerves in all cases. Image-based preoperative vascular and neural element segmentation with 3D reconstruction is highly informative preoperatively and could increase the vigilance of neurosurgeons for preventing neurovascular injury during skull base surgeries. Additionally, the accuracy found in this study is superior to previously reported measurements. This novel preliminary study is encouraging for future validation with larger numbers of patients.

  5. Phosphorylethanolamine content of human brain tumors.

    Science.gov (United States)

    Kinoshita, Y; Yokota, A; Koga, Y

    1994-12-01

    Phosphorylethanolamine (PEA) is the major component of the phosphomonoester peak detected by phosphorus-31 magnetic resonance spectroscopy, but the absolute concentration has not been determined. This study measured the PEA concentration in biopsy specimens of brain tumors and lobectomized cerebral cortex using high-performance liquid chromatography. The concentration of PEA was 118.5 +/- 10.0 mumol/100 g wet wt in cortex, and was significantly higher in malignant gliomas, metastatic pulmonary adenocarcinoma, and neurinoma. The concentration of PEA was especially high in pituitary adenoma, malignant lymphoma, and medulloblastoma.

  6. A novel algorithm for segmentation of brain MR images

    International Nuclear Information System (INIS)

    Sial, M.Y.; Yu, L.; Chowdhry, B.S.; Rajput, A.Q.K.; Bhatti, M.I.

    2006-01-01

    Accurate and fully automatic segmentation of brain from magnetic resonance (MR) scans is a challenging problem that has received an enormous amount of . attention lately. Many researchers have applied various techniques however a standard fuzzy c-means algorithm has produced better results compared to other methods. In this paper, we present a modified fuzzy c-means (FCM) based algorithm for segmentation of brain MR images. Our algorithm is formulated by modifying the objective function of the standard FCM and uses a special spread method to get a smooth and slow varying bias field This method has the advantage that it can be applied at an early stage in an automated data analysis before a tissue model is available. The results on MRI images show that this method provides better results compared to standard FCM algorithms. (author)

  7. Multifunctional Nanoparticles for Brain Tumor Diagnosis and Therapy

    Science.gov (United States)

    Cheng, Yu; Morshed, Ramin; Auffinger, Brenda; Tobias, Alex L.; Lesniak, Maciej S.

    2013-01-01

    Brain tumors are a diverse group of neoplasms that often carry a poor prognosis for patients. Despite tremendous efforts to develop diagnostic tools and therapeutic avenues, the treatment of brain tumors remains a formidable challenge in the field of neuro-oncology. Physiological barriers including the blood-brain barrier result in insufficient accumulation of therapeutic agents at the site of a tumor, preventing adequate destruction of malignant cells. Furthermore, there is a need for improvements in brain tumor imaging to allow for better characterization and delineation of tumors, visualization of malignant tissue during surgery, and tracking of response to chemotherapy and radiotherapy. Multifunctional nanoparticles offer the potential to improve upon many of these issues and may lead to breakthroughs in brain tumor management. In this review, we discuss the diagnostic and therapeutic applications of nanoparticles for brain tumors with an emphasis on innovative approaches in tumor targeting, tumor imaging, and therapeutic agent delivery. Clinically feasible nanoparticle administration strategies for brain tumor patients are also examined. Furthermore, we address the barriers towards clinical implementation of multifunctional nanoparticles in the context of brain tumor management. PMID:24060923

  8. Quantifying brain development in early childhood using segmentation and registration

    Science.gov (United States)

    Aljabar, P.; Bhatia, K. K.; Murgasova, M.; Hajnal, J. V.; Boardman, J. P.; Srinivasan, L.; Rutherford, M. A.; Dyet, L. E.; Edwards, A. D.; Rueckert, D.

    2007-03-01

    In this work we obtain estimates of tissue growth using longitudinal data comprising MR brain images of 25 preterm children scanned at one and two years. The growth estimates are obtained using segmentation and registration based methods. The segmentation approach used an expectation maximisation (EM) method to classify tissue types and the registration approach used tensor based morphometry (TBM) applied to a free form deformation (FFD) model. The two methods show very good agreement indicating that the registration and segmentation approaches can be used interchangeably. The advantage of the registration based method, however, is that it can provide more local estimates of tissue growth. This is the first longitudinal study of growth in early childhood, previous longitudinal studies have focused on later periods during childhood.

  9. Fetal antigen 2 in primary and secondary brain tumors

    DEFF Research Database (Denmark)

    Rasmussen, H Boje; Teisner, B; Schrøder, H D

    1991-01-01

    Immunohistochemical deposition and distribution of fetal antigen 2 (FA2) was examined in normal brain tissue and in primary and metastatic tumors of the brain. In normal brain tissue FA2 was exclusively found linearly around the vessels, along pia and in arachnoidea. A similar localization was seen...... in primary brain tumors except in gliosarcoma where FA2 was distributed diffusely in the sarcoma region and was absent in the glioma region. In metastatic carcinoma with tumor stroma a diffuse staining reaction was seen in the stroma and with a basement membrane (BM) like staining at the tumor cell....../stroma interface. Intracytoplasmic FA2 staining of the tumor cells was seen in areas without tumor stroma. In metastatic melanoma a BM like FA2 staining was seen around and between individual tumor cells. The staining patterns seen in the metastatic tumors were in accordance with that of the corresponding primary...

  10. Residual Deconvolutional Networks for Brain Electron Microscopy Image Segmentation.

    Science.gov (United States)

    Fakhry, Ahmed; Zeng, Tao; Ji, Shuiwang

    2017-02-01

    Accurate reconstruction of anatomical connections between neurons in the brain using electron microscopy (EM) images is considered to be the gold standard for circuit mapping. A key step in obtaining the reconstruction is the ability to automatically segment neurons with a precision close to human-level performance. Despite the recent technical advances in EM image segmentation, most of them rely on hand-crafted features to some extent that are specific to the data, limiting their ability to generalize. Here, we propose a simple yet powerful technique for EM image segmentation that is trained end-to-end and does not rely on prior knowledge of the data. Our proposed residual deconvolutional network consists of two information pathways that capture full-resolution features and contextual information, respectively. We showed that the proposed model is very effective in achieving the conflicting goals in dense output prediction; namely preserving full-resolution predictions and including sufficient contextual information. We applied our method to the ongoing open challenge of 3D neurite segmentation in EM images. Our method achieved one of the top results on this open challenge. We demonstrated the generality of our technique by evaluating it on the 2D neurite segmentation challenge dataset where consistently high performance was obtained. We thus expect our method to generalize well to other dense output prediction problems.

  11. The Brain's Cutting-Room Floor: Segmentation of Narrative Cinema

    Science.gov (United States)

    Zacks, Jeffrey M.; Speer, Nicole K.; Swallow, Khena M.; Maley, Corey J.

    2010-01-01

    Observers segment ongoing activity into meaningful events. Segmentation is a core component of perception that helps determine memory and guide planning. The current study tested the hypotheses that event segmentation is an automatic component of the perception of extended naturalistic activity, and that the identification of event boundaries in such activities results in part from processing changes in the perceived situation. Observers may identify boundaries between events as a result of processing changes in the observed situation. To test this hypothesis and study this potential mechanism, we measured brain activity while participants viewed an extended narrative film. Large transient responses were observed when the activity was segmented, and these responses were mediated by changes in the observed activity, including characters and their interactions, interactions with objects, spatial location, goals, and causes. These results support accounts that propose event segmentation is automatic and depends on processing meaningful changes in the perceived situation; they are the first to show such effects for extended naturalistic human activity. PMID:20953234

  12. An MRI digital brain phantom for validation of segmentation methods.

    Science.gov (United States)

    Alfano, Bruno; Comerci, Marco; Larobina, Michele; Prinster, Anna; Hornak, Joseph P; Selvan, S Easter; Amato, Umberto; Quarantelli, Mario; Tedeschi, Gioacchino; Brunetti, Arturo; Salvatore, Marco

    2011-06-01

    Knowledge of the exact spatial distribution of brain tissues in images acquired by magnetic resonance imaging (MRI) is necessary to measure and compare the performance of segmentation algorithms. Currently available physical phantoms do not satisfy this requirement. State-of-the-art digital brain phantoms also fall short because they do not handle separately anatomical structures (e.g. basal ganglia) and provide relatively rough simulations of tissue fine structure and inhomogeneity. We present a software procedure for the construction of a realistic MRI digital brain phantom. The phantom consists of hydrogen nuclear magnetic resonance spin-lattice relaxation rate (R1), spin-spin relaxation rate (R2), and proton density (PD) values for a 24 × 19 × 15.5 cm volume of a "normal" head. The phantom includes 17 normal tissues, each characterized by both mean value and variations in R1, R2, and PD. In addition, an optional tissue class for multiple sclerosis (MS) lesions is simulated. The phantom was used to create realistic magnetic resonance (MR) images of the brain using simulated conventional spin-echo (CSE) and fast field-echo (FFE) sequences. Results of mono-parametric segmentation of simulations of sequences with different noise and slice thickness are presented as an example of possible applications of the phantom. The phantom data and simulated images are available online at http://lab.ibb.cnr.it/. Copyright © 2011 Elsevier B.V. All rights reserved.

  13. Novel whole brain segmentation and volume estimation using quantitative MRI

    Energy Technology Data Exchange (ETDEWEB)

    West, J. [Linkoeping University, Radiation Physics, Department of Medical and Health Sciences, Faculty of Health Sciences, Linkoeping (Sweden); Linkoeping University, Center for Medical Imaging Science and Visualization (CMIV), Linkoeping (Sweden); SyntheticMR AB, Linkoeping (Sweden); Warntjes, J.B.M. [Linkoeping University, Center for Medical Imaging Science and Visualization (CMIV), Linkoeping (Sweden); SyntheticMR AB, Linkoeping (Sweden); Linkoeping University and Department of Clinical Physiology UHL, County Council of Oestergoetland, Clinical Physiology, Department of Medical and Health Sciences, Faculty of Health Sciences, Linkoeping (Sweden); Lundberg, P. [Linkoeping University, Center for Medical Imaging Science and Visualization (CMIV), Linkoeping (Sweden); Linkoeping University and Department of Radiation Physics UHL, County Council of Oestergoetland, Radiation Physics, Department of Medical and Health Sciences, Faculty of Health Sciences, Linkoeping (Sweden); Linkoeping University and Department of Radiology UHL, County Council of Oestergoetland, Radiology, Department of Medical and Health Sciences, Faculty of Health Sciences, Linkoeping (Sweden)

    2012-05-15

    Brain segmentation and volume estimation of grey matter (GM), white matter (WM) and cerebro-spinal fluid (CSF) are important for many neurological applications. Volumetric changes are observed in multiple sclerosis (MS), Alzheimer's disease and dementia, and in normal aging. A novel method is presented to segment brain tissue based on quantitative magnetic resonance imaging (qMRI) of the longitudinal relaxation rate R{sub 1}, the transverse relaxation rate R{sub 2} and the proton density, PD. Previously reported qMRI values for WM, GM and CSF were used to define tissues and a Bloch simulation performed to investigate R{sub 1}, R{sub 2} and PD for tissue mixtures in the presence of noise. Based on the simulations a lookup grid was constructed to relate tissue partial volume to the R{sub 1}-R{sub 2}-PD space. The method was validated in 10 healthy subjects. MRI data were acquired using six resolutions and three geometries. Repeatability for different resolutions was 3.2% for WM, 3.2% for GM, 1.0% for CSF and 2.2% for total brain volume. Repeatability for different geometries was 8.5% for WM, 9.4% for GM, 2.4% for CSF and 2.4% for total brain volume. We propose a new robust qMRI-based approach which we demonstrate in a patient with MS. (orig.)

  14. Improved Stability of Whole Brain Surface Parcellation with Multi-Atlas Segmentation

    OpenAIRE

    Huo, Yuankai; Bao, Shunxing; Parvathaneni, Prasanna; Landman, Bennett A.

    2017-01-01

    Whole brain segmentation and cortical surface parcellation are essential in understanding the anatomical-functional relationships of the brain. Multi-atlas segmentation has been regarded as one of the leading segmentation methods for the whole brain segmentation. In our recent work, the multi-atlas technique has been adapted to surface reconstruction using a method called Multi-atlas CRUISE (MaCRUISE). The MaCRUISE method not only performed consistent volume-surface analyses but also showed a...

  15. Obstacles to Brain Tumor Therapy: Key ABC Transporters

    Directory of Open Access Journals (Sweden)

    Juwina Wijaya

    2017-11-01

    Full Text Available The delivery of cancer chemotherapy to treat brain tumors remains a challenge, in part, because of the inherent biological barrier, the blood–brain barrier. While its presence and role as a protector of the normal brain parenchyma has been acknowledged for decades, it is only recently that the important transporter components, expressed in the tightly knit capillary endothelial cells, have been deciphered. These transporters are ATP-binding cassette (ABC transporters and, so far, the major clinically important ones that functionally contribute to the blood–brain barrier are ABCG2 and ABCB1. A further limitation to cancer therapy of brain tumors or brain metastases is the blood–tumor barrier, where tumors erect a barrier of transporters that further impede drug entry. The expression and regulation of these two transporters at these barriers, as well as tumor derived alteration in expression and/or mutation, are likely obstacles to effective therapy.

  16. Associations Between Family Adversity and Brain Volume in Adolescence: Manual vs. Automated Brain Segmentation Yields Different Results

    OpenAIRE

    Hannah Lyden; Sarah I. Gimbel; Larissa Del Piero; A. Bryna Tsai; Matthew Elliott Sachs; Matthew Elliott Sachs; Jonas T Kaplan; Gayla Margolin; Darby Saxbe

    2016-01-01

    Associations between brain structure and early adversity have been inconsistent in the literature. These inconsistencies may be partially due to methodological differences. Different methods of brain segmentation may produce different results, obscuring the relationship between early adversity and brain volume. Moreover, adolescence is a time of significant brain growth and certain brain areas have distinct rates of development, which may compromise the accuracy of automated segmentation appr...

  17. Associations between Family Adversity and Brain Volume in Adolescence: Manual vs. Automated Brain Segmentation Yields Different Results

    OpenAIRE

    Lyden, Hannah; Gimbel, Sarah I.; Del Piero, Larissa; Tsai, A. Bryna; Sachs, Matthew E.; Kaplan, Jonas T.; Margolin, Gayla; Saxbe, Darby

    2016-01-01

    Associations between brain structure and early adversity have been inconsistent in the literature. These inconsistencies may be partially due to methodological differences. Different methods of brain segmentation may produce different results, obscuring the relationship between early adversity and brain volume. Moreover, adolescence is a time of significant brain growth and certain brain areas have distinct rates of development, which may compromise the accuracy of automated segmentation appr...

  18. Childhood brain tumors: epidemiology, current management and future directions.

    Science.gov (United States)

    Pollack, Ian F; Jakacki, Regina I

    2011-07-26

    Brain tumors are the most common solid tumors in children. With the increasingly widespread availability of MRI, the incidence of childhood brain tumors seemed to rise in the 1980s, but has subsequently remained relatively stable. However, management of brain tumors in children has evolved substantially during this time, reflecting refinements in classification of tumors, delineation of risk groups within histological subsets of tumors, and incorporation of molecular techniques to further define tumor subgroups. Although considerable progress has been made in the outcomes of certain tumors, prognosis in other childhood brain tumor types is poor. Among the tumor groups with more-favorable outcomes, attention has been focused on reducing long-term morbidity without sacrificing survival rates. Studies for high-risk groups have examined the use of intensive therapy or novel, molecularly targeted approaches to improve disease control rates. In addition to reviewing the literature and providing an overview of the complexities in diagnosing childhood brain tumors, we will discuss advances in the treatment and categorization of several tumor types in which progress has been most apparent, as well as those in which improvements have been lacking. The latest insights from molecular correlative studies that hold potential for future refinements in therapy will also be discussed.

  19. Interphone study - on mobile phones and brain tumors

    International Nuclear Information System (INIS)

    2010-01-01

    Interphone study is the largest study on mobile phone use and risk of brain tumors that have been implemented. The study does not provide reliable answers to whether there is an increased risk of brain tumors using the mobile phone, but is an important contribution. (AG)

  20. Anticonvulsant therapy in brain-tumor related epilepsy

    Directory of Open Access Journals (Sweden)

    Fröscher Walter

    2016-06-01

    Full Text Available Background. The lifetime risk of patients with brain tumors to have focal epileptic seizures is 10-100%; the risk depends on different histology. Specific guidelines for drug treatment of brain tumor-related seizures have not yet been established.

  1. Brain Tumor Trials Collaborative | Center for Cancer Research

    Science.gov (United States)

    Brain Tumor Trials Collaborative In Pursuit of a Cure The mission of the BTTC is to develop and perform state-of-the-art clinical trials in a collaborative and collegial environment, advancing treatments for patients with brain tumors, merging good scientific method with concern for patient well-being and outcome.

  2. State-of-the-Art Methods for Brain Tissue Segmentation: A Review.

    Science.gov (United States)

    Dora, Lingraj; Agrawal, Sanjay; Panda, Rutuparna; Abraham, Ajith

    2017-01-01

    Brain tissue segmentation is one of the most sought after research areas in medical image processing. It provides detailed quantitative brain analysis for accurate disease diagnosis, detection, and classification of abnormalities. It plays an essential role in discriminating healthy tissues from lesion tissues. Therefore, accurate disease diagnosis and treatment planning depend merely on the performance of the segmentation method used. In this review, we have studied the recent advances in brain tissue segmentation methods and their state-of-the-art in neuroscience research. The review also highlights the major challenges faced during tissue segmentation of the brain. An effective comparison is made among state-of-the-art brain tissue segmentation methods. Moreover, a study of some of the validation measures to evaluate different segmentation methods is also discussed. The brain tissue segmentation, content in terms of methodologies, and experiments presented in this review are encouraging enough to attract researchers working in this field.

  3. Air pollution from traffic and risk for brain tumors

    DEFF Research Database (Denmark)

    Poulsen, Aslak Harbo; Sørensen, Mette; Andersen, Zorana J

    2016-01-01

    PURPOSE: Air pollution is an established lung carcinogen, and there is increasing evidence that air pollution also negatively affects the brain. We have previously reported an association between air pollution and risk of brain tumors in a cohort study based on only 95 cases. We set out...... to replicate that finding in a large nationwide case-control study. METHODS: We identified all 4,183 adult brain tumor cases in Denmark in the years 2000-2009 and 8,018 risk set sampled population controls matched on gender and year of birth. We extracted residential address histories and estimated mean...... and risk of brain tumors which was found in our previous study. The suggestion of an increased brain tumor risk at high exposures merits further attention as does the differing results according to tumor morphology....

  4. Stimulated Raman scattering microscopy for rapid brain tumor histology

    Directory of Open Access Journals (Sweden)

    Yifan Yang

    2017-09-01

    Full Text Available Rapid histology of brain tissues with sufficient diagnostic information has the great potential to aid neurosurgeons during operations. Stimulated Raman Scattering (SRS microscopy is an emerging label-free imaging technique, with the intrinsic chemical resolutions to delineate brain tumors from normal tissues without the need of time-consuming tissue processing. Growing number of studies have shown SRS as a “virtual histology” tool for rapid diagnosis of various types of brain tumors. In this review, we focus on the basic principles and current developments of SRS microscopy, as well as its applications for brain tumor imaging.

  5. A Cooperative Method to Improve Segmentation of Brain MR Images

    Directory of Open Access Journals (Sweden)

    lamiche chaabane

    2014-10-01

    Full Text Available In this paper , we present a fully unsupervised segmentation process of magnetic resonance image (MRI of the brain using a data fusion technique and some of ideas of the possibility theory context. The fusion methodology is decomposed into three fundamental phases. We modeling information coming from T2 and PD weighted images in a common framework, in this step an hybridization between FCM and PCM algorithms is retained. In the second phase an operator of fusion is used to combine then these information. Fi nally, an image of fusion is generated when a decision rule is applied. Some results are presented and discussed using a set of simulated MR image.

  6. Mixture Segmentation of Multispectral MR Brain Images for Multiple Sclerosis

    Directory of Open Access Journals (Sweden)

    Lihong Li

    2005-04-01

    Full Text Available We present a fully automatic mixture model-based tissue classification of multispectral (T1- and T2-weighted magnetic resonance (MR brain images. Unlike the conventional hard classification with a unique label for each voxel, our method models a mixture to estimate the partial volumes (PV of multiple tissue types within a voxel. A new Markov random field (MRF model is proposed to reflect the spatial information of tissue mixtures. A mixture classification algorithm is performed by the maximum a posterior (MAP criterion, where the expectation maximization (EM algorithm is utilized to estimate model parameters. The algorithm interleaves segmentation with parameter estimation and improves classification in an iterative manner. The presented method is evaluated by clinical MR image datasets for quantification of brain volumes and multiple sclerosis (MS.

  7. Preliminary study of MR elastography in brain tumors

    International Nuclear Information System (INIS)

    Xu Lei; Gao Peiyi; Lin Yan; Han Jiancheng; Xi Zhinong; Shen Hao

    2008-01-01

    Objective: To investigate the potential values of magnetic resonance elastography (MRE) for evaluating the brain tumor consistency in vivo. Methods: Fourteen patients with known solid brain tumor (5 male, 9 female; age range: 16-63 years) underwent brain MRE studies. Informed consent was obtained from all patients. A dedicated external force actuator for brain MRE study was developed. The actuator was fixed to the head coil. During scan, one side of the actuator was attached to the patients' head. Low frequency oscillation was produced by the actuator and caused shear waves propagating into brain tissue. The pulse sequence used in the study was phase-contrast gradient-echo sequence. Phase images of the brain were obtained and the shear waves within the brain were directly imaged. Phase images were processed with local frequency estimation (LFE) technique to obtain the elasticity image. Consistency of brain tumors was evaluated at surgery and was classified as soft, intermediate, or hard with comparison to the white matter of the brain. Correspondence of MRE evaluation with operative results was studied. Results: The elastic modulus of the tumor was lower than that of white matter in 1 patient, higher in 11 patients, and similar in 2 patients. At surgery, the tumor manifested a soft consistency in 1 patient, hard consistency in 11 patients, intermediate consistency in 2 patients. The elasticity of tumors in 14 patients evaluated by MRE was correlated with the tumor consistency on the operation. Conclusion: MRE can noninvasively display the elasticity of brain tumors in vivo, and evaluate the brain tumor consistency before operation. (authors)

  8. A developmental program drives aggressive embryonal brain tumors.

    Science.gov (United States)

    Archer, Tenley C; Pomeroy, Scott L

    2014-01-01

    Embryonal tumors with multilayered rosettes (ETMRs) are primitive neuroectodermal tumors arising in infants. A new study shows that these tumors are universally driven by fusion of the promoter of a gene with brain-specific expression, TTYH1, to C19MC, the largest human microRNA cluster, activating a fetal neural development program.

  9. Multi-parametric (ADC/PWI/T2-w) image fusion approach for accurate semi-automatic segmentation of tumorous regions in glioblastoma multiforme.

    Science.gov (United States)

    Fathi Kazerooni, Anahita; Mohseni, Meysam; Rezaei, Sahar; Bakhshandehpour, Gholamreza; Saligheh Rad, Hamidreza

    2015-02-01

    Glioblastoma multiforme (GBM) brain tumor is heterogeneous in nature, so its quantification depends on how to accurately segment different parts of the tumor, i.e. viable tumor, edema and necrosis. This procedure becomes more effective when metabolic and functional information, provided by physiological magnetic resonance (MR) imaging modalities, like diffusion-weighted-imaging (DWI) and perfusion-weighted-imaging (PWI), is incorporated with the anatomical magnetic resonance imaging (MRI). In this preliminary tumor quantification work, the idea is to characterize different regions of GBM tumors in an MRI-based semi-automatic multi-parametric approach to achieve more accurate characterization of pathogenic regions. For this purpose, three MR sequences, namely T2-weighted imaging (anatomical MR imaging), PWI and DWI of thirteen GBM patients, were acquired. To enhance the delineation of the boundaries of each pathogenic region (peri-tumoral edema, viable tumor and necrosis), the spatial fuzzy C-means algorithm is combined with the region growing method. The results show that exploiting the multi-parametric approach along with the proposed semi-automatic segmentation method can differentiate various tumorous regions with over 80 % sensitivity, specificity and dice score. The proposed MRI-based multi-parametric segmentation approach has the potential to accurately segment tumorous regions, leading to an efficient design of the pre-surgical treatment planning.

  10. An Adaptive Window-setting Scheme for Segmentation of Bladder Tumor Surface via MR Cystography

    Science.gov (United States)

    Duan, Chaijie; Liu, Fanghua; Xiao, Ping; Lv, Guoqing

    2012-01-01

    This paper proposes an adaptive window-setting scheme for non-invasive detection and segmentation of bladder tumor surface in T1-weighted magnetic resonance (MR) images. The inner border of the bladder wall is firstly covered by a group of ball-shaped detecting windows with different radii. By extracting the candidate tumor windows and excluding the false positive (FP) candidates, the entire bladder tumor surface is detected and segmented by the remaining windows. Different from previous bladder tumor detection methods which are mostly focusing on the existence of a tumor, this paper emphasizes segmenting the entire tumor surface in addition to detecting the presence of the tumor. The presented scheme was validated by 10 clinical T1-weighted MR image datasets (5 volunteers and 5 patients). The bladder tumor surfaces and the normal bladder wall inner borders in the ten datasets were covered by 223 and 10491 windows, respectively. Such large number of the detecting windows makes the validation statistically meaningful. In the FP reduction step, the best feature combination was obtained by using receiver operating characteristics or ROC analysis. The validation results demonstrated the potential of this presented scheme in segmenting the entire tumor surface with high sensitivity and low FP rate. This work inherits our previous results of automatic segmentation of the bladder wall and will be an important element in our MR-based virtual cystoscopy or MR cystography system. PMID:22645274

  11. Patients With Brain Tumors: Who Receives Postacute Occupational Therapy Services?

    Science.gov (United States)

    Chan, Vincy; Xiong, Chen; Colantonio, Angela

    2015-01-01

    Data on the utilization of occupational therapy among patients with brain tumors have been limited to those with malignant tumors and small samples of patients outside North America in specialized palliative care settings. We built on this research by examining the characteristics of patients with brain tumors who received postacute occupational therapy services in Ontario, Canada, using health care administrative data. Between fiscal years 2004-2005 and 2008-2009, 3,199 patients with brain tumors received occupational therapy services in the home care setting after hospital discharge; 12.4% had benign brain tumors, 78.2% had malignant brain tumors, and 9.4% had unspecified brain tumors. However, patients with benign brain tumors were older (mean age=63.3 yr), and a higher percentage were female (65.2%). More than 90% of patients received in-home occupational therapy services. Additional research is needed to examine the significance of these differences and to identify factors that influence access to occupational therapy services in the home care setting. Copyright © 2015 by the American Occupational Therapy Association, Inc.

  12. Detection of tumor recurrence using technetium99m-tetrofosmin brain SPECT in patients with previously irradiated brain tumors

    International Nuclear Information System (INIS)

    Llamas A; Reyes A; Uribe, L F; Martinez T

    2004-01-01

    Objective: to assess the clinical utility of brain SPECT with Tc-99m Tetrofosmin to differentiate between tumor recurrence and radionecrosis in patients with primary brain tumors previously treated with external beam radiotherapy. Materials and methods: thirteen patients with clinical or radiological suspicion of tumor recurrence were studied with brain SPECT using 20-mCi of Tc-99m Tetrofosmin. Obtained images were interpreted by consensus between two experienced observers and subsequently classified as positive or negative for tumor viability. Results were compared to those of conventional diagnostic imaging techniques. Diagnostic test values and 95% confidence intervals were quantified. Results: SPECT results included 7 true-positives, 5 true-negatives and 1 false negative result. Conclusions: Tc-99m Tetrofosmin brain SPECT night be a useful alternative to diagnose recurrent brain tumors, especially with non-conclusive clinical and radiological findings

  13. Brain MRI Tumor Detection using Active Contour Model and Local Image Fitting Energy

    Science.gov (United States)

    Nabizadeh, Nooshin; John, Nigel

    2014-03-01

    Automatic abnormality detection in Magnetic Resonance Imaging (MRI) is an important issue in many diagnostic and therapeutic applications. Here an automatic brain tumor detection method is introduced that uses T1-weighted images and K. Zhang et. al.'s active contour model driven by local image fitting (LIF) energy. Local image fitting energy obtains the local image information, which enables the algorithm to segment images with intensity inhomogeneities. Advantage of this method is that the LIF energy functional has less computational complexity than the local binary fitting (LBF) energy functional; moreover, it maintains the sub-pixel accuracy and boundary regularization properties. In Zhang's algorithm, a new level set method based on Gaussian filtering is used to implement the variational formulation, which is not only vigorous to prevent the energy functional from being trapped into local minimum, but also effective in keeping the level set function regular. Experiments show that the proposed method achieves high accuracy brain tumor segmentation results.

  14. Hypofractionation Regimens for Stereotactic Radiotherapy for Large Brain Tumors

    International Nuclear Information System (INIS)

    Yuan Jiankui; Wang, Jian Z.; Lo, Simon; Grecula, John C.; Ammirati, Mario; Montebello, Joseph F.; Zhang Hualin; Gupta, Nilendu; Yuh, William T.C.; Mayr, Nina A.

    2008-01-01

    Purpose: To investigate equivalent regimens for hypofractionated stereotactic radiotherapy (HSRT) for brain tumor treatment and to provide dose-escalation guidance to maximize the tumor control within the normal brain tolerance. Methods and Materials: The linear-quadratic model, including the effect of nonuniform dose distributions, was used to evaluate the HSRT regimens. The α/β ratio was estimated using the Gammaknife stereotactic radiosurgery (GKSRS) and whole-brain radiotherapy experience for large brain tumors. The HSRT regimens were derived using two methods: (1) an equivalent tumor control approach, which matches the whole-brain radiotherapy experience for many fractions and merges it with the GKSRS data for few fractions; and (2) a normal-tissue tolerance approach, which takes advantages of the dose conformity and fractionation of HSRT to approach the maximal dose tolerance of the normal brain. Results: A plausible α/β ratio of 12 Gy for brain tumor and a volume parameter n of 0.23 for normal brain were derived from the GKSRS and whole-brain radiotherapy data. The HSRT prescription regimens for the isoeffect of tumor irradiation were calculated. The normal-brain equivalent uniform dose decreased as the number of fractions increased, because of the advantage of fractionation. The regimens for potential dose escalation of HSRT within the limits of normal-brain tolerance were derived. Conclusions: The designed hypofractionated regimens could be used as a preliminary guide for HSRT dose prescription for large brain tumors to mimic the GKSRS experience and for dose escalation trials. Clinical studies are necessary to further tune the model parameters and validate these regimens

  15. Segmentation of liver and liver tumor for the Liver-Workbench

    Science.gov (United States)

    Zhou, Jiayin; Ding, Feng; Xiong, Wei; Huang, Weimin; Tian, Qi; Wang, Zhimin; Venkatesh, Sudhakar K.; Leow, Wee Kheng

    2011-03-01

    Robust and efficient segmentation tools are important for the quantification of 3D liver and liver tumor volumes which can greatly help clinicians in clinical decision-making and treatment planning. A two-module image analysis procedure which integrates two novel semi-automatic algorithms has been developed to segment 3D liver and liver tumors from multi-detector computed tomography (MDCT) images. The first module is to segment the liver volume using a flippingfree mesh deformation model. In each iteration, before mesh deformation, the algorithm detects and avoids possible flippings which will cause the self-intersection of the mesh and then the undesired segmentation results. After flipping avoidance, Laplacian mesh deformation is performed with various constraints in geometry and shape smoothness. In the second module, the segmented liver volume is used as the ROI and liver tumors are segmented by using support vector machines (SVMs)-based voxel classification and propagational learning. First a SVM classifier was trained to extract tumor region from one single 2D slice in the intermediate part of a tumor by voxel classification. Then the extracted tumor contour, after some morphological operations, was projected to its neighboring slices for automated sampling, learning and further voxel classification in neighboring slices. This propagation procedure continued till all tumorcontaining slices were processed. The performance of the whole procedure was tested using 20 MDCT data sets and the results were promising: Nineteen liver volumes were successfully segmented out, with the mean relative absolute volume difference (RAVD), volume overlap error (VOE) and average symmetric surface distance (ASSD) to reference segmentation of 7.1%, 12.3% and 2.5 mm, respectively. For live tumors segmentation, the median RAVD, VOE and ASSD were 7.3%, 18.4%, 1.7 mm, respectively.

  16. Computational modeling of brain tumors: discrete, continuum or hybrid?

    Science.gov (United States)

    Wang, Zhihui; Deisboeck, Thomas S.

    In spite of all efforts, patients diagnosed with highly malignant brain tumors (gliomas), continue to face a grim prognosis. Achieving significant therapeutic advances will also require a more detailed quantitative understanding of the dynamic interactions among tumor cells, and between these cells and their biological microenvironment. Data-driven computational brain tumor models have the potential to provide experimental tumor biologists with such quantitative and cost-efficient tools to generate and test hypotheses on tumor progression, and to infer fundamental operating principles governing bidirectional signal propagation in multicellular cancer systems. This review highlights the modeling objectives of and challenges with developing such in silico brain tumor models by outlining two distinct computational approaches: discrete and continuum, each with representative examples. Future directions of this integrative computational neuro-oncology field, such as hybrid multiscale multiresolution modeling are discussed.

  17. Differential diagnostic value of diffusion weighted imaging on brain abscess and necrotic or cystic brain tumors

    International Nuclear Information System (INIS)

    Zhang Xiaoya; Yin Jie; Wang Kunpeng; Zhang Jiandang; Liang Biling

    2009-01-01

    Objective: To investigate the value of diffusion weighted imaging (DWI)on brain abscess and necrotic or cystic brain tumors. Methods: 27 cases with brain abscesses and 33 cases with necrotic or cystic brain tumors (gliomas or metastases) were performed conventional MRI and DWI. Apparent diffusion coefficient (ADC) of region of interest (ROI) was measured and statistically tested. Sensitivity and specificity were calculated and compared with conventional MR and DWI. Results: Hyperintensity signal was seen on most brain abscesses. All necrotic or cystic brain tumors showed hypointensity signal on DWI. There was statistical significance on ADC of them. The sensitivity and specificity of conventional MRI was lower than that of DWI. Conclusion: DWI and ADC were useful in distinguishing brain abscessed from necrotic or cystic brain tumors, which was important in addition to conventional MRI. (authors)

  18. Quantitative Comparison of SPM, FSL, and Brainsuite for Brain MR Image Segmentation

    Directory of Open Access Journals (Sweden)

    Kazemi K

    2014-03-01

    Full Text Available Background: Accurate brain tissue segmentation from magnetic resonance (MR images is an important step in analysis of cerebral images. There are software packages which are used for brain segmentation. These packages usually contain a set of skull stripping, intensity non-uniformity (bias correction and segmentation routines. Thus, assessment of the quality of the segmented gray matter (GM, white matter (WM and cerebrospinal fluid (CSF is needed for the neuroimaging applications. Methods: In this paper, performance evaluation of three widely used brain segmentation software packages SPM8, FSL and Brainsuite is presented. Segmentation with SPM8 has been performed in three frameworks: i default segmentation, ii SPM8 New-segmentation and iii modified version using hidden Markov random field as implemented in SPM8-VBM toolbox. Results: The accuracy of the segmented GM, WM and CSF and the robustness of the tools against changes of image quality has been assessed using Brainweb simulated MR images and IBSR real MR images. The calculated similarity between the segmented tissues using different tools and corresponding ground truth shows variations in segmentation results. Conclusion: A few studies has investigated GM, WM and CSF segmentation. In these studies, the skull stripping and bias correction are performed separately and they just evaluated the segmentation. Thus, in this study, assessment of complete segmentation framework consisting of pre-processing and segmentation of these packages is performed. The obtained results can assist the users in choosing an appropriate segmentation software package for the neuroimaging application of interest.

  19. Labeled Putrescine as a Probe in Brain Tumors

    Science.gov (United States)

    Volkow, Nora; Goldman, Stephen S.; Flamm, Eugene S.; Cravioto, Humberto; Wolf, Alfred P.; Brodie, Jonathan D.

    1983-08-01

    The polyamine metabolism of transplanted N-nitrosomethylurea-derived rat glioma was determined with radiolabeled putrescine used as a marker for malignancy. The uptake of putrescine in vivo was complete within 5 minutes and was specific for tumor tissue. The conversion of putrescine to spermine and other metabolites by the tumor was rapid, in contrast to the case for adjacent normal brain. These results suggest that putrescine labeled with carbon-11 may be used as a positron-emission tomographic tracer for the selective metabolic imaging of brain tumor and may be used in an appropriate model as a marker for tumor growth rate.

  20. Sequential computed tomographic imaging of a transplantable rabbit brain tumor

    International Nuclear Information System (INIS)

    Kumar, A.J.; Rosenbaum, A.E.; Beck, T.J.; Ahn, H.S.; Anderson, J.

    1986-01-01

    The accuracy of CT imaging in evaluating VX-2 tumor growth in the rabbit brain was assessed. CT scanning was performed in 5 outbred New Zealand white male rabbits before and at 4, 7, 9 and 13 (in 3 animals) days after surgical implantation of 3 x 10 5 viable VX-2 tumor cells in the frontoparietal lobes. The CT studies were correlated with gross pathology in each. The tumor was visualized with CT in all 5 rabbits by the 9th day post implantation when the tumor ranged in size from 4-6 x 3-4 x 2-3 mm. Between the 9th and 13th day, the tumor increased 6-fold in two rabbits and 12-fold in the third rabbit. CT is a useful technique to evaluate brain tumor growth in this model and should be valuable in documenting the efficacy of chemotherapy on tumor growth. (orig.)

  1. Application of 31P MR spectroscopy to the brain tumors

    International Nuclear Information System (INIS)

    Ha, Dong Ho; Choi, Sun Seob; Oh, Jong Young; Yoon, Seong Kuk; Kang, Myong Jin; Kim, Ki Uk

    2013-01-01

    To evaluate the clinical feasibility and obtain useful parameters of 3 1P magnetic resonance spectroscopy (MRS) study for making the differential diagnosis of brain tumors. Twenty-eight patients with brain tumorous lesions (22 cases of brain tumor and 6 cases of abscess) and 11 normal volunteers were included. The patients were classified into the astrocytoma group, lymphoma group, metastasis group and the abscess group. We obtained the intracellular pH and the metabolite ratios of phosphomonoesters/phosophodiesters (PME/PDE), PME/inorganic phosphate (Pi), PDE/Pi, PME/adenosine triphosphate (ATP), PDE/ATP, PME/phosphocreatine (PCr), PDE/PCr, PCr/ATP, PCr/Pi, and ATP/Pi, and evaluated the statistical significances. The brain tumors had a tendency of alkalization (pH = 7.28 ± 0.27, p = 0.090), especially the pH of the lymphoma was significantly increased (pH = 7.45 ± 0.32, p = 0.013). The brain tumor group showed increased PME/PDE ratio compared with that in the normal control group (p 0.012). The ratios of PME/PDE, PDE/Pi, PME/PCr and PDE/PCr showed statistically significant differences between each brain lesion groups (p 1 'P MRS, and the pH, PME/PDE, PDE/Pi, PME/PCr, and PDE/PCr ratios are helpful for differentiating among the different types of brain tumors.

  2. Clinical results of BNCT for malignant brain tumors in children

    International Nuclear Information System (INIS)

    Nakagawa, Yoshinobu; Kageji, Teruyoshi; Mizobuchi, Yoshifumi; Kumada, Hiroaki; Nakagawa, Yoshiaki

    2009-01-01

    It is very difficult to treat the patients with malignant brain tumor in children, especially under 3 years, because the conventional irradiation cannot be applied due to the damage of normal brain tissue. However, boron neutron capture therapy (BNCT) has tumor selectivity such that it can make damage only in tumor cells. We evaluated the clinical results and courses in patients with malignant glioma under 15 years. Among 183 patients with brain tumors treated by our group using BSH-based intra-operative BNCT, 23 patients were under 15 years. They included 4 patients under 3 years. There were 3 glioblastomas (GBM), 6 anaplastic astrocytomas(AAS), 7 primitive neuroectodermal tumors (PNET), 6 pontine gliomas and 1 anaplastic ependymoma. All GBM and PNET patients died due to CSF and/or CNS dissemination without local tumor regrowth. All pontine glioma patients died due to regrowth of the tumor. Four of 6 anaplastic astrocytoma and 1 anaplastic ependymoma patients alive without tumor recurrence. BNCT can be applied to malignant brain tumors in children, especially under 3 years instead of conventional radiation. Although it can achieve the local control in the primary site, it cannot prevent CSF dissemination in patients with glioblastoma.

  3. From reverse transcription to human brain tumors

    Directory of Open Access Journals (Sweden)

    Dmitrenko V. V.

    2013-05-01

    Full Text Available Reverse transcriptase from avian myeloblastosis virus (AMV was the subject of the study, from which the investi- gations of the Department of biosynthesis of nucleic acids were started. Production of AMV in grams quantities and isolation of AMV reverse transcriptase were established in the laboratory during the seventies of the past cen- tury and this initiated research on the cDNA synthesis, cloning and investigation of the structure and functions of the eukaryotic genes. Structures of salmon insulin and insulin-like growth factor (IGF family genes and their transcripts were determined during long-term investigations. Results of two modern techniques, microarray-ba- sed hybridization and SAGE, were used for the identification of the genes differentially expressed in astrocytic gliomas and human normal brain. Comparison of SAGE results on the genes overexpressed in glioblastoma with the results of microarray analysis revealed a limited number of common genes. 105 differentially expressed genes, common to both methods, can be included in the list of candidates for the molecular typing of glioblastoma. The first experiments on the classification of glioblastomas based on the data of the 20 genes expression were conducted by using of artificial neural network analysis. The results of these experiments showed that the expression profiles of these genes in 224 glioblastoma samples and 74 normal brain samples could be according to the Koho- nen’s maps. The CHI3L1 and CHI3L2 genes of chitinase-like cartilage protein were revealed among the most overexpressed genes in glioblastoma, which could have prognostic and diagnostic potential. Results of in vitro experiments demonstrated that both proteins, CHI3L1 and CHI3L2, may initiate the phosphorylation of ERK1/ ERK2 and AKT kinases leading to the activation of MAPK/ERK1/2 and PI3K/AKT signaling cascades in human embryonic kidney 293 cells, human glioblastoma U87MG, and U373 cells. The new human cell line

  4. General Information about Childhood Brain and Spinal Cord Tumors

    Science.gov (United States)

    ... with cerebrospinal fluid shown in blue), choroid plexus, hypothalamus, pineal gland, pituitary gland, and optic nerve. The ... cord tumors are a common type of childhood cancer. Although cancer is rare in children, brain and ...

  5. Childhood Brain and Spinal Cord Tumors Treatment Overview

    Science.gov (United States)

    ... with cerebrospinal fluid shown in blue), choroid plexus, hypothalamus, pineal gland, pituitary gland, and optic nerve. The ... cord tumors are a common type of childhood cancer. Although cancer is rare in children, brain and ...

  6. Why does Jack, and not Jill, break his crown? Sex disparity in brain tumors

    OpenAIRE

    Sun, Tao; Warrington, Nicole M; Rubin, Joshua B

    2012-01-01

    Abstract It is often reported that brain tumors occur more frequently in males, and that males suffer a worse outcome from brain tumors than females. If correct, these observations suggest that sex plays a fundamental role in brain tumor biology. The following review of the literature regarding primary and metastatic brain tumors, reveals that brain tumors do occur more frequently in males compared to females regardless of age, tumor histology, or region of the world. Sexually dimorphic mecha...

  7. Modeling and Targeting MYC Genes in Childhood Brain Tumors

    Science.gov (United States)

    Hutter, Sonja; Bolin, Sara; Weishaupt, Holger; Swartling, Fredrik J.

    2017-01-01

    Brain tumors are the second most common group of childhood cancers, accounting for about 20%–25% of all pediatric tumors. Deregulated expression of the MYC family of transcription factors, particularly c-MYC and MYCN genes, has been found in many of these neoplasms, and their expression levels are often correlated with poor prognosis. Elevated c-MYC/MYCN initiates and drives tumorigenesis in many in vivo model systems of pediatric brain tumors. Therefore, inhibition of their oncogenic function is an attractive therapeutic target. In this review, we explore the roles of MYC oncoproteins and their molecular targets during the formation, maintenance, and recurrence of childhood brain tumors. We also briefly summarize recent progress in the development of therapeutic approaches for pharmacological inhibition of MYC activity in these tumors. PMID:28333115

  8. FDTD analysis of a noninvasive hyperthermia system for brain tumors

    Directory of Open Access Journals (Sweden)

    Yacoob Sulafa M

    2012-08-01

    Full Text Available Abstract Background Hyperthermia is considered one of the new therapeutic modalities for cancer treatment and is based on the difference in thermal sensitivity between healthy tissues and tumors. During hyperthermia treatment, the temperature of the tumor is raised to 40–45°C for a definite period resulting in the destruction of cancer cells. This paper investigates design, modeling and simulation of a new non-invasive hyperthermia applicator system capable of effectively heating deep seated as well as superficial brain tumors using inexpensive, simple, and easy to fabricate components without harming surrounding healthy brain tissues. Methods The proposed hyperthermia applicator system is composed of an air filled partial half ellipsoidal chamber, a patch antenna, and a head model with an embedded tumor at an arbitrary location. The irradiating antenna is placed at one of the foci of the hyperthermia chamber while the center of the brain tumor is placed at the other focus. The finite difference time domain (FDTD method is used to compute both the SAR patterns and the temperature distribution in three different head models due to two different patch antennas at a frequency of 915 MHz. Results The obtained results suggest that by using the proposed noninvasive hyperthermia system it is feasible to achieve sufficient and focused energy deposition and temperature rise to therapeutic values in deep seated as well as superficial brain tumors without harming surrounding healthy tissue. Conclusions The proposed noninvasive hyperthermia system proved suitable for raising the temperature in tumors embedded in the brain to therapeutic values by carefully selecting the systems components. The operator of the system only needs to place the center of the brain tumor at a pre-specified location and excite the antenna at a single frequency of 915 MHz. Our study may provide a basis for a clinical applicator prototype capable of heating brain tumors.

  9. [Tumor Cells and Micro-environment in Brain Metastases].

    Science.gov (United States)

    Zhong, Wen; Hu, Chengping

    2016-09-20

    Improvements in survival and quality of life of patients with lung cancer had been achieved due to the progression of early diagnosis and precision medicine at recent years, however, until now, treatments targeted at lesions in central nervous system are far from satisfying, thus threatening livelihood of patients involved. After all, in the issue of prophylaxis and therapeutics of brain metastases, it is crucial to learn about the biological behavior of tumor cells in brain metastases and its mechanism underlying, and the hypothesis "seed and soil", that is, tumor cells would generate series of adaptive changes to fit in the new environment, is liable to help explain this process well. In this assay, we reviewed documents concerning tumor cells, brain micro-environments and their interactions in brain metastases, aiming to provide novel insight into the treatments of brain metastases.

  10. Quality of Radiomic Features in Glioblastoma Multiforme: Impact of Semi-Automated Tumor Segmentation Software.

    Science.gov (United States)

    Lee, Myungeun; Woo, Boyeong; Kuo, Michael D; Jamshidi, Neema; Kim, Jong Hyo

    2017-01-01

    The purpose of this study was to evaluate the reliability and quality of radiomic features in glioblastoma multiforme (GBM) derived from tumor volumes obtained with semi-automated tumor segmentation software. MR images of 45 GBM patients (29 males, 16 females) were downloaded from The Cancer Imaging Archive, in which post-contrast T1-weighted imaging and fluid-attenuated inversion recovery MR sequences were used. Two raters independently segmented the tumors using two semi-automated segmentation tools (TumorPrism3D and 3D Slicer). Regions of interest corresponding to contrast-enhancing lesion, necrotic portions, and non-enhancing T2 high signal intensity component were segmented for each tumor. A total of 180 imaging features were extracted, and their quality was evaluated in terms of stability, normalized dynamic range (NDR), and redundancy, using intra-class correlation coefficients, cluster consensus, and Rand Statistic. Our study results showed that most of the radiomic features in GBM were highly stable. Over 90% of 180 features showed good stability (intra-class correlation coefficient [ICC] ≥ 0.8), whereas only 7 features were of poor stability (ICC NDR ≥1), while above 35% of the texture features showed poor NDR (software tools provided sufficiently reliable tumor segmentation and feature stability; thus helping to overcome the inherent inter-rater and intra-rater variability of user intervention. However, certain aspects of feature quality, including NDR and redundancy, need to be assessed for determination of representative signature features before further development of radiomics.

  11. Involvement of tumor acidification in brain cancer pathophysiology

    Directory of Open Access Journals (Sweden)

    Avinash eHonasoge

    2013-11-01

    Full Text Available Gliomas, primary brain cancers, are characterized by remarkable invasiveness and fast growth. While they share many qualities with other solid tumors, gliomas have developed special mechanisms to convert the cramped brain space and other limitations afforded by the privileged central nervous system into pathophysiological advantages. In this review we discuss gliomas and other primary brain cancers in the context of acid-base regulation and interstitial acidification; namely, how the altered proton (H+ content surrounding these brain tumors influences tumor development in both autocrine and paracrine manners. As proton movement is directly coupled to movement of other ions, pH serves as both a regulator of cell activity as well as an indirect readout of other cellular functions. In the case of brain tumors, these processes result in pathophysiology unique to the central nervous system. We will highlight what is known about pH-sensitive processes in brain tumors in addition to gleaning insight from other solid tumors.

  12. Growth of Malignant Non-CNS Tumors Alters Brain Metabolome

    Science.gov (United States)

    Kovalchuk, Anna; Nersisyan, Lilit; Mandal, Rupasri; Wishart, David; Mancini, Maria; Sidransky, David; Kolb, Bryan; Kovalchuk, Olga

    2018-01-01

    Cancer survivors experience numerous treatment side effects that negatively affect their quality of life. Cognitive side effects are especially insidious, as they affect memory, cognition, and learning. Neurocognitive deficits occur prior to cancer treatment, arising even before cancer diagnosis, and we refer to them as “tumor brain.” Metabolomics is a new area of research that focuses on metabolome profiles and provides important mechanistic insights into various human diseases, including cancer, neurodegenerative diseases, and aging. Many neurological diseases and conditions affect metabolic processes in the brain. However, the tumor brain metabolome has never been analyzed. In our study we used direct flow injection/mass spectrometry (DI-MS) analysis to establish the effects of the growth of lung cancer, pancreatic cancer, and sarcoma on the brain metabolome of TumorGraft™ mice. We found that the growth of malignant non-CNS tumors impacted metabolic processes in the brain, affecting protein biosynthesis, and amino acid and sphingolipid metabolism. The observed metabolic changes were similar to those reported for neurodegenerative diseases and brain aging, and may have potential mechanistic value for future analysis of the tumor brain phenomenon. PMID:29515623

  13. Novel strategies of Raman imaging for brain tumor research.

    Science.gov (United States)

    Anna, Imiela; Bartosz, Polis; Lech, Polis; Halina, Abramczyk

    2017-10-17

    Raman diagnostics and imaging have been shown to be an effective tool for the analysis and discrimination of human brain tumors from normal structures. Raman spectroscopic methods have potential to be applied in clinical practice as they allow for identification of tumor margins during surgery. In this study, we investigate medulloblastoma (grade IV WHO) (n= 5), low-grade astrocytoma (grades I-II WHO) (n =4), ependymoma (n=3) and metastatic brain tumors (n= 1) and the tissue from the negative margins used as normal controls. We compare a high grade medulloblastoma, low grade astrocytoma and non-tumor samples from human central nervous system (CNS) tissue. Based on the properties of the Raman vibrational features and Raman images we provide a real-time feedback method that is label-free to monitor tumor metabolism that reveals reprogramming of biosynthesis of lipids, proteins, DNA and RNA. Our results indicate marked metabolic differences between low and high grade brain tumors. We discuss molecular mechanisms causing these metabolic changes, particularly lipid alterations in malignant medulloblastoma and low grade gliomas that may shed light on the mechanisms driving tumor recurrence thereby revealing new approaches for the treatment of malignant glioma. We have found that the high-grade tumors of central nervous system (medulloblastoma) exhibit enhanced level of β-sheet conformation and down-regulated level of α-helix conformation when comparing against normal tissue. We have found that almost all tumors studied in the paper have increased Raman signals of nucleic acids. This increase can be interpreted as increased DNA/RNA turnover in brain tumors. We have shown that the ratio of Raman intensities I 2930 /I 2845 at 2930 and 2845 cm -1 is a good source of information on the ratio of lipid and protein contents. We have found that the ratio reflects the different lipid and protein contents of cancerous brain tissue compared to the non-tumor tissue. We found that

  14. Mindcontrol: A web application for brain segmentation quality control.

    Science.gov (United States)

    Keshavan, Anisha; Datta, Esha; M McDonough, Ian; Madan, Christopher R; Jordan, Kesshi; Henry, Roland G

    2018-04-15

    Tissue classification plays a crucial role in the investigation of normal neural development, brain-behavior relationships, and the disease mechanisms of many psychiatric and neurological illnesses. Ensuring the accuracy of tissue classification is important for quality research and, in particular, the translation of imaging biomarkers to clinical practice. Assessment with the human eye is vital to correct various errors inherent to all currently available segmentation algorithms. Manual quality assurance becomes methodologically difficult at a large scale - a problem of increasing importance as the number of data sets is on the rise. To make this process more efficient, we have developed Mindcontrol, an open-source web application for the collaborative quality control of neuroimaging processing outputs. The Mindcontrol platform consists of a dashboard to organize data, descriptive visualizations to explore the data, an imaging viewer, and an in-browser annotation and editing toolbox for data curation and quality control. Mindcontrol is flexible and can be configured for the outputs of any software package in any data organization structure. Example configurations for three large, open-source datasets are presented: the 1000 Functional Connectomes Project (FCP), the Consortium for Reliability and Reproducibility (CoRR), and the Autism Brain Imaging Data Exchange (ABIDE) Collection. These demo applications link descriptive quality control metrics, regional brain volumes, and thickness scalars to a 3D imaging viewer and editing module, resulting in an easy-to-implement quality control protocol that can be scaled for any size and complexity of study. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

  15. Primary Brain Tumors in Adults: Diagnosis and Treatment.

    Science.gov (United States)

    Perkins, Allen; Liu, Gerald

    2016-02-01

    Primary intracranial tumors of the brain structures, including meninges, are rare with an overall five-year survival rate of 33.4%; they are collectively called primary brain tumors. Proven risk factors for these tumors include certain genetic syndromes and exposure to high-dose ionizing radiation. Primary brain tumors are classified by histopathologic criteria and immunohistochemical data. The most common symptoms of these tumors are headache and seizures. Diagnosis of a suspected brain tumor is dependent on appropriate brain imaging and histopathology. The imaging modality of choice is gadolinium-enhanced magnetic resonance imaging. There is no specific pathognomonic feature on imaging that differentiates between primary brain tumors and metastatic or nonneoplastic disease. In cases of suspected or pathologically proven metastatic disease, chest and abdomen computed tomography may be helpful, although determining the site of the primary tumor is often difficult, especially if there are no clinical clues from the history and physical examination. Using fluorodeoxyglucose positron emission tomography to search for a primary lesion is not recommended because of low specificity for differentiating a neoplasm from benign or inflammatory lesions. Treatment decisions are individualized by a multidisciplinary team based on tumor type and location, malignancy potential, and the patient's age and physical condition. Treatment often includes a combination of surgery, radiotherapy, and chemotherapy. After craniotomy, patients should be followed closely for complications, including deep venous thrombosis, pulmonary embolism, intracranial bleeding, wound infection, systemic infection, seizure, depression, worsening neurologic status, and adverse drug reaction. Hospice and palliative care should be offered when appropriate throughout treatment.

  16. Evolution of Brain Tumor and Stability of Geometric Invariants

    Directory of Open Access Journals (Sweden)

    K. Tawbe

    2008-01-01

    Full Text Available This paper presents a method to reconstruct and to calculate geometric invariants on brain tumors. The geometric invariants considered in the paper are the volume, the area, the discrete Gauss curvature, and the discrete mean curvature. The volume of a tumor is an important aspect that helps doctors to make a medical diagnosis. And as doctors seek a stable calculation, we propose to prove the stability of some invariants. Finally, we study the evolution of brain tumor as a function of time in two or three years depending on patients with MR images every three or six months.

  17. Factors affecting intellectual outcome in pediatric brain tumor patients

    International Nuclear Information System (INIS)

    Ellenberg, L.; McComb, J.G.; Siegel, S.E.; Stowe, S.

    1987-01-01

    A prospective study utilizing repeated intellectual testing was undertaken in 73 children with brain tumors consecutively admitted to Childrens Hospital of Los Angeles over a 3-year period to determine the effect of tumor location, extent of surgical resection, hydrocephalus, age of the child, radiation therapy, and chemotherapy on cognitive outcome. Forty-three patients were followed for at least two sequential intellectual assessments and provide the data for this study. Children with hemispheric tumors had the most general cognitive impairment. The degree of tumor resection, adequately treated hydrocephalus, and chemotherapy had no bearing on intellectual outcome. Age of the child affected outcome mainly as it related to radiation. Whole brain radiation therapy was associated with cognitive decline. This was especially true in children below 7 years of age, who experienced a very significant loss of function after whole brain radiation therapy

  18. Critical Care Management of Cerebral Edema in Brain Tumors.

    Science.gov (United States)

    Esquenazi, Yoshua; Lo, Victor P; Lee, Kiwon

    2017-01-01

    Cerebral edema associated with brain tumors is extremely common and can occur in both primary and metastatic tumors. The edema surrounding brain tumors results from leakage of plasma across the vessel wall into the parenchyma secondary to disruption of the blood-brain barrier. The clinical signs of brain tumor edema depend on the location of the tumor as well as the extent of the edema, which often exceeds the mass effect induced by the tumor itself. Uncontrolled cerebral edema may result in increased intracranial pressure and acute herniation syndromes that can result in permanent neurological dysfunction and potentially fatal herniation. Treatment strategies for elevated intracranial pressure consist of general measures, medical interventions, and surgery. Alhough the definitive treatment for the edema may ultimately be surgical resection of the tumor, the impact of the critical care management cannot be underestimated and thus patients must be vigilantly monitored in the intensive care unit. In this review, we discuss the pathology, pathophysiology, and clinical features of patients presenting with cerebral edema. Imaging findings and treatment modalities used in the intensive care unit are also discussed. © The Author(s) 2015.

  19. Lung Tumor Segmentation Using Electric Flow Lines for Graph Cuts

    DEFF Research Database (Denmark)

    Hollensen, Christian; Cannon, George; Cannon, Donald

    2012-01-01

    Lung cancer is the most common cause of cancer-related death. A common treatment is radiotherapy where the lung tumors are irradiated with ionizing radiation. The treatment is typically fractionated, i.e. spread out over time, allowing healthy tissue to recover between treatments and allowing tum...

  20. Prediction of brain tumor progression using a machine learning technique

    Science.gov (United States)

    Shen, Yuzhong; Banerjee, Debrup; Li, Jiang; Chandler, Adam; Shen, Yufei; McKenzie, Frederic D.; Wang, Jihong

    2010-03-01

    A machine learning technique is presented for assessing brain tumor progression by exploring six patients' complete MRI records scanned during their visits in the past two years. There are ten MRI series, including diffusion tensor image (DTI), for each visit. After registering all series to the corresponding DTI scan at the first visit, annotated normal and tumor regions were overlaid. Intensity value of each pixel inside the annotated regions were then extracted across all of the ten MRI series to compose a 10 dimensional vector. Each feature vector falls into one of three categories:normal, tumor, and normal but progressed to tumor at a later time. In this preliminary study, we focused on the trend of brain tumor progression during three consecutive visits, i.e., visit A, B, and C. A machine learning algorithm was trained using the data containing information from visit A to visit B, and the trained model was used to predict tumor progression from visit A to visit C. Preliminary results showed that prediction for brain tumor progression is feasible. An average of 80.9% pixel-wise accuracy was achieved for tumor progression prediction at visit C.

  1. SU-C-207B-03: A Geometrical Constrained Chan-Vese Based Tumor Segmentation Scheme for PET

    Energy Technology Data Exchange (ETDEWEB)

    Chen, L; Zhou, Z; Wang, J [UT Southwestern Medical Center, Dallas, TX (United States)

    2016-06-15

    Purpose: Accurate segmentation of tumor in PET is challenging when part of tumor is connected with normal organs/tissues with no difference in intensity. Conventional segmentation methods, such as thresholding or region growing, cannot generate satisfactory results in this case. We proposed a geometrical constrained Chan-Vese based scheme to segment tumor in PET for this special case by considering the similarity between two adjacent slices. Methods: The proposed scheme performs segmentation in a slice-by-slice fashion where an accurate segmentation of one slice is used as the guidance for segmentation of rest slices. For a slice that the tumor is not directly connected to organs/tissues with similar intensity values, a conventional clustering-based segmentation method under user’s guidance is used to obtain an exact tumor contour. This is set as the initial contour and the Chan-Vese algorithm is applied for segmenting the tumor in the next adjacent slice by adding constraints of tumor size, position and shape information. This procedure is repeated until the last slice of PET containing tumor. The proposed geometrical constrained Chan-Vese based algorithm was implemented in Matlab and its performance was tested on several cervical cancer patients where cervix and bladder are connected with similar activity values. The positive predictive values (PPV) are calculated to characterize the segmentation accuracy of the proposed scheme. Results: Tumors were accurately segmented by the proposed method even when they are connected with bladder in the image with no difference in intensity. The average PPVs were 0.9571±0.0355 and 0.9894±0.0271 for 17 slices and 11 slices of PET from two patients, respectively. Conclusion: We have developed a new scheme to segment tumor in PET images for the special case that the tumor is quite similar to or connected to normal organs/tissues in the image. The proposed scheme can provide a reliable way for segmenting tumors.

  2. Associations Between Family Adversity and Brain Volume in Adolescence: Manual vs. Automated Brain Segmentation Yields Different Results

    Directory of Open Access Journals (Sweden)

    Hannah Lyden

    2016-09-01

    Full Text Available Associations between brain structure and early adversity have been inconsistent in the literature. These inconsistencies may be partially due to methodological differences. Different methods of brain segmentation may produce different results, obscuring the relationship between early adversity and brain volume. Moreover, adolescence is a time of significant brain growth and certain brain areas have distinct rates of development, which may compromise the accuracy of automated segmentation approaches. In the current study, 23 adolescents participated in two waves of a longitudinal study. Family aggression was measured when the youths were 12 years old, and structural scans were acquired an average of 4 years later. Bilateral amygdalae and hippocampi were segmented using three different methods (manual tracing, FSL, and NeuroQuant. The segmentation estimates were compared, and linear regressions were run to assess the relationship between early family aggression exposure and all three volume segmentation estimates. Manual tracing results showed a positive relationship between family aggression and right amygdala volume, whereas FSL segmentation showed negative relationships between family aggression and both the left and right hippocampi. However, results indicate poor overlap between methods, and different associations between early family aggression exposure and brain volume depending on the segmentation method used.

  3. Associations between Family Adversity and Brain Volume in Adolescence: Manual vs. Automated Brain Segmentation Yields Different Results.

    Science.gov (United States)

    Lyden, Hannah; Gimbel, Sarah I; Del Piero, Larissa; Tsai, A Bryna; Sachs, Matthew E; Kaplan, Jonas T; Margolin, Gayla; Saxbe, Darby

    2016-01-01

    Associations between brain structure and early adversity have been inconsistent in the literature. These inconsistencies may be partially due to methodological differences. Different methods of brain segmentation may produce different results, obscuring the relationship between early adversity and brain volume. Moreover, adolescence is a time of significant brain growth and certain brain areas have distinct rates of development, which may compromise the accuracy of automated segmentation approaches. In the current study, 23 adolescents participated in two waves of a longitudinal study. Family aggression was measured when the youths were 12 years old, and structural scans were acquired an average of 4 years later. Bilateral amygdalae and hippocampi were segmented using three different methods (manual tracing, FSL, and NeuroQuant). The segmentation estimates were compared, and linear regressions were run to assess the relationship between early family aggression exposure and all three volume segmentation estimates. Manual tracing results showed a positive relationship between family aggression and right amygdala volume, whereas FSL segmentation showed negative relationships between family aggression and both the left and right hippocampi. However, results indicate poor overlap between methods, and different associations were found between early family aggression exposure and brain volume depending on the segmentation method used.

  4. Impact of PET and MRI threshold-based tumor volume segmentation on patient-specific targeted radionuclide therapy dosimetry using CLR1404

    Science.gov (United States)

    Besemer, Abigail E.; Titz, Benjamin; Grudzinski, Joseph J.; Weichert, Jamey P.; Kuo, John S.; Robins, H. Ian; Hall, Lance T.; Bednarz, Bryan P.

    2017-08-01

    Variations in tumor volume segmentation methods in targeted radionuclide therapy (TRT) may lead to dosimetric uncertainties. This work investigates the impact of PET and MRI threshold-based tumor segmentation on TRT dosimetry in patients with primary and metastatic brain tumors. In this study, PET/CT images of five brain cancer patients were acquired at 6, 24, and 48 h post-injection of 124I-CLR1404. The tumor volume was segmented using two standardized uptake value (SUV) threshold levels, two tumor-to-background ratio (TBR) threshold levels, and a T1 Gadolinium-enhanced MRI threshold. The dice similarity coefficient (DSC), jaccard similarity coefficient (JSC), and overlap volume (OV) metrics were calculated to compare differences in the MRI and PET contours. The therapeutic 131I-CLR1404 voxel-level dose distribution was calculated from the 124I-CLR1404 activity distribution using RAPID, a Geant4 Monte Carlo internal dosimetry platform. The TBR, SUV, and MRI tumor volumes ranged from 2.3-63.9 cc, 0.1-34.7 cc, and 0.4-11.8 cc, respectively. The average  ±  standard deviation (range) was 0.19  ±  0.13 (0.01-0.51), 0.30  ±  0.17 (0.03-0.67), and 0.75  ±  0.29 (0.05-1.00) for the JSC, DSC, and OV, respectively. The DSC and JSC values were small and the OV values were large for both the MRI-SUV and MRI-TBR combinations because the regions of PET uptake were generally larger than the MRI enhancement. Notable differences in the tumor dose volume histograms were observed for each patient. The mean (standard deviation) 131I-CLR1404 tumor doses ranged from 0.28-1.75 Gy GBq-1 (0.07-0.37 Gy GBq-1). The ratio of maximum-to-minimum mean doses for each patient ranged from 1.4-2.0. The tumor volume and the interpretation of the tumor dose is highly sensitive to the imaging modality, PET enhancement metric, and threshold level used for tumor volume segmentation. The large variations in tumor doses clearly demonstrate the need for standard

  5. Preoperative coiling of coexisting intracranial aneurysm and subsequent brain tumor surgery

    Energy Technology Data Exchange (ETDEWEB)

    Park, Keun Young; Kim, Byung Moon; Kim, Dong Joon [Severance Hospital, Yonsei University College of Medicine, Seoul (Korea, Republic of)

    2016-11-15

    Few studies have investigated treatment strategies for brain tumor with a coexisting unruptured intracranial aneurysm (cUIA). The purpose of this study was to evaluate the safety and efficacy of preoperative coiling for cUIA, and subsequent brain tumor surgery. A total of 19 patients (mean age, 55.2 years; M:F = 4:15) underwent preoperative coiling for 23 cUIAs and subsequent brain tumor surgery. Primary brain tumors were meningiomas (n = 7, 36.8%), pituitary adenomas (n = 7, 36.8%), gliomas (n = 3, 15.8%), vestibular schwannoma (n = 1, 5.3%), and Rathke's cleft cyst (n = 1, 5.3%). cUIAs were located at the distal internal carotid artery (n = 9, 39.1%), anterior cerebral artery (n = 8, 34.8%), middle cerebral artery (n = 4, 17.4%), basilar artery top (n = 1, 4.3%), and posterior cerebral artery, P1 segment (n = 1, 4.3%). The outcomes of preoperative coiling of cUIA and subsequent brain tumor surgery were retrospectively evaluated. Single-microcatheter technique was used in 13 cases (56.5%), balloon-assisted in 4 cases (17.4%), double-microcatheter in 4 cases (17.4%), and stent-assisted in 2 cases (8.7%). Complete cUIA occlusion was achieved in 18 cases (78.3%), while residual neck occurred in 5 cases (21.7%). The only coiling-related complication was 1 transient ischemic attack (5.3%). Neurological deterioration did not occur in any patient during the period between coiling and tumor surgery. At the latest clinical follow-up (mean, 29 months; range, 2-120 months), 15 patients (78.9%) had favorable outcomes (modified Rankin Scale, 0-2), while 4 patients (21.1%) had unfavorable outcomes due to consequences of brain tumor surgery. Preoperative coiling and subsequent tumor surgery was safe and effective, making it a reasonable treatment option for patients with brain tumor and cUIA.

  6. Brain and Spinal Tumors: Hope through Research

    Science.gov (United States)

    ... understand, diagnose, and treat CNS tumors. Several of today’s treatment regimens were experimental therapies only a decade ... up Meeting Now That You Are Funded Small Business Grants Overview Areas of Interest Budget Information Grant ...

  7. Automated breast tumor detection and segmentation with a novel computational framework of whole ultrasound images.

    Science.gov (United States)

    Liu, Lei; Li, Kai; Qin, Wenjian; Wen, Tiexiang; Li, Ling; Wu, Jia; Gu, Jia

    2018-02-01

    Due to the low contrast and ambiguous boundaries of the tumors in breast ultrasound (BUS) images, it is still a challenging task to automatically segment the breast tumors from the ultrasound. In this paper, we proposed a novel computational framework that can detect and segment breast lesions fully automatic in the whole ultrasound images. This framework includes several key components: pre-processing, contour initialization, and tumor segmentation. In the pre-processing step, we applied non-local low-rank (NLLR) filter to reduce the speckle noise. In contour initialization step, we cascaded a two-step Otsu-based adaptive thresholding (OBAT) algorithm with morphologic operations to effectively locate the tumor regions and initialize the tumor contours. Finally, given the initial tumor contours, the improved Chan-Vese model based on the ratio of exponentially weighted averages (CV-ROEWA) method was utilized. This pipeline was tested on a set of 61 breast ultrasound (BUS) images with diagnosed tumors. The experimental results in clinical ultrasound images prove the high accuracy and robustness of the proposed framework, indicating its potential applications in clinical practice. Graphical abstract ᅟ.

  8. Image Analysis for MRI Based Brain Tumor Detection and Feature Extraction Using Biologically Inspired BWT and SVM

    Directory of Open Access Journals (Sweden)

    Nilesh Bhaskarrao Bahadure

    2017-01-01

    Full Text Available The segmentation, detection, and extraction of infected tumor area from magnetic resonance (MR images are a primary concern but a tedious and time taking task performed by radiologists or clinical experts, and their accuracy depends on their experience only. So, the use of computer aided technology becomes very necessary to overcome these limitations. In this study, to improve the performance and reduce the complexity involves in the medical image segmentation process, we have investigated Berkeley wavelet transformation (BWT based brain tumor segmentation. Furthermore, to improve the accuracy and quality rate of the support vector machine (SVM based classifier, relevant features are extracted from each segmented tissue. The experimental results of proposed technique have been evaluated and validated for performance and quality analysis on magnetic resonance brain images, based on accuracy, sensitivity, specificity, and dice similarity index coefficient. The experimental results achieved 96.51% accuracy, 94.2% specificity, and 97.72% sensitivity, demonstrating the effectiveness of the proposed technique for identifying normal and abnormal tissues from brain MR images. The experimental results also obtained an average of 0.82 dice similarity index coefficient, which indicates better overlap between the automated (machines extracted tumor region with manually extracted tumor region by radiologists. The simulation results prove the significance in terms of quality parameters and accuracy in comparison to state-of-the-art techniques.

  9. Subacute brain atrophy induced by radiation therapy to the malignant brain tumors

    International Nuclear Information System (INIS)

    Asai, Akio; Matsutani, Masao; Takakura, Kintomo.

    1987-01-01

    In order to analyze brain atrophy after radiation therapy to the brain tumors, we calculated a CSF-cranial volume ratio on CT scan as an index of brain atrophy, and estimated dementia-score by Hasegawa's method in 91 post-irradiated patients with malignant brain tumors. Radiation-induced brain atrophy was observed in 51 out of 91 patients (56 %) and dementia in 23 out of 47 patients (49 %). These two conditions were closely related, and observed significantly more often in aged and whole-brain-irradiated patients. As radiation-induced brain atrophy accompanied by dementia appeared 2 - 3 months after the completion of radiation therapy, it should be regarded as a subacute brain injury caused by radiation therapy. (author)

  10. Working Memory Performance among Childhood Brain Tumor Survivors

    Science.gov (United States)

    Conklin, Heather M.; Ashford, Jason M.; Howarth, Robyn A.; Merchant, Thomas E.; Ogg, Robert J.; Santana, Victor; Reddick, Wilburn E.; Wu, Shengjie; Xiong, Xiaoping

    2012-01-01

    While longitudinal studies of children treated for brain tumors have consistently revealed declines on measures of intellectual functioning, greater specification of cognitive changes following treatment is imperative for isolating vulnerable neural systems and developing targeted interventions. Accordingly, this cross-sectional study evaluated the performance of childhood brain tumor survivors (n= 50) treated with conformal radiation therapy, solid tumor survivors (n= 40) who had not received CNS-directed therapy, and healthy sibling controls (n= 40) on measures of working memory [Digit Span and computerized self-ordered search (SOS) tasks]. Findings revealed childhood brain tumor survivors were impaired on both traditional [Digit Span Backward- F(2, 127)= 5.98, p< .01] and experimental [SOS-Verbal- F(2, 124)= 4.18, p< .05; SOS-Object- F(2, 126)= 5.29, p< .01] measures of working memory, and performance on working memory measures correlated with intellectual functioning (Digit Span Backward- r= .45, p< .0001; SOS- r= −.32 − −.26, p< .01). Comparison of performance on working memory tasks to recognition memory tasks (computerized delayed match-to-sample) offered some support for greater working memory impairment. This pattern of findings is consistent with vulnerability in functional networks that include prefrontal brain regions and has implications for the clinical management of children with brain tumors. PMID:22691544

  11. AUTOMATIC BRAIN TUMOUR SEGMENTATION OF MAGNETIC RESONANCE IMAGES (MRI BASED ON REGION OF INTEREST (ROI

    Directory of Open Access Journals (Sweden)

    ANGULAKSHMI M.

    2017-04-01

    Full Text Available Segmentation is one of techniques used for classifying brain tissues in Magnetic Resonance Image (MRI for identifying anatomical structures in the brain. The automated brain tumour segmentation remains challenging and computationally intensive because tumour appears in different size and intensity. In this paper, we have proposed a method for fast and automatic segmentation of tumour from Region of Interest (ROI identified in MRI. ROI is a smaller portion of the image containing tumour. In the first step, tumour slices are identified using bilateral asymmetry property of the brain. In the second step, the ROI is identified using quadtree decomposition and similarity detection based on coefficient computed with gray level intensity histograms. In the third step, only the ROI is segmented using spectral clustering method rather than considering the whole image. Experimental results on real-world datasets are carried and compared with the recent existing works which show better results in terms of accuracy and less processing time for segmentation

  12. Groupwise registration of MR brain images with tumors

    Science.gov (United States)

    Tang, Zhenyu; Wu, Yihong; Fan, Yong

    2017-09-01

    A novel groupwise image registration framework is developed for registering MR brain images with tumors. Our method iteratively estimates a normal-appearance counterpart for each tumor image to be registered and constructs a directed graph (digraph) of normal-appearance images to guide the groupwise image registration. Particularly, our method maps each tumor image to its normal appearance counterpart by identifying and inpainting brain tumor regions with intensity information estimated using a low-rank plus sparse matrix decomposition based image representation technique. The estimated normal-appearance images are groupwisely registered to a group center image guided by a digraph of images so that the total length of ‘image registration paths’ to be the minimum, and then the original tumor images are warped to the group center image using the resulting deformation fields. We have evaluated our method based on both simulated and real MR brain tumor images. The registration results were evaluated with overlap measures of corresponding brain regions and average entropy of image intensity information, and Wilcoxon signed rank tests were adopted to compare different methods with respect to their regional overlap measures. Compared with a groupwise image registration method that is applied to normal-appearance images estimated using the traditional low-rank plus sparse matrix decomposition based image inpainting, our method achieved higher image registration accuracy with statistical significance (p  =  7.02  ×  10-9).

  13. Effect of fluosol and carbogen on rat brain tumor therapy

    International Nuclear Information System (INIS)

    Martin, D.F.; Kimler, B.F.; Evans, R.G.; Morantz, R.A.; Vats, T.S.

    1987-01-01

    The authors used the 9L rat brain tumor model to investigate the efficacy of a perfluorochemical emulsion as a potentiator of brain tumor therapy with two effective treatment modalities; BCNU and radiation. Rats with intracerebral 9L brain tumors were injected i.v. with 10 ml/kg Fluosol-DA 20%, (Alpha Therepeutic Corp., Los Angeles, CA), and held in carbogen, (95% oxygen, 5% carbon dioxide), during treatment with BCNU or radiation. The combination of Fluosol, carbogen-breathing, and BCNU was significantly (p < 0.025) more effective at prolonging median survival time (MST) than was BCNU alone. The MST for the Fluosol/carbogen/BCNU combination treatment was 42 days vs 34 days for BCNU alone and 24 days for untreated controls. Fluosol without carbogen did not alter the effect of BCNU; and the Fluosol/carbogen combination without BCNU did not alter survival. Carbogen-breathing without Fluosol did not have significant effect on BCNU therapy. Fluosol and carbogen-breathing did not alter the effect of single doses of radiation on these tumors. These results support the hypothesis that 9L brain tumors contain few, if any, critical hypoxic cells. However, these tumors may contain cells which, although not radiobiologically hypoxic, are oxygen-deficient to the extent that BCNU therapy can be enhanced by Fluosol and carbogen-breathing

  14. Application of 31P MR spectroscopy to the brain tumors.

    Science.gov (United States)

    Ha, Dong-Ho; Choi, Sunseob; Oh, Jong Young; Yoon, Seong Kuk; Kang, Myong Jin; Kim, Ki-Uk

    2013-01-01

    To evaluate the clinical feasibility and obtain useful parameters of (31)P magnetic resonance spectroscopy (MRS) study for making the differential diagnosis of brain tumors. Twenty-eight patients with brain tumorous lesions (22 cases of brain tumor and 6 cases of abscess) and 11 normal volunteers were included. The patients were classified into the astrocytoma group, lymphoma group, metastasis group and the abscess group. We obtained the intracellular pH and the metabolite ratios of phosphomonoesters/phosophodiesters (PME/PDE), PME/inorganic phosphate (Pi), PDE/Pi, PME/adenosine triphosphate (ATP), PDE/ATP, PME/phosphocreatine (PCr), PDE/PCr, PCr/ATP, PCr/Pi, and ATP/Pi, and evaluated the statistical significances. The brain tumors had a tendency of alkalization (pH = 7.28 ± 0.27, p = 0.090), especially the pH of the lymphoma was significantly increased (pH = 7.45 ± 0.32, p = 0.013). The brain tumor group showed increased PME/PDE ratio compared with that in the normal control group (p = 0.012). The ratios of PME/PDE, PDE/Pi, PME/PCr and PDE/PCr showed statistically significant differences between each brain lesion groups (p PME/PDE and PME/PCr ratio. The ratios of PDE/Pi, PME/PCr, and PDE/PCr in lymphoma group were lower than those in the control group and astrocytoma group. The metastasis group showed an increased PME/PDE ratio, compared with that in the normal control group. We have obtained the clinically applicable (31)P MRS, and the pH, PME/PDE, PDE/Pi, PME/PCr, and PDE/PCr ratios are helpful for differentiating among the different types of brain tumors.

  15. Evaluation of an automatic brain segmentation method developed for neonates on adult MR brain images

    Science.gov (United States)

    Moeskops, Pim; Viergever, Max A.; Benders, Manon J. N. L.; Išgum, Ivana

    2015-03-01

    Automatic brain tissue segmentation is of clinical relevance in images acquired at all ages. The literature presents a clear distinction between methods developed for MR images of infants, and methods developed for images of adults. The aim of this work is to evaluate a method developed for neonatal images in the segmentation of adult images. The evaluated method employs supervised voxel classification in subsequent stages, exploiting spatial and intensity information. Evaluation was performed using images available within the MRBrainS13 challenge. The obtained average Dice coefficients were 85.77% for grey matter, 88.66% for white matter, 81.08% for cerebrospinal fluid, 95.65% for cerebrum, and 96.92% for intracranial cavity, currently resulting in the best overall ranking. The possibility of applying the same method to neonatal as well as adult images can be of great value in cross-sectional studies that include a wide age range.

  16. Automatic Lung Tumor Segmentation on PET/CT Images Using Fuzzy Markov Random Field Model

    Directory of Open Access Journals (Sweden)

    Yu Guo

    2014-01-01

    Full Text Available The combination of positron emission tomography (PET and CT images provides complementary functional and anatomical information of human tissues and it has been used for better tumor volume definition of lung cancer. This paper proposed a robust method for automatic lung tumor segmentation on PET/CT images. The new method is based on fuzzy Markov random field (MRF model. The combination of PET and CT image information is achieved by using a proper joint posterior probability distribution of observed features in the fuzzy MRF model which performs better than the commonly used Gaussian joint distribution. In this study, the PET and CT simulation images of 7 non-small cell lung cancer (NSCLC patients were used to evaluate the proposed method. Tumor segmentations with the proposed method and manual method by an experienced radiation oncologist on the fused images were performed, respectively. Segmentation results obtained with the two methods were similar and Dice’s similarity coefficient (DSC was 0.85 ± 0.013. It has been shown that effective and automatic segmentations can be achieved with this method for lung tumors which locate near other organs with similar intensities in PET and CT images, such as when the tumors extend into chest wall or mediastinum.

  17. A noninvasive multimodal technique to monitor brain tumor vascularization

    Science.gov (United States)

    Saxena, Vishal; Gonzalez-Gomez, Ignacio; Laug, Walter E.

    2007-09-01

    Determination of tumor oxygenation at the microvascular level will provide important insight into tumor growth, angiogenesis, necrosis and therapeutic response and will facilitate to develop protocols for studying tumor behavior. The non-ionizing near infrared spectroscopy (NIRS) technique has the potential to differentiate lesion and hemoglobin dynamics; however, it has a limited spatial resolution. On the other hand, magnetic resonance imaging (MRI) has achieved high spatial resolution with excellent tissue discrimination but is more susceptible to limited ability to monitor the hemoglobin dynamics. In the present work, the vascular status and the pathophysiological changes that occur during tumor vascularization are studied in an orthotopic brain tumor model. A noninvasive multimodal approach based on the NIRS technique, namely steady state diffuse optical spectroscopy (SSDOS) along with MRI, is applied for monitoring the concentrations of oxyhemoglobin, deoxyhemoglobin and water within tumor region. The concentrations of oxyhemoglobin, deoxyhemoglobin and water within tumor vasculature are extracted at 15 discrete wavelengths in a spectral window of 675-780 nm. We found a direct correlation between tumor size, intratumoral microvessel density and tumor oxygenation. The relative decrease in tumor oxygenation with growth indicates that though blood vessels infiltrate and proliferate the tumor region, a hypoxic trend is clearly present.

  18. A noninvasive multimodal technique to monitor brain tumor vascularization

    International Nuclear Information System (INIS)

    Saxena, Vishal; Gonzalez-Gomez, Ignacio; Laug, Walter E

    2007-01-01

    Determination of tumor oxygenation at the microvascular level will provide important insight into tumor growth, angiogenesis, necrosis and therapeutic response and will facilitate to develop protocols for studying tumor behavior. The non-ionizing near infrared spectroscopy (NIRS) technique has the potential to differentiate lesion and hemoglobin dynamics; however, it has a limited spatial resolution. On the other hand, magnetic resonance imaging (MRI) has achieved high spatial resolution with excellent tissue discrimination but is more susceptible to limited ability to monitor the hemoglobin dynamics. In the present work, the vascular status and the pathophysiological changes that occur during tumor vascularization are studied in an orthotopic brain tumor model. A noninvasive multimodal approach based on the NIRS technique, namely steady state diffuse optical spectroscopy (SSDOS) along with MRI, is applied for monitoring the concentrations of oxyhemoglobin, deoxyhemoglobin and water within tumor region. The concentrations of oxyhemoglobin, deoxyhemoglobin and water within tumor vasculature are extracted at 15 discrete wavelengths in a spectral window of 675-780 nm. We found a direct correlation between tumor size, intratumoral microvessel density and tumor oxygenation. The relative decrease in tumor oxygenation with growth indicates that though blood vessels infiltrate and proliferate the tumor region, a hypoxic trend is clearly present

  19. Deep Learning for Brain MRI Segmentation: State of the Art and Future Directions.

    Science.gov (United States)

    Akkus, Zeynettin; Galimzianova, Alfiia; Hoogi, Assaf; Rubin, Daniel L; Erickson, Bradley J

    2017-08-01

    Quantitative analysis of brain MRI is routine for many neurological diseases and conditions and relies on accurate segmentation of structures of interest. Deep learning-based segmentation approaches for brain MRI are gaining interest due to their self-learning and generalization ability over large amounts of data. As the deep learning architectures are becoming more mature, they gradually outperform previous state-of-the-art classical machine learning algorithms. This review aims to provide an overview of current deep learning-based segmentation approaches for quantitative brain MRI. First we review the current deep learning architectures used for segmentation of anatomical brain structures and brain lesions. Next, the performance, speed, and properties of deep learning approaches are summarized and discussed. Finally, we provide a critical assessment of the current state and identify likely future developments and trends.

  20. Photon spectrum and absorbed dose in brain tumor

    Energy Technology Data Exchange (ETDEWEB)

    Silva S, A. [General Electric Healthcare, Antonio Dovali Jaime 70, Torre A 3er. piso, Col. Santa Fe, 01210 Mexico D. F. (Mexico); Vega C, H. R. [Universidad Autonoma de Zacatecas, Unidad Academica de Estudios Nucleares, Cipres No. 10, Fracc. La Penuela, 98068 Zacatecas, Zac. (Mexico); Rivera M, T. [IPN, Centro de Investigacion en Ciencia Aplicada y Tecnologia Avanzada, Av. Legaria No. 694, 11500 Mexico D. F. (Mexico)

    2015-10-15

    Using Monte Carlo methods a BOMAB phantom inside a treatment hall with a brain tumor nearby the pituitary gland was treated with photons produced by a Varian 6 MV linac. The photon spectrum and the absorbed dose were calculated in the tumor, pituitary gland and the head. The treatment beam was collimated to illuminate only the tumor volume; however photons were noticed in the gland. Photon fluence reaching the tumor is 78.1 times larger than the fluence in the pituitary gland, on the other hand the absorbed dose in the tumor is 188 times larger than the dose in the gland because photons that reach the pituitary gland are scattered, by the head and the tumor, through Compton effect. (Author)

  1. Detecting brain tumor in computed tomography images using Markov random fields and fuzzy C-means clustering techniques

    Science.gov (United States)

    Abdulbaqi, Hayder Saad; Jafri, Mohd Zubir Mat; Omar, Ahmad Fairuz; Mustafa, Iskandar Shahrim Bin; Abood, Loay Kadom

    2015-04-01

    Brain tumors, are an abnormal growth of tissues in the brain. They may arise in people of any age. They must be detected early, diagnosed accurately, monitored carefully, and treated effectively in order to optimize patient outcomes regarding both survival and quality of life. Manual segmentation of brain tumors from CT scan images is a challenging and time consuming task. Size and location accurate detection of brain tumor plays a vital role in the successful diagnosis and treatment of tumors. Brain tumor detection is considered a challenging mission in medical image processing. The aim of this paper is to introduce a scheme for tumor detection in CT scan images using two different techniques Hidden Markov Random Fields (HMRF) and Fuzzy C-means (FCM). The proposed method has been developed in this research in order to construct hybrid method between (HMRF) and threshold. These methods have been applied on 4 different patient data sets. The result of comparison among these methods shows that the proposed method gives good results for brain tissue detection, and is more robust and effective compared with (FCM) techniques.

  2. Irradiation effects on the tumor and adjacent tissues of brain tumor-bearing mice

    International Nuclear Information System (INIS)

    Yoshii, Yoshihiko; Maki, Yutaka; Tsunemoto, Hiroshi; Koike, Sachiko; Furukawa, Shigeo.

    1979-01-01

    C 3 H mice aged 56 - 70 days, weighing 27 - 37 g were used throughout this experiment. A transplantable fibrosarcoma arising spontaneously from C 3 H mice was used. For experiment, 10 4 tumor cells suspended in 0.025 ml of saline solution were injected into the cerebral hemisphere by a 26 gauge needle with a micrometer syringe under nembutal anesthesia. Whole brain irradiation was performed at 7 days after injection of the tumor cells and the radiation doses were 2,000 and 20,000 rads, respectively. The feature of x-rays were 200 kVp, 20 mA, 0.5 mm Cu + 0.5 mm Al filtration and TSD 20 cm. The dose-rate was 340 - 360 R/min. The articles of this study were as follows: a) Determination of LD 50 values for the mice, tumor-bearing in the brain or non-tumor-bearing; and b) Observation of clinical features and gross autopsy findings of the mice following irradiation. The LD 50 values for 2,000 rad irradiation in the tumor-bearing or non-tumor-bearing mice were 10.9 and 11.4 days, respectively. LD 50 values of 3.7 days and 4.3 days were the results for the tumor-bearing and non-tumor-bearing mice irradiated by 20,000 rad, respectively. On the other hand, the LD 50 value for the control group, i.e. non-irradiated mice, was 6.7 days. At postmortem examinations, gastrointestinal bleeding was observed frequently in mice bearing tumor in the brain. Whole brain irradiation is effective to prolong the life of tumor-bearing mice. However, in some instances, deaths have occurred earlier in tumor-bearing mice compared to the control group. (author)

  3. Automated ventricular systems segmentation in brain CT images by combining low-level segmentation and high-level template matching

    Directory of Open Access Journals (Sweden)

    Ward Kevin R

    2009-11-01

    Full Text Available Abstract Background Accurate analysis of CT brain scans is vital for diagnosis and treatment of Traumatic Brain Injuries (TBI. Automatic processing of these CT brain scans could speed up the decision making process, lower the cost of healthcare, and reduce the chance of human error. In this paper, we focus on automatic processing of CT brain images to segment and identify the ventricular systems. The segmentation of ventricles provides quantitative measures on the changes of ventricles in the brain that form vital diagnosis information. Methods First all CT slices are aligned by detecting the ideal midlines in all images. The initial estimation of the ideal midline of the brain is found based on skull symmetry and then the initial estimate is further refined using detected anatomical features. Then a two-step method is used for ventricle segmentation. First a low-level segmentation on each pixel is applied on the CT images. For this step, both Iterated Conditional Mode (ICM and Maximum A Posteriori Spatial Probability (MASP are evaluated and compared. The second step applies template matching algorithm to identify objects in the initial low-level segmentation as ventricles. Experiments for ventricle segmentation are conducted using a relatively large CT dataset containing mild and severe TBI cases. Results Experiments show that the acceptable rate of the ideal midline detection is over 95%. Two measurements are defined to evaluate ventricle recognition results. The first measure is a sensitivity-like measure and the second is a false positive-like measure. For the first measurement, the rate is 100% indicating that all ventricles are identified in all slices. The false positives-like measurement is 8.59%. We also point out the similarities and differences between ICM and MASP algorithms through both mathematically relationships and segmentation results on CT images. Conclusion The experiments show the reliability of the proposed algorithms. The

  4. Automated ventricular systems segmentation in brain CT images by combining low-level segmentation and high-level template matching.

    Science.gov (United States)

    Chen, Wenan; Smith, Rebecca; Ji, Soo-Yeon; Ward, Kevin R; Najarian, Kayvan

    2009-11-03

    Accurate analysis of CT brain scans is vital for diagnosis and treatment of Traumatic Brain Injuries (TBI). Automatic processing of these CT brain scans could speed up the decision making process, lower the cost of healthcare, and reduce the chance of human error. In this paper, we focus on automatic processing of CT brain images to segment and identify the ventricular systems. The segmentation of ventricles provides quantitative measures on the changes of ventricles in the brain that form vital diagnosis information. First all CT slices are aligned by detecting the ideal midlines in all images. The initial estimation of the ideal midline of the brain is found based on skull symmetry and then the initial estimate is further refined using detected anatomical features. Then a two-step method is used for ventricle segmentation. First a low-level segmentation on each pixel is applied on the CT images. For this step, both Iterated Conditional Mode (ICM) and Maximum A Posteriori Spatial Probability (MASP) are evaluated and compared. The second step applies template matching algorithm to identify objects in the initial low-level segmentation as ventricles. Experiments for ventricle segmentation are conducted using a relatively large CT dataset containing mild and severe TBI cases. Experiments show that the acceptable rate of the ideal midline detection is over 95%. Two measurements are defined to evaluate ventricle recognition results. The first measure is a sensitivity-like measure and the second is a false positive-like measure. For the first measurement, the rate is 100% indicating that all ventricles are identified in all slices. The false positives-like measurement is 8.59%. We also point out the similarities and differences between ICM and MASP algorithms through both mathematically relationships and segmentation results on CT images. The experiments show the reliability of the proposed algorithms. The novelty of the proposed method lies in its incorporation of

  5. Brain mapping in tumors: intraoperative or extraoperative?

    Science.gov (United States)

    Duffau, Hugues

    2013-12-01

    In nontumoral epilepsy surgery, the main goal for all preoperative investigation is to first determine the epileptogenic zone, and then to analyze its relation to eloquent cortex, in order to control seizures while avoiding adverse postoperative neurologic outcome. To this end, in addition to neuropsychological assessment, functional neuroimaging and scalp electroencephalography, extraoperative recording, and electrical mapping, especially using subdural strip- or grid-electrodes, has been reported extensively. Nonetheless, in tumoral epilepsy surgery, the rationale is different. Indeed, the first aim is rather to maximize the extent of tumor resection while minimizing postsurgical morbidity, in order to increase the median survival as well as to preserve quality of life. As a consequence, as frequently seen in infiltrating tumors such as gliomas, where these lesions not only grow but also migrate along white matter tracts, the resection should be performed according to functional boundaries both at cortical and subcortical levels. With this in mind, extraoperative mapping by strips/grids is often not sufficient in tumoral surgery, since in essence, it allows study of the cortex but cannot map subcortical pathways. Therefore, intraoperative electrostimulation mapping, especially in awake patients, is more appropriate in tumor surgery, because this technique allows real-time detection of areas crucial for cerebral functions--eloquent cortex and fibers--throughout the resection. In summary, rather than choosing one or the other of different mapping techniques, methodology should be adapted to each pathology, that is, extraoperative mapping in nontumoral epilepsy surgery and intraoperative mapping in tumoral surgery. Wiley Periodicals, Inc. © 2013 International League Against Epilepsy.

  6. VoxResNet: Deep voxelwise residual networks for brain segmentation from 3D MR images.

    Science.gov (United States)

    Chen, Hao; Dou, Qi; Yu, Lequan; Qin, Jing; Heng, Pheng-Ann

    2018-04-15

    Segmentation of key brain tissues from 3D medical images is of great significance for brain disease diagnosis, progression assessment and monitoring of neurologic conditions. While manual segmentation is time-consuming, laborious, and subjective, automated segmentation is quite challenging due to the complicated anatomical environment of brain and the large variations of brain tissues. We propose a novel voxelwise residual network (VoxResNet) with a set of effective training schemes to cope with this challenging problem. The main merit of residual learning is that it can alleviate the degradation problem when training a deep network so that the performance gains achieved by increasing the network depth can be fully leveraged. With this technique, our VoxResNet is built with 25 layers, and hence can generate more representative features to deal with the large variations of brain tissues than its rivals using hand-crafted features or shallower networks. In order to effectively train such a deep network with limited training data for brain segmentation, we seamlessly integrate multi-modality and multi-level contextual information into our network, so that the complementary information of different modalities can be harnessed and features of different scales can be exploited. Furthermore, an auto-context version of the VoxResNet is proposed by combining the low-level image appearance features, implicit shape information, and high-level context together for further improving the segmentation performance. Extensive experiments on the well-known benchmark (i.e., MRBrainS) of brain segmentation from 3D magnetic resonance (MR) images corroborated the efficacy of the proposed VoxResNet. Our method achieved the first place in the challenge out of 37 competitors including several state-of-the-art brain segmentation methods. Our method is inherently general and can be readily applied as a powerful tool to many brain-related studies, where accurate segmentation of brain

  7. Regional cerebral blood flow in the patient with brain tumor

    Energy Technology Data Exchange (ETDEWEB)

    Tsuchida, Shohei (Okayama Univ. (Japan). School of Medicine)

    1993-06-01

    Regional cerebral blood flow (rCBF) was measured with xenon-enhanced CT (Xe-CT) in 21 cases of intracranial tumors (13 meningiomas, 5 gliomas, 3 metastatic brain tumors). Peritumoral edema was graded as mild, moderate or severe based on the extent of edema on CT and MRI. According to intratumoral blood flow distribution patterns, three patterns were classified as central type with relatively high blood flow at the center of the tumor, homogeneous type with an almost homogeneous blood flow distribution, and marginal type with relatively high blood flow at the periphery of the tumor. High grade astrocytoma and metastatic brain tumor showed marginal type blood flow and moderate or severe edema except in one case. Five meningiomas with severe peritumoral edema revealed marginal type blood flow and four with mild peritumoral edema showed central type blood flow, except for one case. No correlation was found between the extent of peritumoral edema and histological subtype, tumor size, location, duration of clinical history, vascularization on angiogram, and mean blood flow in the tumor. These results suggest that blood flow distribution patterns within the tumor may affect the extension of peritumoral edema. Pre- and postoperative rCBFs were evaluated with Xe-CT and IMP-SPECT in 7 cases, mean rCBF of peritumoral edema was 6.2 ml/100 g/min preoperatively, and discrepancy between rCBF on Xe-CT and that on IMP-SPECT was shown in the remote cortical region ipsilateral to the tumor. Postoperative rCBF revealed an improved blood flow in both adjacent and remote areas, suggesting that the decreased blood flow associated with brain tumors might be relieved after surgery. (author) 53 refs.

  8. Regional cerebral blood flow in the patient with brain tumor

    International Nuclear Information System (INIS)

    Tsuchida, Shohei

    1993-01-01

    Regional cerebral blood flow (rCBF) was measured with xenon-enhanced CT (Xe-CT) in 21 cases of intracranial tumors (13 meningiomas, 5 gliomas, 3 metastatic brain tumors). Peritumoral edema was graded as mild, moderate or severe based on the extent of edema on CT and MRI. According to intratumoral blood flow distribution patterns, three patterns were classified as central type with relatively high blood flow at the center of the tumor, homogeneous type with an almost homogeneous blood flow distribution, and marginal type with relatively high blood flow at the periphery of the tumor. High grade astrocytoma and metastatic brain tumor showed marginal type blood flow and moderate or severe edema except in one case. Five meningiomas with severe peritumoral edema revealed marginal type blood flow and four with mild peritumoral edema showed central type blood flow, except for one case. No correlation was found between the extent of peritumoral edema and histological subtype, tumor size, location, duration of clinical history, vascularization on angiogram, and mean blood flow in the tumor. These results suggest that blood flow distribution patterns within the tumor may affect the extension of peritumoral edema. Pre- and postoperative rCBFs were evaluated with Xe-CT and IMP-SPECT in 7 cases, mean rCBF of peritumoral edema was 6.2 ml/100 g/min preoperatively, and discrepancy between rCBF on Xe-CT and that on IMP-SPECT was shown in the remote cortical region ipsilateral to the tumor. Postoperative rCBF revealed an improved blood flow in both adjacent and remote areas, suggesting that the decreased blood flow associated with brain tumors might be relieved after surgery. (author) 53 refs

  9. Epigenetic alterations in human brain tumors in a Brazilian population

    Directory of Open Access Journals (Sweden)

    Nilson Praia Anselmo

    2006-01-01

    Full Text Available Aberrant methylation of CpG islands located in promoter regions represents one of the major mechanisms for silencing cancer-related genes in tumor cells. We determined the frequency of aberrant CpG island methylation for several tumor-associated genes: DAPK, MGMT, p14ARF, p16INK4a, TP73, RB1 and TIMP-3 in 55 brain tumors, consisting of 26 neuroepithelial tumors, 6 peripheral nerve tumors, 13 meningeal tumors and 10 metastatic brain tumors. Aberrant methylation of at least one of the seven genes studied was detected in 83.6% of the cases. The frequencies of aberrant methylation were: 40% for p14ARF, 38.2% for MGMT, 30.9% for, p16INK4a, 14.6% for TP73 and for TIMP-3, 12.7% for DAPK and 1.8% for RB1. These data suggest that the hypermethylation observed in the genes p14ARF, MGMT and p16INK4a is a very important event in the formation or progression of brain tumors, since the inactivation of these genes directly interferes with the cell cycle or DNA repair. The altered methylation rate of the other genes has already been reported to be related to tumorigenesis, but the low methylation rate of RB1 found in tumors in our sample is different from that so far reported in the literature, suggesting that perhaps hypermethylation of the promoter is not the main event in the inactivation of this gene. Our results suggest that hypermethylation of the promoter region is a very common event in nervous system tumors.

  10. Simultaneous Tumor Segmentation, Image Restoration, and Blur Kernel Estimation in PET Using Multiple Regularizations.

    Science.gov (United States)

    Li, Laquan; Wang, Jian; Lu, Wei; Tan, Shan

    2017-02-01

    Accurate tumor segmentation from PET images is crucial in many radiation oncology applications. Among others, partial volume effect (PVE) is recognized as one of the most important factors degrading imaging quality and segmentation accuracy in PET. Taking into account that image restoration and tumor segmentation are tightly coupled and can promote each other, we proposed a variational method to solve both problems simultaneously in this study. The proposed method integrated total variation (TV) semi-blind de-convolution and Mumford-Shah segmentation with multiple regularizations. Unlike many existing energy minimization methods using either TV or L 2 regularization, the proposed method employed TV regularization over tumor edges to preserve edge information, and L 2 regularization inside tumor regions to preserve the smooth change of the metabolic uptake in a PET image. The blur kernel was modeled as anisotropic Gaussian to address the resolution difference in transverse and axial directions commonly seen in a clinic PET scanner. The energy functional was rephrased using the Γ -convergence approximation and was iteratively optimized using the alternating minimization (AM) algorithm. The performance of the proposed method was validated on a physical phantom and two clinic datasets with non-Hodgkin's lymphoma and esophageal cancer, respectively. Experimental results demonstrated that the proposed method had high performance for simultaneous image restoration, tumor segmentation and scanner blur kernel estimation. Particularly, the recovery coefficients (RC) of the restored images of the proposed method in the phantom study were close to 1, indicating an efficient recovery of the original blurred images; for segmentation the proposed method achieved average dice similarity indexes (DSIs) of 0.79 and 0.80 for two clinic datasets, respectively; and the relative errors of the estimated blur kernel widths were less than 19% in the transversal direction and 7% in the

  11. A Learning-Based Wrapper Method to Correct Systematic Errors in Automatic Image Segmentation: Consistently Improved Performance in Hippocampus, Cortex and Brain Segmentation

    Science.gov (United States)

    Wang, Hongzhi; Das, Sandhitsu R.; Suh, Jung Wook; Altinay, Murat; Pluta, John; Craige, Caryne; Avants, Brian; Yushkevich, Paul A.

    2011-01-01

    We propose a simple but generally applicable approach to improving the accuracy of automatic image segmentation algorithms relative to manual segmentations. The approach is based on the hypothesis that a large fraction of the errors produced by automatic segmentation are systematic, i.e., occur consistently from subject to subject, and serves as a wrapper method around a given host segmentation method. The wrapper method attempts to learn the intensity, spatial and contextual patterns associated with systematic segmentation errors produced by the host method on training data for which manual segmentations are available. The method then attempts to correct such errors in segmentations produced by the host method on new images. One practical use of the proposed wrapper method is to adapt existing segmentation tools, without explicit modification, to imaging data and segmentation protocols that are different from those on which the tools were trained and tuned. An open-source implementation of the proposed wrapper method is provided, and can be applied to a wide range of image segmentation problems. The wrapper method is evaluated with four host brain MRI segmentation methods: hippocampus segmentation using FreeSurfer (Fischl et al., 2002); hippocampus segmentation using multi-atlas label fusion (Artaechevarria et al., 2009); brain extraction using BET (Smith, 2002); and brain tissue segmentation using FAST (Zhang et al., 2001). The wrapper method generates 72%, 14%, 29% and 21% fewer erroneously segmented voxels than the respective host segmentation methods. In the hippocampus segmentation experiment with multi-atlas label fusion as the host method, the average Dice overlap between reference segmentations and segmentations produced by the wrapper method is 0.908 for normal controls and 0.893 for patients with mild cognitive impairment. Average Dice overlaps of 0.964, 0.905 and 0.951 are obtained for brain extraction, white matter segmentation and gray matter

  12. Differential diagnosis of the epileptogenic supratentorial brain tumors in children

    Directory of Open Access Journals (Sweden)

    V. S. Khalilov

    2015-01-01

    Full Text Available Fifty-six out of 79 pediatric patients with supratentorial brain tumors were noted to have symptomatic epilepsy. Dysembryoplastic neuroepithelial tumors (DNET, diffuse astrocytomas (DA, and gangliogliomas (GG were the most epileptogenic tumors. Seizures were new-onset in all our noted cases of DNET and in 4 patients with GG and the only clinical tumor sign in 6 of 8 cases of DNET. The neuroimaging features of the MRI pattern of DNET, DA, and GG were an iso/hypointense signal on Tl-weighted magnetic resonance images and a signal, the intensity of which varied from heterogeneous to cerebrospinal fluid, on T2-weighted FLAIR images. Cases of DNET and GG displayed no mass effect or perifocal edema, a trend towards location in the temporoinsular regions, and a frequent concurrence with local gray-white matter differentiation disorders and atrophy. The FLAIR images clearly showed the so-called foam-like (multicystic structure with pericystic changes. No significant change in the dimensions of the identified DNET and GG was observed during the follow up period. In low-grade DA, tumor growth was reduced and it is difficult to differentiate minimal perifocal edema from tumor-like tissue. The sensitivity of these tumors to contrast enhancement is ambiguous. Along with DNET (that was epileptogenic in 100% of cases, DA (91,7% and GG (80% were the most common epileptogenic brain tumors.

  13. The impact of dietary isoflavonoids on malignant brain tumors

    International Nuclear Information System (INIS)

    Sehm, Tina; Fan, Zheng; Weiss, Ruth; Schwarz, Marc; Engelhorn, Tobias; Hore, Nirjhar; Doerfler, Arnd; Buchfelder, Michael; Eyüpoglu, IIker Y; Savaskan, Nic E

    2014-01-01

    Poor prognosis and limited therapeutic options render malignant brain tumors one of the most devastating diseases in clinical medicine. Current treatment strategies attempt to expand the therapeutic repertoire through the use of multimodal treatment regimens. It is here that dietary fibers have been recently recognized as a supportive natural therapy in augmenting the body's response to tumor growth. Here, we investigated the impact of isoflavonoids on primary brain tumor cells. First, we treated glioma cell lines and primary astrocytes with various isoflavonoids and phytoestrogens. Cell viability in a dose-dependent manner was measured for biochanin A (BCA), genistein (GST), and secoisolariciresinol diglucoside (SDG). Dose–response action for the different isoflavonoids showed that BCA is highly effective on glioma cells and nontoxic for normal differentiated brain tissues. We further investigated BCA in ex vivo and in vivo experimentations. Organotypic brain slice cultures were performed and treated with BCA. For in vivo experiments, BCA was intraperitoneal injected in tumor-implanted Fisher rats. Tumor size and edema were measured and quantified by magnetic resonance imaging (MRI) scans. In vascular organotypic glioma brain slice cultures (VOGIM) we found that BCA operates antiangiogenic and neuroprotective. In vivo MRI scans demonstrated that administered BCA as a monotherapy was effective in reducing significantly tumor-induced brain edema and showed a trend for prolonged survival. Our results revealed that dietary isoflavonoids, in particular BCA, execute toxicity toward glioma cells, antiangiogenic, and coevally neuroprotective properties, and therefore augment the range of state-of-the-art multimodal treatment approach

  14. Quality of radiomic features in glioblastoma multiforme: Impact of semi-automated tumor segmentation software

    Energy Technology Data Exchange (ETDEWEB)

    Lee, Myung Eun; Kim, Jong Hyo [Center for Medical-IT Convergence Technology Research, Advanced Institutes of Convergence Technology, Seoul National University, Suwon (Korea, Republic of); Woo, Bo Yeong [Dept. of Transdisciplinary Studies, Graduate School of Convergence Science and Technology, Seoul National University, Suwon (Korea, Republic of); Ko, Micheal D.; Jamshidi, Neema [Dept. of Radiological Sciences, University of California, Los Angeles, Los Angeles (United States)

    2017-06-15

    The purpose of this study was to evaluate the reliability and quality of radiomic features in glioblastoma multiforme (GBM) derived from tumor volumes obtained with semi-automated tumor segmentation software. MR images of 45 GBM patients (29 males, 16 females) were downloaded from The Cancer Imaging Archive, in which post-contrast T1-weighted imaging and fluid-attenuated inversion recovery MR sequences were used. Two raters independently segmented the tumors using two semi-automated segmentation tools (TumorPrism3D and 3D Slicer). Regions of interest corresponding to contrast-enhancing lesion, necrotic portions, and non-enhancing T2 high signal intensity component were segmented for each tumor. A total of 180 imaging features were extracted, and their quality was evaluated in terms of stability, normalized dynamic range (NDR), and redundancy, using intra-class correlation coefficients, cluster consensus, and Rand Statistic. Our study results showed that most of the radiomic features in GBM were highly stable. Over 90% of 180 features showed good stability (intra-class correlation coefficient [ICC] ≥ 0.8), whereas only 7 features were of poor stability (ICC < 0.5). Most first order statistics and morphometric features showed moderate-to-high NDR (4 > NDR ≥1), while above 35% of the texture features showed poor NDR (< 1). Features were shown to cluster into only 5 groups, indicating that they were highly redundant. The use of semi-automated software tools provided sufficiently reliable tumor segmentation and feature stability; thus helping to overcome the inherent inter-rater and intra-rater variability of user intervention. However, certain aspects of feature quality, including NDR and redundancy, need to be assessed for determination of representative signature features before further development of radiomics.

  15. Aerobic Glycolysis as a Marker of Tumor Aggressiveness: Preliminary Data in High Grade Human Brain Tumors

    Directory of Open Access Journals (Sweden)

    Andrei G. Vlassenko

    2015-01-01

    Full Text Available Objectives. Glucose metabolism outside of oxidative phosphorylation, or aerobic glycolysis (AG, is a hallmark of active cancer cells that is not directly measured with standard 18F-fluorodeoxyglucose (FDG positron emission tomography (PET. In this study, we characterized tumor regions with elevated AG defined based on PET measurements of glucose and oxygen metabolism. Methods. Fourteen individuals with high-grade brain tumors underwent structural MR scans and PET measurements of cerebral blood flow (CBF, oxygen (CMRO2 and glucose (CMRGlu metabolism, and AG, using 15O-labeled CO, O2 and H2O, and FDG, and were compared to a normative cohort of 20 age-matched individuals. Results. Elevated AG was observed in most high-grade brain tumors and it was associated with decreased CMRO2 and CBF, but not with significant changes in CMRGlu. Elevated AG was a dramatic and early sign of tumor growth associated with decreased survival. AG changes associated with tumor growth were differentiated from the effects of nonneoplastic processes such as epileptic seizures. Conclusions. Our findings demonstrate that high-grade brain tumors exhibit elevated AG as a marker of tumor growth and aggressiveness. AG may detect areas of active tumor growth that are not evident on conventional FDG PET.

  16. [Tumor-segmental resection of hand-foot-giant cell tumor of bone and autologous iliac bone graft reconstruction].

    Science.gov (United States)

    Ge, Jianhua; Chen, Ge; Zhang, Zhongjie; Wan, Yongxian; Lu, Xiaobo

    2010-08-01

    To evaluate the effectiveness of tumor-segmental resection and autologous iliac bone graft reconstruction combined with internal fixation in treating hand-foot-giant cell tumor of bone. Between August 1997 and April 2008, 8 cases of hand-foot-giant cell tumor of bone were treated, including 3 males and 5 females with an average age of 28.5 years (range, 16-42 years). The locations were metacarpal bones in 3 cases, metatarsal bones in 4 cases, and phalanges of toes in 1 case. According to Campanacci's gradation of X-ray films, there were 1 case of grade I and 7 cases of grade II; according to pathological examination before operation, there were 3 cases of grade I to II, 4 cases of grade II, and 1 case of grade II to III; and according to TNM staging, there were 1 case of TisN0M0, 4 cases of T1N0M0, and 3 cases of T2N0M0. There were 2 cases of recurrence, the time from the first operation to recurrence were 11 and 14 months, respectively. The tumor size was 1.8 cm x 1.0 cm to 6.0 cm x 2.0 cm, the cortical bone became thinner, and the boundary between tumor and periosteum was clear. All patients underwent tumor-segmental resection combined with autologous iliac bone graft reconstruction, and miniplate internal fixation by lumbar anesthesia or trachea cannula anesthesia. All incision healed by first intention. Eight patients were followed up 10 to 84 months with an average of 46 months. Radiographs showed that fracture union was achieved at 3 to 9 months (mean, 5 months). No significant rotation, angular, and shortening deformity occurred in iliac bone graft. The function of iliac bone donor site recovered excellently. The pathological examination showed giant cell tumor of bone in all cases, including 2 case of grade I-II, 5 cases of grade II, and 1 case of grade II-III. The hand or foot function recovered excellently. No tumor recurrence or lung metastasis occurred during follow-up. Tumor-segmental resection combined with autologous iliac bone graft reconstruction

  17. Brain MRI tumor image fusion combined with Shearlet and wavelet

    Science.gov (United States)

    Zhang, Changjiang; Fang, Mingchao

    2017-11-01

    In order to extract the effective information in different modalities of the tumor region in brain Magnetic resonance imaging (MRI) images, we propose a brain MRI tumor image fusion method combined with Shearlet and wavelet transform. First, the source images are transformed into Shearlet domain and wavelet domain. Second, the low frequency component of Shearlet domain is fused by Laplace pyramid decomposition. Then the low-frequency fusion image is obtained through inverse Shearlet transform. Third, the high frequency subimages in wavelet domain are fused. Then the high-frequency fusion image is obtained through inverse wavelet transform. Finally, the low-frequency fusion image and high-frequency fusion image are summated to get the final fusion image. Through experiments conducted on 10 brain MRI tumor images, the result shown that the proposed fusion algorithm has the best fusion effect in the evaluation indexes of spatial frequency, edge strength and average gradient. The main spatial frequency of 10 images is 29.22, and the mean edge strength and average gradient is 103.77 and 10.42. Compared with different fusion methods, we find that the proposed method effectively fuses the information of multimodal brain MRI tumor images and improves the clarity of the tumor area well.

  18. Episodic Memory Impairments in Primary Brain Tumor Patients.

    Science.gov (United States)

    Durand, Thomas; Berzero, Giulia; Bompaire, Flavie; Hoffmann, Sabine; Léger, Isabelle; Jego, Virginie; Baruteau, Marie; Delgadillo, Daniel; Taillia, Hervé; Psimaras, Dimitri; Ricard, Damien

    2018-01-04

    Cognitive investigations in brain tumor patients have mostly explored episodic memory without differentiating between encoding, storage, and retrieval deficits. The aim of this study is to offer insight into the memory sub-processes affected in primary brain tumor patients and propose an appropriate assessment method. We retrospectively reviewed the clinical and memory assessments of 158 patients with primary brain tumors who had presented to our departments with cognitive complaints and were investigated using the Free and Cued Selective Reminding Test. Retrieval was the process of episodic memory most frequently affected, with deficits in this domain detected in 92% of patients with episodic memory impairments. Storage and encoding deficits were less prevalent, with impairments, respectively, detected in 41% and 23% of memory-impaired patients. The pattern of episodic memory impairment was similar across different tumor histologies and treatment modalities. Although all processes of episodic memory were found to be impaired, retrieval was by far the most widely affected function. A thorough assessment of all three components of episodic memory should be part of the regular neuropsychological evaluation in patients with primary brain tumors.

  19. Training stem cells for treatment of malignant brain tumors

    Science.gov (United States)

    Li, Shengwen Calvin; Kabeer, Mustafa H; Vu, Long T; Keschrumrus, Vic; Yin, Hong Zhen; Dethlefs, Brent A; Zhong, Jiang F; Weiss, John H; Loudon, William G

    2014-01-01

    The treatment of malignant brain tumors remains a challenge. Stem cell technology has been applied in the treatment of brain tumors largely because of the ability of some stem cells to infiltrate into regions within the brain where tumor cells migrate as shown in preclinical studies. However, not all of these efforts can translate in the effective treatment that improves the quality of life for patients. Here, we perform a literature review to identify the problems in the field. Given the lack of efficacy of most stem cell-based agents used in the treatment of malignant brain tumors, we found that stem cell distribution (i.e., only a fraction of stem cells applied capable of targeting tumors) are among the limiting factors. We provide guidelines for potential improvements in stem cell distribution. Specifically, we use an engineered tissue graft platform that replicates the in vivo microenvironment, and provide our data to validate that this culture platform is viable for producing stem cells that have better stem cell distribution than with the Petri dish culture system. PMID:25258664

  20. Gonadal status in male survivors following childhood brain tumors

    DEFF Research Database (Denmark)

    Schmiegelow, M; Lassen, S; Poulsen, H S

    2001-01-01

    The effect of radiotherapy (RT) and chemotherapy (CT) on gonadal function was assessed in males treated for a childhood brain tumor not directly involving the hypothalamus/pituitary (HP) axis in a population-based study with a long follow-up time. All males......The effect of radiotherapy (RT) and chemotherapy (CT) on gonadal function was assessed in males treated for a childhood brain tumor not directly involving the hypothalamus/pituitary (HP) axis in a population-based study with a long follow-up time. All males...

  1. Boron neutron capture therapy: Brain Tumor Treatment Evaluation Program

    International Nuclear Information System (INIS)

    Griebenow, M.L.; Dorn, R.V. III; Gavin, P.R.; Spickard, J.H.

    1988-01-01

    The United States (US) Department of Energy (DOE) recently initiated a focused, multidisciplined program to evaluate Boron Neutron Capture Therapy (BNCT) for the treatment of brain tumors. The program, centered at the DOE/endash/Idaho National Engineering Laboratory (INEL), will develop the analytical, diagnostic and treatment tools, and the database required for BNCT technical assessment. The integrated technology will be evaluated in a spontaneously-occurring canine brain-tumor model. Successful animal studies are expected to lead to human clinical trials within four to five years. 2 refs., 3 figs

  2. Boron neutron capture therapy for children with malignant brain tumor

    International Nuclear Information System (INIS)

    Nakagawa, Yoshinobu; Komatsu, Hisao; Kageji, Teruyoshi; Tsuji, Fumio; Matsumoto, Keizo; Kitamura, Katsuji; Hatanaka, Hiroshi; Minobe, Takashi.

    1993-01-01

    Among the 131 cases with brain tumors treated by boron-neutron capture therapy (BNCT), seventeen were children. Eight supratentorial tumors included five astrocytomas(grade 2-4), two primitive neuroectodermal tumors (PNET) and one rhabdomyosarcoma. Seven pontine tumors included one astrocytoma, one PNET and 5 unverified gliomas. Two cerebellar tumors (PNET and astrocytoma) were also treated. All pontine tumors showed remarkable decrease in size after BNCT. However, most of them showed regrowth of the tumors because the neutrons were insufficient due to the depth. Four cases with cerebral tumor died of remote cell dissemination, although they all responded to BNCT. One of them survived 7 years after repeated BNCTs. An 11 years old girl with a large astrocytoma in the right frontal lobe has lived more than 11 years and is now a draftswoman at a civil engineering company after graduating from a technical college. An 8 years old girl with an astrocytoma in the left occipital lobe has no recurrence of the tumor for 2 years and attends on elementary school without mental and physical problems. Two children (one year old girl and four years old boy) with cerebellar tumors have shown showed an excellent growth after BNCT and had no neurological deficits. Mental and physical development in patients treated by BNCT is usually better than that in patients treated by conventional radiotherapy. (author)

  3. Clinical features of depressive disorders in patients with brain tumors

    Directory of Open Access Journals (Sweden)

    Ogorenko V.V.

    2014-03-01

    Full Text Available The aim of the study was to examine the structure of psychopathology and clinical features of depressive disorders in patients with brain oncopathology. Polymorphic mental disorders of various clinical content and severity in most cases not only are comorbid to oncological pathology of the brain, but most often are the first clinical signs of early tumors. The study was conducted using the following methods: clinical psychiatric, questionnaire Simptom Check List- 90 -Revised-SCL- 90 -R, Luscher test and mathematical processing methods. Sample included 175 patients with brain tumors with non-psychotic level of mental disorders. The peculiarities of mental disorders and psychopathological structure of nonpsychotic depressive disorders have been a clinical option of cancer debut in patients with brain tumors. We found that nonpsychotic depression is characterized by polymorphism and syndromal incompletion; this causes ambiguity of diagnoses interpretation on stages of diagnostic period. Features of depressive symptoms depending on the signs of malignancy / nonmalignancy of brain tumor were defined.

  4. Connectivity derived thalamic segmentation in deep brain stimulation for tremor.

    Science.gov (United States)

    Akram, Harith; Dayal, Viswas; Mahlknecht, Philipp; Georgiev, Dejan; Hyam, Jonathan; Foltynie, Thomas; Limousin, Patricia; De Vita, Enrico; Jahanshahi, Marjan; Ashburner, John; Behrens, Tim; Hariz, Marwan; Zrinzo, Ludvic

    2018-01-01

    The ventral intermediate nucleus (VIM) of the thalamus is an established surgical target for stereotactic ablation and deep brain stimulation (DBS) in the treatment of tremor in Parkinson's disease (PD) and essential tremor (ET). It is centrally placed on a cerebello-thalamo-cortical network connecting the primary motor cortex, to the dentate nucleus of the contralateral cerebellum through the dentato-rubro-thalamic tract (DRT). The VIM is not readily visible on conventional MR imaging, so identifying the surgical target traditionally involved indirect targeting that relies on atlas-defined coordinates. Unfortunately, this approach does not fully account for individual variability and requires surgery to be performed with the patient awake to allow for intraoperative targeting confirmation. The aim of this study is to identify the VIM and the DRT using probabilistic tractography in patients that will undergo thalamic DBS for tremor. Four male patients with tremor dominant PD and five patients (three female) with ET underwent high angular resolution diffusion imaging (HARDI) (128 diffusion directions, 1.5 mm isotropic voxels and b value = 1500) preoperatively. Patients received VIM-DBS using an MR image guided and MR image verified approach with indirect targeting. Postoperatively, using parallel Graphical Processing Unit (GPU) processing, thalamic areas with the highest diffusion connectivity to the primary motor area (M1), supplementary motor area (SMA), primary sensory area (S1) and contralateral dentate nucleus were identified. Additionally, volume of tissue activation (VTA) corresponding to active DBS contacts were modelled. Response to treatment was defined as 40% reduction in the total Fahn-Tolosa-Martin Tremor Rating Score (FTMTRS) with DBS-ON, one year from surgery. Three out of nine patients had a suboptimal, long-term response to treatment. The segmented thalamic areas corresponded well to anatomically known counterparts in the ventrolateral (VL

  5. BEaST: brain extraction based on nonlocal segmentation technique

    NARCIS (Netherlands)

    Eskildsen, Simon F.; Coupé, Pierrick; Fonov, Vladimir; Manjón, José V.; Leung, Kelvin K.; Guizard, Nicolas; Wassef, Shafik N.; Østergaard, Lasse Riis; Collins, D. Louis; Saradha, A.; Abdi, Hervé; Abdulkadir, Ahmed; Acharya, Deepa; Achuthan, Anusha; Adluru, Nagesh; Aghajanian, Jania; Agrusti, Antonella; Agyemang, Alex; Ahdidan, Jamila; Ahmad, Duaa; Ahmed, Shiek; Aisen, Paul; Akhondi-Asl, Alireza; Aksu, Yaman; Alberca, Roman; Alcauter, Sarael; Alexander, Daniel; Alin, Aylin; Almeida, Fabio; Alvarez-Linera, Juan; Amlien, Inge; Anand, Shyam; Anderson, Dallas; Ang, Amma; Angersbach, Steve; Ansarian, Reza; Aoyama, Eiji; Appannah, Arti; Arfanakis, Konstantinos; Armor, Tom; Arrighi, Michael; Arumughababu, S. Vethanayaki; Arunagiri, Vidhya; Ashe-McNalley, Cody; Ashford, Wes; Le Page, Aurelie; Avants, Brian; Aviv, Richard; Awasthi, Sukrati; Ayache, Nicholas; Ayan-Oshodi, Mosun; Ayhan, Murat; Sumana, B. V.; Babic, Tomislav; Baek, Young; Bagepally, Bhavani; Baird, Geoffrey; Baker, John; Baker, Suzanne; Bakker, Arnold; Barbash, Shahar; Bard, Jonathan; Barker, Warren; Bartlett, Jonathan; Baruchin, Andrea; Battaglini, Iacopo; Bauer, Corinna; Bayley, Peter; Beck, Irene; Becker, James; Becker, J. Alex; Beckett, Laurel; Bednar, Martin; Bedner, Arkadiusz; Beg, Mirza Faisal; Bekris, Lynn; Belaroussi, Boubakeur; Belloch, Vicente; Belmokhtar, Nabil; Ben Ahmed, Olfa; Bender, J. Dennis; Benois-Pineau, Jenny; Bhaskar, Uday; Bienkowska, Katarzyna; Biffi, Alessandro; Bigler, Erin; Bilgic, Basar; Bishop, Courtney; Bittner, Daniel; Black, Sandra; Bloss, Cinnamon; Bocti, Christian; Bohorquez, Adriana; Bokde, Arun; Boone, John; Boppana, Madhu; Borrie, Michael; Bourgeat, Pierrick; Bouttout, Haroune; Bowes, Mike; Bowman, DuBois; Bowman, Gene; Bracard, Serge; Braskie, Meredith; Braunewell, Karl; Breitner, Joihn; Bresell, Anders; Brewer, James; Brickhouse, Michael; Brickman, Adam; Britschgi, Markus; Broadbent, Steve; Brogren, Jacob; Brunton, Simon; Buchsbaum, Monte; Buckley, Chris; Buerger, Katharina; Bunce, David; Burnham, Samantha; Burns, Jeffrey; Burton, David; Burzykowski, Tomasz; Butler, Tracy; Cabeza, Rafael; Caffery, Terrell; Cairns, Nigel; Callhoff, Johanna; Calvini, Piero; Carbotti, Angela; Carle, Adam; Carmasin, Jeremy; Carmichael, Owen; Carvalho, Janessa; Casabianca, Jodi; Casanova, Ramon; Casey, Anne; Cash, David; Cataldo, Rosella; Cedarbaum, Jesse; Cella, Massimo; Celsis, Pierre; Chakravarty, Mallar; Chang, Ih; Chao, Linda; Charil, Arnaud; Chang, Che-Wei; Chemali, Zeina; Chen, Kewei; Chen, Shuzhong; Chen, Rong; Chen, Qiang; Chen, Jung-Tai; Chen, Gang; Chen, Jake; Chen, Wei; Cheng, Wei-Chen; Cheng, Xi; Cherkas, Yauheniya; Chertkow, Howard; Cheung, Vinci; Cheung, Charlton; Chiang, Gloria; Chiao, Ping; chibane, Mouatez Billah; Chida, Noriko; Chin, Simon; Ching, Christopher; Chisholm, Jane; Cho, Claire; Cho, Youngsang; Choe, John; Choubey, Suresh; Chowbina, Sudhir; Christensen, Anette Luther; Ciocia, Gianluigi; Clark, David; Clark, Chris; Clarkson, Matt; Clerc, Stephanie; Clunie, David; Coen, Michael; Coimbra, Alexandre; Compton, David; Coppola, Giovanni; Coubard, Olivier; Coulin, Samuel; Cover, Keith S.; Crane, Paul; Crans, Gerald; Croop, Robert; Crowther, Daniel; Crum, William; Cui, Yue; Curry, Charles; Cutter, Gary; Da, Long; Daliri, Mohammad Reza; Damato, Vito Domenico; Darby, Eveleen; Darkner, Sune; Davatzikos, Christos; DavidPrakash, Bhaskaran; Davidson, Christopher; Davis, Melissa; de Bruijne, Marleen; de Meyer, Geert; de Nunzio, Giorgio; Decarli, Charles; Dechairo, Bryan; DeDuck, Kristina; Dehghan, Hossein; Delfino, Manuel; Della Rosa, Pasquale Anthony; Dellavedova, Luca; Delpassand, Ebrahim; Delrieu, Julien; DeOrchis, Vincent; Dépy Carron, Delphine; Desjardins, Benoit; deToledo-Morrell, Leyla; Devanand, Davangere; Devanarayan, Viswanath; Devier, Deidre; DeVous, Michael; Dgetluck, Nancy; Di, Jianing; Di, Xin; Diaz-Arrastia, Ramon; Dickerson, Bradford; Dickie, David Alexander; Dill, Vanderson; Ding, Xiaobo; Dinov, Ivo; Dobosh, Brian; Dobson, Howard; Dodge, Hiroko; Dolman, Andrew; Dolmo, Bess-Carolina; Donohue, Michael; Dore, Vincent; Dorflinger, Ernest; Dowling, Maritza; Dragicevic, Natasa; Dubal, Dena; Duchesne, Simon; Duff, Kevin; Dukart, Jürgen; Durazzo, Timothy; Dutta, Joyita; DWors, Robert; Earl, Nancy; Edula, Goutham; Elcoroaristizabal, Xabier; Emahazion, Tesfai; Endres, Christopher; Epstein, Noam; Ereshefsky, Larry; Eskildsen, Simon; Espinosa, Ana; Esposito, Mario; Ewers, Michael; Falcone, Guido; Fan, Yong; Fan, Jing; Fan, Lingzhong; Farahibozorg, Seyedehrezvan; Farb, Norman; Fardo, David; Farias, Sarah; Farnum, Michael; Farrer, Lindsay; Fatke, Bastian; Faux, Noel; Feldman, Howard; Feldman, Susan; Feldman, Betsy; Félix, Zandra; Fennema-Notestine, Christine; Fernandes, Michel; Fernandez, Elsa; Ferreira, Manuel Joao; Ferrer, Eugene; Fetterman, Bob; Figurski, Michal; Fillit, Howard; Finch, Stephen; Fiot, Jean-Baptiste; Flenniken, Derek; Fletcher, Evan; Flores, Christopher; Longmire, Crystal Flynn; Focke, Niels; Forman, Mark; Forsythe, Alan; Fox, Steven; Fox-Bosetti, Sabrina; Foxhall, Suzanne; Franko, Edit; Freeman, Roderick; Friedrich, Christoph M.; Friesenhahn, Michel; Frisoni, Giovanni; Fritzsche, Klaus; Fujimoto, Yoko; Fujiwara, Ken; Fullerton, Terence; Gaffour, Yacine; Galvin, Ben; Gamst, Anthony; Gao, Sujuan; Garg, Gaurav; Gaser, Christian; Gastineau, Edward; Gattaz, Wagner; Gaubert, Malo; Gauthier, Serge; Gavett, Brandon; Ge, Tian; Gemme, Gianluca; Geraci, Joseph; Gholipour, Farhad; Ghosh, Debashis; Ghosh, Satrajit; Gieschke, Ronald; Gill, Ryan; Gillespie, William; Gitelman, Darren; Gkontra, Xenia; Gleason, Carey; Glymour, M. Maria; Godbey, Michael; Gold, Brian; Goldberg, Terry; Goldman, Jennifer; Gonzalez-Beltran, Alejandra; Goodro, Robert; Gore, Chris; Gorriz, Juan Manuel; Goto, Masami; Grachev, Igor; Gradkowski, Wojciech; Grandey, Emily; Grasela, Thaddeus; Gray, Katherine; Greenberg, Barry; Greicius, Michael; Grill, Joshua; Gross, Alden; Gross, Alan; Grydeland, Håkon; Guignot, Isabelle; Guo, Qimiao; Guo, Linag-Hao; Guo, Hongbin; Gupta, Vinay; Guyot, Jennifer; Habeck, Christian; Habte, Frezghi; Haight, Thaddeus; Hajaj, Chen; Hajiesmaeili, Maryam; Hajjar, Ihab; Hammarstrom, Per; Hampel, Harald; Han, Duke; Han, Jian; Han, Zhaoying; Hanna, Yousef; Hao, Yongfu; Hardy, Peter; Harvey, Danielle; Hasan, Md Kamrul; Hayashi, Toshihiro; Haynes, John-Dylan; He, Huiguang; He, Yong; Head, Denise; Heckemann, Rolf; Heegaard, Niels; Heidebrink, Judith; Hellyer, Peter; Helwig, Michael; Henderson, David; Herholz, Karl; Herskovits, A. Zara; Hess, Christopher; Hildenbrand, Maike; Ming, Au Yeung Ho; Hobart, Jeremy; Hochstetler, Helen; Hofer, Scott; Hoffman, John; Holder, Daniel; Hollingworth, Paul; Holmes, Robin; Hong, Quan; Honigberg, Lee; Hope, Thomas; Hoppin, Jack; Hot, Pascal; Hou, Yangyang; Hsieh, Helen; Hsu, Ailing; Hu, Xiaochen; Hu, Mingxing; Hu, William; Hua, Wen-Yu; Huang, Shuai; Huang, Fude; Huang, Zihan; Huang, Chun-Jung; Huang, Chien-Chih; Huang, Juebin; Hubbard, Rebecca; Huentelman, Matthew; Huppertz, Hans-Jürgen; Hurko, Orest; Hurt, Stephen; Hutchins, Jim; Hwang, Scott; Hyun, JungMoon; Ifeachor, Emmanuel; Iglesias, Martina; Ikari, Yasuhiko; Ikonomidou, Vasiliki; Iman, Adjoudj; Imani, Farzin; Immermann, Fred; Inlow, Mark; Inoue, Lurdes; Insel, Philip; Irizarry, Michael; Ishibashi, Taro; Ishii, Kenji; Ismail, Sara; Ito, Kaori; Iturria-Medina, Yasser; Iwatsubo, Takeshi; Jacks, Adam; Jacobson, Mark; Jacqmin, Philippe; Jaffe, Carl; Jagust, William; Janousova, Eva; Jara, Hernan; Jasperse, Bas; Jedynak, Bruno; Jefferson, Angela; Jennings, J. Richard; Jenq, John; Jessen, Walter; Jia, Fucang; Jiang, Tianzi; Jiao, Yun; Jing, Huang; Johnson, Kent; Johnson, Sterling; Johnson, David K.; Johnson, Julene; Jones, Gareth; Jones, Mark; Jones, Richard; Joshi, Shantanu; Jouvent, Eric; Juengling, Freimut; Julin, Per; Junjie, Zhuo; Kabilan, Meena; Kadish, Bill; Kairui, Zhang; Kam, Hye Jin; Kamboh, M. Ilyas; Kamer, Angela; Kanakaraj, Sithara; Kanchev, Vladimir; Kaneko, Tomoki; Kaneta, Tomohiro; Kang, Hyunseok; Kang, Ju Hee; Kang, Jian; Karageorgiou, Elissaios; Karantzoulis, Stella; Karlawish, Jason; Katz, Elyse; Kaushik, Sandeep S.; Kauwe, John; Kawakami, Hirofumi; Kawashima, Shoji; Kaye, Edward; Kazemi, Samaneh; Ke, Han; Kelleher, Thomas; Kennedy, Richard; Keogh, Bart; Kerchner, Geoffrey; Kerr, Daniel; Keshava, Nirmal; Khalil, Iya; Khalil, Andre; Khondker, Zakaria; Kihara, Takeshi; Killeen, Neil; Killiany, Ron; Kim, Dajung; Kim, Hyoungkyu; Kim, Seongkyun; Kim, Jong Hun; Kim, Ana; Kim, Jung-Hyun; Kimberg, Daniel; Kimura, Tokunori; King, Richard; Kirby, Justin; Kirsch, Wolff; Klimas, Michael; Kline, Richard; Kling, Mitchel; Klopfenstein, Erin; Koen, Joshua; Koikkalainen, Juha; Kokomoor, Anders; Kong, Xiangnan; Koppel, Jeremy; Korolev, Igor; Kotran, Nickolas; Kowalczyk, Adam; Krahnke, Tillmann; Krams, Michael; Kuceyeski, Amy; Kuhl, Donald; Kumar, Vinod; Roy, P. Kumar; Kuo, Julie; Labrish, Catherine; Lai, Song; Lakatos, Anita; Lalonde, François; Lam, On Ki; Lampron, Antoine; Landau, Susan; Lane, Richard; Lane, Barton; Langbaum, Jessica; Langford, Dianne; Lanius, Vivian; Latella, Marco; Leahy, Richard; an Lee, Jong; Lee, Dongsoo; Lee, Noah; Lee, Sei; Lee, Doheon; Lee, Grace; Lefkimmiatis, Stamatis; Lemaitre, Herve; Lenfant, Pierre; Lenz, Robert; Leong, Josiah; Leoutsakos, Jeannie-Marie; Leung, Yuk Yee; Levey, Allan; Li, Rui; Li, Xiaodong; Li, Weidong; Li, Xiaobo; Li, Ming; Li, Lexin; Li, Jun; Li, Gang; Li, Quanzheng; Li, Yi; Li, Junning; Li, Jie; Li, Yue; Li, Shanshan; Liang, Kelvin; Liang, Kuchang; Liang, Peipeng; Liang, Lichen; Liao, Weiqi; Liaquat, Saad; Liberman, Gilad; Lin, Lan; Lin, Ai-Ling; Lin, Frank; Liu, Tao; Liu, Dazhong; Liu, Li; Liu, Honggang; Liu, Sidong; Liu, Tianming; Liu, Xiuwen; Liu, Sophia; Liu, Linda; Liu, Wei; Liu, Guodong; Liu, Yanping; Liu, Collins; Lo, Raymond; Lobanov, Victor; Lockhart, Andrew; Loewenstein, David; Logovinsky, Veronika; Long, Miaomiao; Long, Ziyi; Long, Xiaojing; Looi, Jeffrey; Lu, Huanxiang; Lu, Po-Haong; Lucena, Nathaniel; Lukas, Carsten; Lukic, Ana; Luo, Lei; Luo, Xiongjian; Luo, Xi; Lynch, John; Ma, Shen-Ming; Mackin, Scott; Mada, Marius; Madabhushi, Anant; Maglio, Silvio; Mahanta, Mohammad Shahin; Maikusa, Norihide; Maldjian, Joseph; Mandal, Indrajit; Manjon, Jose; Mantri, Ninad; Manzour, Amir; Marchewka, Artur; Marcus, Daniel; Margolin, Richard; Marrett, Sean; Marshall, Gad; Gonzalez, Alberto Martinez; Torteya, Antonio Martinez; Mather, Mara; Mathis, Chester; Mattei, Peter; Matthews, Dawn; McArdle, John; McCarroll, Steven; McEvoy, Linda; McGeown, William; McGinnis, Scott; McGonigle, John; McIntyre, John; McLaren, Donald; McQuail, Joseph; Meadowcroft, Mark; Meda, Shashwath; Melie-Garcia, Lester; Melrose, Rebecca; Mendelson, Alexander; Mendez, Mario; Menendez, Enrique; Meng, Meng; Meredith, Jere; Metti, Andrea; Meyer, Carsten; Mez, Jesse; Mickael, Guedj; Miftahof, Roustem; Mikula, Margit; Miller, Michael; Millikin, Colleen; Nintun, Mark; Mirza, Mubeena; Mistridis, Panagiota; Mitchell, Meghan; Mitsis, Effie; Mon, Anderson; Moore, Dana; Morabito, Francesco C.; Birgani, Parmida Moradi; Moratal, David; Morimoto, Bruce; Mormino, Elizabeth; Morris, Jill; Mortamet, Bénédicte; Moscato, Pablo; Mueller, Kathyrne; Mueller, Susanne; Mukherjee, Shubhabrata; Mulder, Emma; Mungas, Dan; Munir, Kamran; Murayama, Shigeo; Murphy, Michael; Myers, Amanda; Sairam, N.; Nagata, Ken; Nair, Anil; Nativio, Raffaella; Nazarparvar, Babak; Nazeri, Arash; Nejad, Leila; Nekooei, Sirous; Nettiksimmons, Jasmine; Neu, Scott; Ng, Yen-Bee; Nguyen, Nghi; Nichols, Thomas; Nicodemus, Kristin; Niecko, Timothy; Nielsen, Casper; Nishio, Tomoyuki; Nordstrom, Matthew; Noshad, Sina; Notomi, Keiji; Novak, Nic; Nutakki, Gopi Chand; O'Bryant, Sid; Obisesan, Thomas; Oh, Joonmi; Okonkwo, Ozioma; Olde Rikkert, Marcel; Oliveira, Ailton; Oliveira, João; Oliver, Ruth; Olmos, Salvador; Oltra, Javier; Ortner, Marion; Osadebey, Michael; Ostrowitzki, Susanne; Overholser, Rosanna; Anishiya, P.; Chitra, P. K. A.; Pa, Judy; Palanisamy, Preethi; Pan, Sarah; Pan, Zhifang; Pande, Yogesh; Pardo, Jose; Pardoe, Heath; Park, Sang hyun; Park, Sujin; Park, Lovingly; Park, Hyunjin; Park, Moon Ho; Parker, Christopher; Patel, Yogen; Patil, Amol; Patil, Manasi; Pawlak, Mikolaj; Payoux, Pierre; Pearson, Jim; Pell, Gaby; Peng, Yahong; Pennec, Xavier; Pepin, Jean louis; Pereira, Francisco; Perneczky, Robert; Petitti, Diana; Petrella, Jeffrey; Peyrat, Jean-Marc; Ngoc, Phuong Trinh Pham; Phillips, Justin; Phillips, Nicole; Pian, Wen-ting; Pierson, Ronald; Piovezan, Mauro; Pipitone, Jon; Pirraglia, Elizabeth; Planes, Xavi; Podhorski, Adam; Pollari, Mika; Pomara, Nunzio; Pontecorvo, Michael; Popov, Veljko; Poppenk, Jordan; Posner, Holly; Potkin, Steven; Potter, Guy; Potter, Elizabeth; Poulin, Stephane; Prastawa, Marcel; Prince, Jerry; Priya, Anandh; Pruessner, Jens; Qiu, Wendy; Qu, Annie; Qualls, Constance Dean; Quarg, Peter; Quinlan, Judith; Rabbia, Michael; Rajagovindan, Rajasimhan; Rajeesh, Rajeesh; Rallabandi, V. P. Subramanyam; Ramadubramani, Vanamamalai; Ramage, Amy; Ramirez, Alfredo; Randolph, Christopher; Rao, Anil; Rao, Hengyi; Rao, Divya; Raubertas, Richard; Ray, Debashis; Razak, Hana; Reed, Bruce; Reid, Andrew; Reilhac, Anthonin; Reiner, Peggy; Reinsberger, Claus; Restrepo, Lucas; Retico, Alessandra; Rhatigan, Lewis; Rhinn, Herve; Rhoades, Earl; Ribbens, Annemie; Richard, Edo; Richards, John; Richter, Mirco; Riddle, William; Ridgway, Gerard; Ries, Michele; Ringman, John; Rischall, Matt; Rizk-Jackson, Angela; Rizzi, Massimo; Robieson, Weining; Rodriguez, Laura; Rodriguez-Vieitez, Elena; Rogalski, Emily; Rogers, Elizabeth; Balderrama, Javier Rojas; Rokicki, Jaroslav; Romero, Klaus; Rorden, Chris; Rosand, Jonathan; Rosen, Ori; Rosenberg, Paul; Roubini, Eli; Rousseau, François; Rowe, Christopher; Rubin, Daniel; Rubright, Jonathan; Rucinski, Marek; Ruiz, Agustin; Rulseh, Aaron; Rusinek, Henry; Ryan, Laurie; Saad, Ahmed; Sabuncu, Mert; Sahuquillo, Juan; Said, Yasmine; Saito, Naomi; Sakata, Muneyuki; Salama, Mahetab; Salazar, Diego; Salter, Hugh; Saman, Sudad; Sanchez, Luciano; Sanders, Elizabeth; Sankar, Tejas; Santhamma, Sindhumol; Sarnel, Haldun; Sasaki, Toshiaki; Sasaya, Tenta; Sato, Hajime; Sattlecker, Martina; Saumier, Daniel; Savio, Alexandre; Saykin, Andrew; Scanlon, Blake; Scharre, Douglas; Schegerin, Marc; Schmand, Ben; Schmansky, Nick; Schmidt-Wilcke, Tobias; Schramm, Hauke; Schuerch, Markus; Schwartz, Craig; Schwartz, Eben; Schwarz, Adam; Schwarz, John; Selnes, Per; Sembritzki, Klaus; Senjem, Matthew; Sevigny, Jeffrey; Sfikas, Giorgos; Sghedoni, Roberto; Shah, Said Khalid; Shahbaba, Babak; Shams, Soheil; Shankle, William; Shattuck, David; Shaw, Leslie; Sheela, Jaba; Shen, Jie; Shen, Qi; Shen, Weijia; Shen, Qian; Shera, David; Sherman, John; Sherva, Richard; Shi, Jie; Shokouhi, Sepideh; Shukla, Vinay; Shulman, Joshua; Sideris, Konstantinos; Siegel, Rene; Silveira, Margarida; Silverman, Daniel; Sim, Ida; Simak, Alex; Simmons, Andy; Simoes, Rita; Simon, Adam; Simon, Melvin; Simpson, Ivor; Singh, Nikhil; Singh, Simer Preet; Sinha, Neelam; Siuciak, Judy; Sjögren, Niclas; Skinner, Jeannine; Smith, Michael; Smith, Charles; Smyth, Timothy; Snow, Sarah; Snyder, Peter; Soares, Holly; Soldan, Anja; Soldea, Octavian; Solomon, Alan; Solomon, Paul; Som, Subhojit; Song, Zhuang; Song, Shide; Sosova, Iveta; Soydemir, Melih; Spampinato, Maria Vittoria; Speier, William; Sperling, Reisa; Spiegel, Renãâ; Spies, Lothar; Springate, Beth; Staff, Roger; Steffener, Jason; Stern, Yaakov; Stokman, Harro; Straw, Jack; Stricker, Nikki; Stühler, Elisabeth; Styren, Scot; Subramanian, Vijayalakshmi; Suen, Summit; Sugishita, Morihiro; Sukkar, Rafid; Sun, Ying; Sun, Jia; Sun, Yu; Sundell, Karen; Suzuki, Akiyuki; Svetnik, Vladimir; Swan, Melanie; Symons, Sean; Szigeti, Kinga; Szoeke, Cassandra; Sørensen, Lauge; Genish, T.; Takahasi, Tetsuhiko; Takeuchi, Tomoko; Tanaka, Rie; Tanchi, Chaturaphat; Tancredi, Daniel; Tang, Qi; Tarnow, Eugen; Tartaglia, Maria Carmela; Tarver, Erika; Tassy, Dominique; Tauber, Clovis; Taylor-Reinwald, Lisa; Teipel, Stefan; Teng, Edmond; Terriza, Felipe; Thambisetty, Madhav; Thames, April; Thatavarti, Raja Sekhar; Thiele, Frank; Thomas, Ronald; Thomas, Benjamin; Thomas, Charlene; Thompson, Wesley; Thompson, Paul; Thornton-Wells, Tricia; Thorvaldsson, Valgeir; Thurfjell, Lennart; Tokuda, Takahiko; Toledo, Juan B.; Tölli, Tuomas; Toma, Ahmed; Tomita, Naoki; Toro, Roberto; Torrealdea, Patxi; Tosto, Giuseppe; Tosun, Duygu; Tousian, Mona; Toussaint, Paule; Toyoshiba, Hiroyoshi; Tractenberg, Rochelle E.; Triggs, Tyler; Trittschuh, Emily; Trojanowski, John; Trotta, Gabriele; Huu, Tram Truong; Truran, Diana; Tsanas, Athanasios; Tsang, Candy; Tufail, Ahsan; Tung, Joyce; Turken, And; Ueda, Yoji; Uematsu, Daisuke; Ullrich, Lauren; Venkataraju, Kannan Umadevi; Umar, Nisser; Ungar, Leo; Uzunbas, Gokhan; van de Nes, Joseph; van der Brug, Marcel; van der Lijn, Fedde; van Hecke, Wim; van Horn, John; van Leemput, Koen; van Train, Kenneth; Varkuti, Balint; Vasanawala, Minal; Veeraraghavan, Harini; Vemuri, Prashanthi; Verma, Manish; Videbaek, Charlotte; Vidoni, Eric; Villanueva-Meyer, Javier; Vinyes, Georgina; Visser, Pieter Jelle; Vitek, Michael; Vogel, Simon; Voineskos, Aristotle; Vos, Stephanie; Vounou, Maria; Wade, Sara; Walsh, Alexander; Wan, Hong; Wang, Tianyao; Wang, Yongmei Michelle; Wang, Wei; Wang, Angela; Wang, Song; Wang, Lubin; Wang, Li; Wang, Yaping; Wang, Li-San; Wang, Lei; Wang, Alex; Wang, Yu; Wang, Xu; Wang, Ze; Wang, Tiger; Ward, Michael; Ward, Andrew; Watanabe, Toshiyuki; Watson, David; Webb, David; Wefel, Jeffrey; Weiner, Michael; Westlye, Lars T.; Wheland, David; Whitcher, Brandon; White, Brooke; Whitlow, Christopher; Wilhelmsen, Kirk; Wilmot, Beth; Wilson, Lorraine; Wimsatt, Matt; Wingo, Thomas; Wirth, Miranka; Wishart, Heather; Wiste, Heather; Wolf, Henrike; Wolke, Ira; Wolz, Robin; Wong, Koon; Woo, Jongwook; Woo, Ellen; Woods, Lynn; Worth, Andrew; Wu, Yanjun; Wu, Liang; Wu, Ellen; Wyman, Bradley; Xiao, Guanghua; Xie, Sharon; Xu, Jun; Xu, Guofan; Xu, Steven; Xu, Shunbin; Xu, Ye; Xu, Yi-Zheng; Yamada, Tomoko; Yamashita, Fumio; Yan, Pingkun; Yan, Yunyi; Yang, Guang; Yang, Wenlu; Yang, Eric; Yang, Hyun Duk; Yang, Jinzhong; Yang, Chung-Yi; Yang, Zijiang; Yang, Edward; Yassa, Michael; Yavorsky, Christian; Ye, Byoung Seok; Ye, Liang; Ye, Jong; Yee, Laura; Ying, Song; Yokoyama, Takao; Young, Stewart; Young, Jonathan; Younhyun, Jung; Yu, Dongchuan; Yu, Shiwei; Yu, C. Q.; Yu, Peng; Yuan, Ying; Yuan, Guihong; Yuan, Kai; Yuen, Bob; Yushkevich, Paul; Zaborszky, Laszlo; Zagorodnov, Vitali; Zagorski, Michael; Zahodne, Laura; Zarei, Mojtaba; Zawadzki, Rezi; Zeitzer, Jamie; Zelinski, Elizabeth; Zeskind, Benjamin; Zhan, Shu; Zhang, Jing; Zhang, Lijun; Zhang, Zhiguo; Zhang, Linda; Zhang, Zhe; Zhang, Daoqiang; Zhang, Huixiong; Zhang, Xin; Zhang, Tianhao; Zhang, Ping; Zhao, Jim; Zhao, Qinying; Zhao, Peng; Zhen, Xiantong; Zhijun, Yao; Zhou, Luping; Zhou, Bin; Zhou, Yongxia; Zhou, Sheng; Zhu, Hongtu; Zhu, Wen; Zhu, Wanlin; Zhu, Xuyan; Ziegler, Gabriel; Zilka, Samantha; Zisserman, Andrew; Zito, Giancarlo; Zu, Chen; Zulfigar, Annam

    2012-01-01

    Brain extraction is an important step in the analysis of brain images. The variability in brain morphology and the difference in intensity characteristics due to imaging sequences make the development of a general purpose brain extraction algorithm challenging. To address this issue, we propose a

  6. Expression of iron-related genes in human brain and brain tumors

    Directory of Open Access Journals (Sweden)

    Britton Robert S

    2009-04-01

    Full Text Available Abstract Background Defective iron homeostasis may be involved in the development of some diseases within the central nervous system. Although the expression of genes involved in normal iron balance has been intensively studied in other tissues, little is known about their expression in the brain. We investigated the mRNA levels of hepcidin (HAMP, HFE, neogenin (NEO1, transferrin receptor 1 (TFRC, transferrin receptor 2 (TFR2, and hemojuvelin (HFE2 in normal human brain, brain tumors, and astrocytoma cell lines. The specimens included 5 normal brain tissue samples, 4 meningiomas, one medulloblastoma, 3 oligodendrocytic gliomas, 2 oligoastrocytic gliomas, 8 astrocytic gliomas, and 3 astrocytoma cell lines. Results Except for hemojuvelin, all genes studied had detectable levels of mRNA. In most tumor types, the pattern of gene expression was diverse. Notable findings include high expression of transferrin receptor 1 in the hippocampus and medulla oblongata compared to other brain regions, low expression of HFE in normal brain with elevated HFE expression in meningiomas, and absence of hepcidin mRNA in astrocytoma cell lines despite expression in normal brain and tumor specimens. Conclusion These results indicate that several iron-related genes are expressed in normal brain, and that their expression may be dysregulated in brain tumors.

  7. Analysis of p53- immunoreactivity in astrocytic brain tumors

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    Shinkarenko T.V.

    2016-12-01

    Full Text Available P53 is an antioncogene with the frequently occured mutations in human tumor cells, leading to corresponding protein overexpression which can be detected by immunohistochemistry. Researches dedicated to the investigation of possibilities of using this technique gave controversial results. The authors investigated features of p53 protein expression in astrocytic brain tumors with different degrees of malignancy. Analyzed the relationship of the expression level of p53 by tumor cells with clinical parameters and Ki-67 proliferation index (PI as well. Tissues were collected from 52 cases with diagnosed astrocytic brain tumors. The sections were immunohistochemically stained with p53 and Ki-67. For each marker, 1000 tumor cells were counted and the ratio of positive tumor cells was calculated using software package ImageJ 1,47v. In normal brain tissue p53- expression was not identified. p53-immunoreactive tumor cells were detected in 25% (1/4 pilocytic astrocytomas, 33.3% (2/6 of diffuse astrocytomas, 53.8% (7/13 anaplastic astrocytomas, 58.6% (17/29 glioblastomas. A high proportion of p53-immunoreactive cells (> 30% was observed only in glioblastomas. The level of p53-imunoreactivity was not related to the age, gender and Grade WHO (p> 0,05. Spearman correlation coefficient between the relative quantity of ki-67- and p53-immunoreactive nuclei showed weak direct correlation (0.023, but the one was not statistically significant (p> 0,05. The level of p53-imunoreactivity is not dependent from age and sex of patients, Grade (WHO and proliferative activity (p>0,05 but the high level of p53-immunoreactive cells (>30% is found in glioblastoma specimens only, that may be due to the accumulation of mutations in DNA of tumor cells. There is insignificant weak relationship between relative quantities of ki-67- and p53-immunoreactive tumor cells (p>0,05.

  8. Simultaneous Whole-Brain Segmentation and White Matter Lesion Detection Using Contrast-Adaptive Probabilistic Models

    DEFF Research Database (Denmark)

    Puonti, Oula; Van Leemput, Koen

    2016-01-01

    In this paper we propose a new generative model for simultaneous brain parcellation and white matter lesion segmentation from multi-contrast magnetic resonance images. The method combines an existing whole-brain segmentation technique with a novel spatial lesion model based on a convolutional...... in multiple sclerosis indicate that the method’s lesion segmentation accuracy compares well to that of the current state-of-the-art in the field, while additionally providing robust whole-brain segmentations....... restricted Boltzmann machine. Unlike current state-of-the-art lesion detection techniques based on discriminative modeling, the proposed method is not tuned to one specific scanner or imaging protocol, and simultaneously segments dozens of neuroanatomical structures. Experiments on a public benchmark dataset...

  9. Neuroradiologic work-up of brain tumors

    International Nuclear Information System (INIS)

    Fishbein, D.S.

    1988-01-01

    The presence of an intracranial tumor may be suspected or deduced from the clinical history and examination, or it may be discovered incidentally during investigation of another disorder. Once the suggestion is raised, a variety of neuroradiologic techniques are available to define the extent and nature of the lesion. The studies performed may allow a tissue diagnosis to be presumed, may serve as a guide to proposed surgical therapy, or may allow the course of a previously diagnosed lesion to be followed. This chapter discusses the utility of common neuroradiologic techniques and their specific indications in the work-up of intracranial tumors. Emphasis is placed upon tests that are most frequently utilized and have the greatest value

  10. Cyclosporin safety in a simplified rat brain tumor implantation model

    Directory of Open Access Journals (Sweden)

    Francisco H. C. Felix

    2012-01-01

    Full Text Available Brain cancer is the second neurological cause of death. A simplified animal brain tumor model using W256 (carcinoma 256, Walker cell line was developed to permit the testing of novel treatment modalities. Wistar rats had a cell tumor solution inoculated stereotactically in the basal ganglia (right subfrontal caudate. This model yielded tumor growth in 95% of the animals, and showed absence of extracranial metastasis and systemic infection. Survival median was 10 days. Estimated tumor volume was 17.08±6.7 mm³ on the 7th day and 67.25±19.8 mm³ on 9th day post-inoculation. Doubling time was 24.25 h. Tumor growth induced cachexia, but no hematological or biochemical alterations. This model behaved as an undifferentiated tumor and can be promising for studying tumor cell migration in the central nervous system. Dexamethasone 3.0 mg/kg/day diminished significantly survival in this model. Cyclosporine 10 mg/kg/day administration was safely tolerated.

  11. Analysis of Mammalian Septin Expression in Human Malignant Brain Tumors

    Directory of Open Access Journals (Sweden)

    Dong-Seok Kim

    2004-03-01

    Full Text Available Septins are a highly conserved subfamily of GTPases that play an important role in the process of cytokinesis. To increase our understanding of the expression and localization of the different mammalian septins in human brain tumors, we used antibodies against septins 2, 3, 4, 5, 6, 7, 9, and 11 in immunofluorescence and Western blot analyses of astrocytomas and medulloblastomas. We then characterized the expression and subcellular distribution of the SEPT2 protein in aphidicolin-synchronized U373 MG astrocytoma cells by immunofluorescence and fluorescenceactivated cell sorter analysis. To determine the role of SEPT2 in astrocytoma cytokinesis, we inducibly expressed a dominant-negative (DN SEPT2 mutant in U373 MG astrocytoma cells. We show variable levels and expression patterns of the different septins in brain tissue, brain tumor specimens, and human brain tumor cell lines. SEPT2 was abundantly expressed in all brain tumor samples and cell lines studied. SEPT3 was expressed in medulloblastoma specimens and cell lines, but not in astrocytoma specimens or cell lines. SEPT2 expression was cell cycle-related, with maximal levels in G2-M. Immunocytochemical analysis showed endogenous levels of the different septins within the perinuclear and peripheral cytoplasmic regions. In mitosis, SEPT2 was concentrated at the cleavage furrow. By immunocytochemistry and flow cytometry, we show that a DN SEPT2 mutant inhibits the completion of cell division and results in the accumulation of multinucleated cells. These results suggest that septins are variably expressed in human brain tumors. Stable expression of the DN SEPT2 mutant leads to a G2-M cell cycle block in astrocytoma cells.

  12. A statistical method for lung tumor segmentation uncertainty in PET images based on user inference.

    Science.gov (United States)

    Zheng, Chaojie; Wang, Xiuying; Feng, Dagan

    2015-01-01

    PET has been widely accepted as an effective imaging modality for lung tumor diagnosis and treatment. However, standard criteria for delineating tumor boundary from PET are yet to develop largely due to relatively low quality of PET images, uncertain tumor boundary definition, and variety of tumor characteristics. In this paper, we propose a statistical solution to segmentation uncertainty on the basis of user inference. We firstly define the uncertainty segmentation band on the basis of segmentation probability map constructed from Random Walks (RW) algorithm; and then based on the extracted features of the user inference, we use Principle Component Analysis (PCA) to formulate the statistical model for labeling the uncertainty band. We validated our method on 10 lung PET-CT phantom studies from the public RIDER collections [1] and 16 clinical PET studies where tumors were manually delineated by two experienced radiologists. The methods were validated using Dice similarity coefficient (DSC) to measure the spatial volume overlap. Our method achieved an average DSC of 0.878 ± 0.078 on phantom studies and 0.835 ± 0.039 on clinical studies.

  13. Dexamethasone alleviates tumor-associated brain damage and angiogenesis.

    Directory of Open Access Journals (Sweden)

    Zheng Fan

    Full Text Available Children and adults with the most aggressive form of brain cancer, malignant gliomas or glioblastoma, often develop cerebral edema as a life-threatening complication. This complication is routinely treated with dexamethasone (DEXA, a steroidal anti-inflammatory drug with pleiotropic action profile. Here we show that dexamethasone reduces murine and rodent glioma tumor growth in a concentration-dependent manner. Low concentrations of DEXA are already capable of inhibiting glioma cell proliferation and at higher levels induce cell death. Further, the expression of the glutamate antiporter xCT (system Xc-; SLC7a11 and VEGFA is up-regulated after DEXA treatment indicating early cellular stress responses. However, in human gliomas DEXA exerts differential cytotoxic effects, with some human glioma cells (U251, T98G resistant to DEXA, a finding corroborated by clinical data of dexamethasone non-responders. Moreover, DEXA-resistant gliomas did not show any xCT alterations, indicating that these gene expressions are associated with DEXA-induced cellular stress. Hence, siRNA-mediated xCT knockdown in glioma cells increased the susceptibility to DEXA. Interestingly, cell viability of primary human astrocytes and primary rodent neurons is not affected by DEXA. We further tested the pharmacological effects of DEXA on brain tissue and showed that DEXA reduces tumor-induced disturbances of the microenvironment such as neuronal cell death and tumor-induced angiogenesis. In conclusion, we demonstrate that DEXA inhibits glioma cell growth in a concentration and species-dependent manner. Further, DEXA executes neuroprotective effects in brains and reduces tumor-induced angiogenesis. Thus, our investigations reveal that DEXA acts pleiotropically and impacts tumor growth, tumor vasculature and tumor-associated brain damage.

  14. American brain tumor patients treated with BNCT in Japan

    International Nuclear Information System (INIS)

    Laramore, G.E.; Griffin, B.R.; Spence, A.

    1995-01-01

    The purpose of this work is to establish and maintain a database for patients from the United States who have received BNCT in Japan for malignant gliomas of the brain. This database will serve as a resource for the DOE to aid in decisions relating to BNCT research in the United States, as well as assisting the design and implementation of clinical trials of BNCT for brain cancer patients in this country. The database will also serve as an information resource for patients with brain tumors and their families who are considering this form of therapy

  15. CADrx for GBM Brain Tumors: Predicting Treatment Response from Changes in Diffusion-Weighted MRI

    Directory of Open Access Journals (Sweden)

    Matthew S. Brown

    2009-11-01

    Full Text Available The goal of this study was to develop a computer-aided therapeutic response (CADrx system for early prediction of drug treatment response for glioblastoma multiforme (GBM brain tumors with diffusion weighted (DW MR images. In conventional Macdonald assessment, tumor response is assessed nine weeks or more post-treatment. However, we will investigate the ability of DW-MRI to assess response earlier, at five weeks post treatment. The apparent diffusion coefficient (ADC map, calculated from DW images, has been shown to reveal changes in the tumor’s microenvironment preceding morphologic tumor changes. ADC values in treated brain tumors could theoretically both increase due to the cell kill (and thus reduced cell density and decrease due to inhibition of edema. In this study, we investigated the effectiveness of features that quantify changes from pre- and post-treatment tumor ADC histograms to detect treatment response. There are three parts to this study: first, tumor regions were segmented on T1w contrast enhanced images by Otsu’s thresholding method, and mapped from T1w images onto ADC images by a 3D region of interest (ROI mapping tool using DICOM header information; second, ADC histograms of the tumor region were extracted from both pre- and five weeks post-treatment scans, and fitted by a two-component Gaussian mixture model (GMM. The GMM features as well as standard histogram-based features were extracted. Finally, supervised machine learning techniques were applied for classification of responders or non-responders. The approach was evaluated with a dataset of 85 patients with GBM under chemotherapy, in which 39 responded and 46 did not, based on tumor volume reduction. We compared adaBoost, random forest and support vector machine classification algorithms, using ten-fold cross validation, resulting in the best accuracy of 69.41% and the corresponding area under the curve (Az of 0.70.

  16. MR Fingerprinting of Adult Brain Tumors: Initial Experience.

    Science.gov (United States)

    Badve, C; Yu, A; Dastmalchian, S; Rogers, M; Ma, D; Jiang, Y; Margevicius, S; Pahwa, S; Lu, Z; Schluchter, M; Sunshine, J; Griswold, M; Sloan, A; Gulani, V

    2017-03-01

    MR fingerprinting allows rapid simultaneous quantification of T1 and T2 relaxation times. This study assessed the utility of MR fingerprinting in differentiating common types of adult intra-axial brain tumors. MR fingerprinting acquisition was performed in 31 patients with untreated intra-axial brain tumors: 17 glioblastomas, 6 World Health Organization grade II lower grade gliomas, and 8 metastases. T1, T2 of the solid tumor, immediate peritumoral white matter, and contralateral white matter were summarized within each ROI. Statistical comparisons on mean, SD, skewness, and kurtosis were performed by using the univariate Wilcoxon rank sum test across various tumor types. Bonferroni correction was used to correct for multiple-comparison testing. Multivariable logistic regression analysis was performed for discrimination between glioblastomas and metastases, and area under the receiver operator curve was calculated. Mean T2 values could differentiate solid tumor regions of lower grade gliomas from metastases (mean, 172 ± 53 ms, and 105 ± 27 ms, respectively; P = .004, significant after Bonferroni correction). The mean T1 of peritumoral white matter surrounding lower grade gliomas differed from peritumoral white matter around glioblastomas (mean, 1066 ± 218 ms, and 1578 ± 331 ms, respectively; P = .004, significant after Bonferroni correction). Logistic regression analysis revealed that the mean T2 of solid tumor offered the best separation between glioblastomas and metastases with an area under the curve of 0.86 (95% CI, 0.69-1.00; P fingerprinting allows rapid simultaneous T1 and T2 measurement in brain tumors and surrounding tissues. MR fingerprinting-based relaxometry can identify quantitative differences between solid tumor regions of lower grade gliomas and metastases and between peritumoral regions of glioblastomas and lower grade gliomas. © 2017 by American Journal of Neuroradiology.

  17. Combination strategies in multi-atlas image segmentation: application to brain MR data.

    Science.gov (United States)

    Artaechevarria, Xabier; Munoz-Barrutia, Arrate; Ortiz-de-Solorzano, Carlos

    2009-08-01

    It has been shown that employing multiple atlas images improves segmentation accuracy in atlas-based medical image segmentation. Each atlas image is registered to the target image independently and the calculated transformation is applied to the segmentation of the atlas image to obtain a segmented version of the target image. Several independent candidate segmentations result from the process, which must be somehow combined into a single final segmentation. Majority voting is the generally used rule to fuse the segmentations, but more sophisticated methods have also been proposed. In this paper, we show that the use of global weights to ponderate candidate segmentations has a major limitation. As a means to improve segmentation accuracy, we propose the generalized local weighting voting method. Namely, the fusion weights adapt voxel-by-voxel according to a local estimation of segmentation performance. Using digital phantoms and MR images of the human brain, we demonstrate that the performance of each combination technique depends on the gray level contrast characteristics of the segmented region, and that no fusion method yields better results than the others for all the regions. In particular, we show that local combination strategies outperform global methods in segmenting high-contrast structures, while global techniques are less sensitive to noise when contrast between neighboring structures is low. We conclude that, in order to achieve the highest overall segmentation accuracy, the best combination method for each particular structure must be selected.

  18. A Hybrid Hierarchical Approach for Brain Tissue Segmentation by Combining Brain Atlas and Least Square Support Vector Machine

    Science.gov (United States)

    Kasiri, Keyvan; Kazemi, Kamran; Dehghani, Mohammad Javad; Helfroush, Mohammad Sadegh

    2013-01-01

    In this paper, we present a new semi-automatic brain tissue segmentation method based on a hybrid hierarchical approach that combines a brain atlas as a priori information and a least-square support vector machine (LS-SVM). The method consists of three steps. In the first two steps, the skull is removed and the cerebrospinal fluid (CSF) is extracted. These two steps are performed using the toolbox FMRIB's automated segmentation tool integrated in the FSL software (FSL-FAST) developed in Oxford Centre for functional MRI of the brain (FMRIB). Then, in the third step, the LS-SVM is used to segment grey matter (GM) and white matter (WM). The training samples for LS-SVM are selected from the registered brain atlas. The voxel intensities and spatial positions are selected as the two feature groups for training and test. SVM as a powerful discriminator is able to handle nonlinear classification problems; however, it cannot provide posterior probability. Thus, we use a sigmoid function to map the SVM output into probabilities. The proposed method is used to segment CSF, GM and WM from the simulated magnetic resonance imaging (MRI) using Brainweb MRI simulator and real data provided by Internet Brain Segmentation Repository. The semi-automatically segmented brain tissues were evaluated by comparing to the corresponding ground truth. The Dice and Jaccard similarity coefficients, sensitivity and specificity were calculated for the quantitative validation of the results. The quantitative results show that the proposed method segments brain tissues accurately with respect to corresponding ground truth. PMID:24696800

  19. Life satisfaction in adult survivors of childhood brain tumors.

    Science.gov (United States)

    Crom, Deborah B; Li, Zhenghong; Brinkman, Tara M; Hudson, Melissa M; Armstrong, Gregory T; Neglia, Joseph; Ness, Kirsten K

    2014-01-01

    Adult survivors of childhood brain tumors experience multiple, significant, lifelong deficits as a consequence of their malignancy and therapy. Current survivorship literature documents the substantial impact such impairments have on survivors' physical health and quality of life. Psychosocial reports detail educational, cognitive, and emotional limitations characterizing survivors as especially fragile, often incompetent, and unreliable in evaluating their circumstances. Anecdotal data suggest some survivors report life experiences similar to those of healthy controls. The aim of our investigation was to determine whether life satisfaction in adult survivors of childhood brain tumors differs from that of healthy controls and to identify potential predictors of life satisfaction in survivors. This cross-sectional study compared 78 brain tumor survivors with population-based matched controls. Chi-square tests, t tests, and linear regression models were used to investigate patterns of life satisfaction and identify potential correlates. Results indicated that life satisfaction of adult survivors of childhood brain tumors was similar to that of healthy controls. Survivors' general health expectations emerged as the primary correlate of life satisfaction. Understanding life satisfaction as an important variable will optimize the design of strategies to enhance participation in follow-up care, reduce suffering, and optimize quality of life in this vulnerable population. © 2014 by Association of Pediatric Hematology/Oncology Nurses.

  20. Radiotherapy combined with Tegafur (FT-207s) for brain tumors

    International Nuclear Information System (INIS)

    Aoki, Yoshiro

    1981-01-01

    5-Fluorouracil (5-FU) has anti-tumor effects as an anti-metabolite, but it cannot pass the Blood-Brain-Barrier (BBB). FT-207 a masked-compound of 5-FU, is easily lipid soluble and is able to pass the BBB. Twenty eight patients of primary brain tumor and 8 patients of metastatic brain tumor were treated with irradiation combined with 750 mg of FT-207 suppository. Twenty four patients of primary brain tumor were treated only with irradiation as control. The mean survival time was 20.4 +- 11.8 months for the combined therapy group and 17.6 +- 8.6 months for the control. The concentration of FT-207 and 5-FU in serum and in cerebrospinal fluid (CSF) was investigated after administration of 750 mg of FT-207 suppository per annum. The maximum concentration of FT-207 and of 5-FU in serum was 20.4 +- 11.8 mcg/ml and 0.06 +- 0.02 mcg/ml, respectively. There were observed several side effects, such as anorexia, nausea, exanthema and etc. These side effects were not so great as to interrupt the therapy at the dose level of 750 mg of FT-207. However, at the dose of 1500 mg, one case showed disturbance of consciousness, to which attention should be called. (author)

  1. Psychological defense peculiarities in patients with brain tumor induced epilepsy

    Directory of Open Access Journals (Sweden)

    Alvin Acas Miranda

    2017-03-01

    Conclusion ― Life Style Index and Level of Subjective Control psychometric tests are an important component in the complex evaluation and treatment of patients with brain tumors and should be advocated as useful additional investigation method based on their prognostic value in patients with possible terminal illness.

  2. Gadolinium neutron capture therapy for brain tumors. Biological aspects

    International Nuclear Information System (INIS)

    Takagaki, Masao; Oda, Yoshifumi; Matsumoto, Masato; Kikuchi, Haruhiko; Kobayashi, Tooru; Kanda, Keiji; Ujeno, Yowri.

    1994-01-01

    This study investigated the tumoricidal effect of gadolinium neutron capture therapy (Gd-NCT) in in vitro and in vivo systems using Gd-DTPA. In in vitro study, a certain amount of Gd-DTPA, yielding 5000 ppm Gd-n, was added to human glioma cells, T98G, upon which thermal neutrons were exposed. After irradiation, the cells were incubated and the colonies were counted 10 days later. In in vivo study, Fisher-344 rats with experimentally induced gliosarcoma cells (9L) were exposed to thermal neutrons at a fluence rate of 3E+9/s for 1 h immediately after iv injection of Gd-DTPA. Two weeks after irradiation, brain samples were histologically examined. Tumor clearance of Gd-DTPA was also determined. In vitro analysis showed that a 1% survival level was obtained at 3.75E+12 (n/cm 2 ) for the Gd (+) medium and 2.50E+13 (n/cm 2 ) for the Gd (-) medium. In in vivo analysis, the concentration of Gd in 9L-rat brain tumor after iv injection of 0.2 mg/kg Gd-DTPA was found to be less than 100 ppm, but Gd-NCT on 9L-rat brain tumor administered with a ten-fold dose showed a substantial killing effect on tumor without serious injury to the normal brain structure. The killing effect of Gd-NCT was confirmed in in vitro and in vivo systems. (N.K.)

  3. Local anesthetics for brain tumor resection: Current perspectives

    NARCIS (Netherlands)

    J.W. Potters (Jan Willem); M. Klimek (Markus)

    2018-01-01

    textabstractThis review summarizes the added value of local anesthetics in patients undergoing craniotomy for brain tumor resection, which is a procedure that is carried out frequently in neurosurgical practice. The procedure can be carried out under general anesthesia, sedation with local

  4. Anxiety in the preoperative phase of awake brain tumor surgery

    NARCIS (Netherlands)

    Ruis, Carla; Huenges Wajer, I.M.C.; Robe, Pierre; van Zandvoort, Martine

    OBJECTIVE: Awake surgery emerges as a standard of care for brain tumors located in or near eloquent areas. Levels of preoperative anxiety in patients are important, because anxiety can influence cognitive performance and participation, hence altering the outcome of the procedure. In this study we

  5. Anxiety in the preoperative phase of awake brain tumor surgery

    NARCIS (Netherlands)

    Ruis, C.; Huenges Wajer, I.M.C.; Robe, Pierre; van Zandvoort, M.J.E.

    Objective Awake surgery emerges as a standard of care for brain tumors located in or near eloquent areas. Levels of preoperative anxiety in patients are important, because anxiety can influence cognitive performance and participation, hence altering the outcome of the procedure. In this study we

  6. Computed tomography in the CSF seeding of brain tumors

    International Nuclear Information System (INIS)

    Nakagawa, Yoshio; Fujimoto, Masahito; Naruse, Shoji; Ueda, Satoshi; Hirakawa, Kimiyoshi

    1981-01-01

    In the past three years nine cases of brain tumors with CSF seeding have been revealed by computed tomography (CT). We have been analyzing the CT pattern of CSF seeding, CSF cytology, and spinal metastasis. The brain tumors were classified as follows: five medulloblastomas, two glioblastomas, one germinoma, and one meningeal carcinomatosis. Their CT patterns were divided into three groups: 1) diffuse seeding of the basal cisterns. 2) invasion of the ventricular wall. 3) solitary metastasis in the ventricle. The subarachnoid seeding included four medulloblastomas and one meningeal carcinomatosis. The second type of seeding included two glioblastomas and one germinoma. One medulloblastoma had a single metastasis in the lateral ventricle. In the medulloblastomas, the diffuse seeding of the basal cisterns was more common than the invasion of the ventricular wall or solitary metastasis in the ventricle. Medulloblastomas were also accompanied by spinal metastasis. Because there were many cases of spinal metastasis in the first type of seeding, we concluded that there was a definite correlation between the CSF seeding of the basal cisterns and spinal metastasis. Needless to say, CT was the most important method for the diagnosis of the CSF seeding of brain tumors. However, because there was a case of CSF seeding which had not been demonstrated by CT, we also emphasized the importance of neurological examination and CSF cytology in the diagnosis of the CSF seeding of brain tumors. (author)

  7. Clinical study on brain tumors in the aged

    International Nuclear Information System (INIS)

    Teramoto, Akira; Manaka, Shinya; Takakura, Kintomo

    1981-01-01

    In order to investigate the clinical features and the prognosis of brain tumors in the aged, 132 cases over 60 years of age were studied from the consecutive series of 1,793 brain tumors in the University of Tokyo Hospital (1963 - 1979). The incidence of brain tumors in the aged was 7.4% on the whole, while it showed a significant increase from 4.8% (1960's) to 11.5% (the later half of 1970's). Histologically, meningiomas were the most common tumors (26%), followed by neurinomas (17%), pituitary adenomas (16%) and metastatic tumors (15%). Malignant gliomas were found more frequently than benign ones. There were more meningiomas as age advanced. The proportion and the number of meningioma cases has obviously increased in recent years when CT scanners became available. Symptoms of intracranial hypertention were found less frequently in aged patients although they were still common in cases of glioblastomas. The duration from onset to surgery was relatively long, especially in cases of neurinomas and pituitary adenomas. Two cases of astrocytomas belonged to the category of silent gliomas. Overall operative mortality rate was 10.6%, while it showed a marked decrease to 4.7% in the 1970's. Five-year survival rates were as follows: meningiomas (58%), pituitary adenomas (70%), neurinomas (80%), glioblastomas (20%) and astrocytomas (25%). As for functional prognoses, 30% of the patients showed poor states on ADL, mostly because of residual psychic disorders. (author)

  8. A fractional motion diffusion model for grading pediatric brain tumors.

    Science.gov (United States)

    Karaman, M Muge; Wang, He; Sui, Yi; Engelhard, Herbert H; Li, Yuhua; Zhou, Xiaohong Joe

    2016-01-01

    To demonstrate the feasibility of a novel fractional motion (FM) diffusion model for distinguishing low- versus high-grade pediatric brain tumors; and to investigate its possible advantage over apparent diffusion coefficient (ADC) and/or a previously reported continuous-time random-walk (CTRW) diffusion model. With approval from the institutional review board and written informed consents from the legal guardians of all participating patients, this study involved 70 children with histopathologically-proven brain tumors (30 low-grade and 40 high-grade). Multi- b -value diffusion images were acquired and analyzed using the FM, CTRW, and mono-exponential diffusion models. The FM parameters, D fm , φ , ψ (non-Gaussian diffusion statistical measures), and the CTRW parameters, D m , α , β (non-Gaussian temporal and spatial diffusion heterogeneity measures) were compared between the low- and high-grade tumor groups by using a Mann-Whitney-Wilcoxon U test. The performance of the FM model for differentiating between low- and high-grade tumors was evaluated and compared with that of the CTRW and the mono-exponential models using a receiver operating characteristic (ROC) analysis. The FM parameters were significantly lower ( p  CTRW model. Similar to the CTRW model, the FM model can improve differentiation between low- and high-grade pediatric brain tumors over ADC.

  9. ICA-Based Segmentation of the Brain on Perfusion Data

    National Research Council Canada - National Science Library

    Tasciyan, T

    2001-01-01

    ...) images of the brain. Tissue types such as gray matter (GM), white matter (WM), and pathology appear as different ICA components as a result of their distinct temporal response to the first passage of contrast agent through the brain...

  10. Cerenkov and radioluminescence imaging of brain tumor specimens during neurosurgery

    Science.gov (United States)

    Spinelli, Antonello Enrico; Schiariti, Marco P.; Grana, Chiara M.; Ferrari, Mahila; Cremonesi, Marta; Boschi, Federico

    2016-05-01

    We presented the first example of Cerenkov luminescence imaging (CLI) and radioluminescence imaging (RLI) of human tumor specimens. A patient with a brain meningioma localized in the left parietal region was injected with 166 MBq of Y90-DOTATOC the day before neurosurgery. The specimens of the tumor removed during surgery were imaged using both CLI and RLI using an optical imager prototype developed in our laboratory. The system is based on a cooled electron multiplied charge coupled device coupled with an f/0.95 17-mm C-mount lens. We showed for the first time the possibility of obtaining CLI and RLI images of fresh human brain tumor specimens removed during neurosurgery.

  11. Multi-fractal detrended texture feature for brain tumor classification

    Science.gov (United States)

    Reza, Syed M. S.; Mays, Randall; Iftekharuddin, Khan M.

    2015-03-01

    We propose a novel non-invasive brain tumor type classification using Multi-fractal Detrended Fluctuation Analysis (MFDFA) [1] in structural magnetic resonance (MR) images. This preliminary work investigates the efficacy of the MFDFA features along with our novel texture feature known as multifractional Brownian motion (mBm) [2] in classifying (grading) brain tumors as High Grade (HG) and Low Grade (LG). Based on prior performance, Random Forest (RF) [3] is employed for tumor grading using two different datasets such as BRATS-2013 [4] and BRATS-2014 [5]. Quantitative scores such as precision, recall, accuracy are obtained using the confusion matrix. On an average 90% precision and 85% recall from the inter-dataset cross-validation confirm the efficacy of the proposed method.

  12. Cytokine Gene Polymorphisms in Egyptian Cases with Brain Tumors

    International Nuclear Information System (INIS)

    Badr El-Din, N.K.; Abdel-Hady, E.K.; Salem, F.K.; Settin, A.; ALI, N.

    2009-01-01

    Background: Cytokines are proposed to play important roles in brain tumor biology as well as neuro degeneration or impaired neuronal function. Objectives: This work aimed to check the association of polymorphisms of cytokine genes in Egyptian cases with brain tumors. Methods: This work included 45 cases affected by brain tumors diagnosed as 24 benign and 21 malignant. Their median age was 45 years, and they were 20 males and 25 females. These cases were taken randomly from the Neurosurgery Department of Mansoura University Hospital, Egypt. Case genotypes were compared to 98 healthy unrelated controls from the same locality. DNA was amplified using PCR utilizing sequence specific primers (SSP) for detection of polymorphisms related to TNF-a-308 (G/A), IL-10-1082 (G/A), IL-6-174 (G/C) and IL-1Ra (VNTR) genes. Results: Cases affected with benign brain tumors showed a significant higher frequency of IL-10-1082 A/A [odds ratio (OR=8.0), p<0.001] and IL-6-174 C/C (OR=6.3, p=0.002) homozygous genotypes as compared to controls. Malignant cases, on the other hand, showed significantly higher frequency of IL-6-174 C/C (OR =4.8, p=0.002) homozygous genotype and TNF-a-308 A/A (OR=4.9, p<0.001) homozygous genotype when compared to controls. In the meantime, all cases showed no significant difference regarding the distribution of IL-1Ra VNTR genotype polymorphism compared to controls. Conclusions: Cytokine gene polymorphisms showed a pattern of association with brain tumors which may have potential impact on family counseling and disease management.

  13. Obtention of tumor volumes in PET images stacks using techniques of colored image segmentation

    International Nuclear Information System (INIS)

    Vieira, Jose W.; Lopes Filho, Ferdinand J.; Vieira, Igor F.

    2014-01-01

    This work demonstrated step by step how to segment color images of the chest of an adult in order to separate the tumor volume without significantly changing the values of the components R (Red), G (Green) and B (blue) of the colors of the pixels. For having information which allow to build color map you need to segment and classify the colors present at appropriate intervals in images. The used segmentation technique is to select a small rectangle with color samples in a given region and then erase with a specific color called 'rubber' the other regions of image. The tumor region was segmented into one of the images available and the procedure is displayed in tutorial format. All necessary computational tools have been implemented in DIP (Digital Image Processing), software developed by the authors. The results obtained, in addition to permitting the construction the colorful map of the distribution of the concentration of activity in PET images will also be useful in future work to enter tumors in voxel phantoms in order to perform dosimetric assessments

  14. Segmentation of tumor ultrasound image in HIFU therapy based on texture and boundary encoding

    International Nuclear Information System (INIS)

    Zhang, Dong; Xu, Menglong; Quan, Long; Yang, Yan; Qin, Qianqing; Zhu, Wenbin

    2015-01-01

    It is crucial in high intensity focused ultrasound (HIFU) therapy to detect the tumor precisely with less manual intervention for enhancing the therapy efficiency. Ultrasound image segmentation becomes a difficult task due to signal attenuation, speckle effect and shadows. This paper presents an unsupervised approach based on texture and boundary encoding customized for ultrasound image segmentation in HIFU therapy. The approach oversegments the ultrasound image into some small regions, which are merged by using the principle of minimum description length (MDL) afterwards. Small regions belonging to the same tumor are clustered as they preserve similar texture features. The mergence is completed by obtaining the shortest coding length from encoding textures and boundaries of these regions in the clustering process. The tumor region is finally selected from merged regions by a proposed algorithm without manual interaction. The performance of the method is tested on 50 uterine fibroid ultrasound images from HIFU guiding transducers. The segmentations are compared with manual delineations to verify its feasibility. The quantitative evaluation with HIFU images shows that the mean true positive of the approach is 93.53%, the mean false positive is 4.06%, the mean similarity is 89.92%, the mean norm Hausdorff distance is 3.62% and the mean norm maximum average distance is 0.57%. The experiments validate that the proposed method can achieve favorable segmentation without manual initialization and effectively handle the poor quality of the ultrasound guidance image in HIFU therapy, which indicates that the approach is applicable in HIFU therapy. (paper)

  15. Segmentation of tumor ultrasound image in HIFU therapy based on texture and boundary encoding

    Science.gov (United States)

    Zhang, Dong; Xu, Menglong; Quan, Long; Yang, Yan; Qin, Qianqing; Zhu, Wenbin

    2015-02-01

    It is crucial in high intensity focused ultrasound (HIFU) therapy to detect the tumor precisely with less manual intervention for enhancing the therapy efficiency. Ultrasound image segmentation becomes a difficult task due to signal attenuation, speckle effect and shadows. This paper presents an unsupervised approach based on texture and boundary encoding customized for ultrasound image segmentation in HIFU therapy. The approach oversegments the ultrasound image into some small regions, which are merged by using the principle of minimum description length (MDL) afterwards. Small regions belonging to the same tumor are clustered as they preserve similar texture features. The mergence is completed by obtaining the shortest coding length from encoding textures and boundaries of these regions in the clustering process. The tumor region is finally selected from merged regions by a proposed algorithm without manual interaction. The performance of the method is tested on 50 uterine fibroid ultrasound images from HIFU guiding transducers. The segmentations are compared with manual delineations to verify its feasibility. The quantitative evaluation with HIFU images shows that the mean true positive of the approach is 93.53%, the mean false positive is 4.06%, the mean similarity is 89.92%, the mean norm Hausdorff distance is 3.62% and the mean norm maximum average distance is 0.57%. The experiments validate that the proposed method can achieve favorable segmentation without manual initialization and effectively handle the poor quality of the ultrasound guidance image in HIFU therapy, which indicates that the approach is applicable in HIFU therapy.

  16. Banking Brain Tumor Specimens Using a University Core Facility.

    Science.gov (United States)

    Bregy, Amade; Papadimitriou, Kyriakos; Faber, David A; Shah, Ashish H; Gomez, Carmen R; Komotar, Ricardo J; Egea, Sophie C

    2015-08-01

    Within the past three decades, the significance of banking human cancer tissue for the advancement of cancer research has grown exponentially. The purpose of this article is to detail our experience in collecting brain tumor specimens in collaboration with the University of Miami/Sylvester Tissue Bank Core Facility (UM-TBCF), to ensure the availability of high-quality samples of central nervous system tumor tissue for research. Successful tissue collection begins with obtaining informed consent from patients following institutional IRB and federal HIPAA guidelines, and it needs a well-trained professional staff and continued maintenance of high ethical standards and record keeping. Since starting in 2011, we have successfully banked 225 brain tumor specimens for research. Thus far, the most common tumor histology identified among those specimens has been glioblastoma (22.1%), followed by meningioma (18.1%). The majority of patients were White, non-Hispanics accounting for 45.1% of the patient population; Hispanic/Latinos accounted for 23%, and Black/African Americans accounted for 14%, which represent the particular population of the State of Florida according to the 2010 census data. The most common tumors found in each subgroup were as follows: Black/African American, glioblastoma and meningioma; Hispanic, metastasis and glioblastoma; White, glioblastoma and meningioma. The UM-TBCF is a valuable repository, offering high-quality tumor samples from a unique patient population.

  17. Non-tumor enhancement at the surgical margin on CT after the removal of brain tumors

    International Nuclear Information System (INIS)

    Adachi, Michito; Hosoya, Takaaki; Yamaguchi, Kohichi; Yamada, Kiyotada

    1992-01-01

    Marginal enhancement is occasionally seen at the surgical margin on CT after the total removal of brain tumors. This enhancement disappears in due time, and therefore we call it non-tumor enhancement. It is often difficult, however, to differentiate non-tumor enhancement from tumor recurrence. In this study, we attempted to determine the characteristics of non-tumor enhancement. The subjects of the study consisted of 15 patients with astrocytoma and one with metastatic tumor in whom sequential CT scans had been performed after total removal of the tumor. Based on the observation of these sequential CT scans, the characteristics of non-tumor enhancement were presumed to be as follows: (1) In four cases, enhancement at the surgical margin persisted more than four months after surgery and then disappeared. Therefore, these cases were considered non-tumor enhancement. Prolonged duration of enhancement such as that in these cases is not necessarily due to recurrence. Marginal enhancement within 3 mm in thickness and with a well-demarcated border like that of a flax is likely to be non-tumor enhancement. (author)

  18. Injectable Hydrogels for Localized Chemotherapy and Radiotherapy in Brain Tumors.

    Science.gov (United States)

    Puente, Pilar de la; Fettig, Nicole; Luderer, Micah J; Jin, Abbey; Shah, Shruti; Muz, Barbara; Kapoor, Vaishali; Goddu, Sreekrishna M; Salama, Noha Nabil; Tsien, Christina; Thotala, Dinesh; Shoghi, Kooresh; Rogers, Buck; Azab, Abdel Kareem

    2018-03-01

    Overall survival of patients with newly diagnosed glioblastoma (GBM) remains dismal at 16 months with state-of-the-art treatment that includes surgical resection, radiation, and chemotherapy. GBM tumors are highly heterogeneous, and mechanisms for overcoming tumor resistance have not yet fully been elucidated. An injectable chitosan hydrogel capable of releasing chemotherapy (temozolomide [TMZ]) while retaining radioactive isotopes agents (iodine, [ 131 I]) was used as a vehicle for localized radiation and chemotherapy, within the surgical cavity. Release from hydrogels loaded with TMZ or 131 I was characterized in vitro and in vivo and their efficacy on tumor progression and survival on GBM tumors was also measured. The in vitro release of 131 I was negligible over 42 days, whereas the TMZ was completely released over the first 48 h. 131 I was completely retained in the tumor bed with negligible distribution in other tissues and that when delivered locally, the chemotherapy accumulated in the tumor at 10-fold higher concentrations than when delivered systemically. We found that the tumors were significantly decreased, and survival was improved in both treatment groups compared to the control group. Novel injectable chemo-radio-hydrogel implants may potentially improve the local control and overall outcome of aggressive, poor prognosis brain tumors. Copyright © 2018. Published by Elsevier Inc.

  19. MR brain scan tissues and structures segmentation: local cooperative Markovian agents and Bayesian formulation

    International Nuclear Information System (INIS)

    Scherrer, B.

    2008-12-01

    Accurate magnetic resonance brain scan segmentation is critical in a number of clinical and neuroscience applications. This task is challenging due to artifacts, low contrast between tissues and inter-individual variability that inhibit the introduction of a priori knowledge. In this thesis, we propose a new MR brain scan segmentation approach. Unique features of this approach include (1) the coupling of tissue segmentation, structure segmentation and prior knowledge construction, and (2) the consideration of local image properties. Locality is modeled through a multi-agent framework: agents are distributed into the volume and perform a local Markovian segmentation. As an initial approach (LOCUS, Local Cooperative Unified Segmentation), intuitive cooperation and coupling mechanisms are proposed to ensure the consistency of local models. Structures are segmented via the introduction of spatial localization constraints based on fuzzy spatial relations between structures. In a second approach, (LOCUS-B, LOCUS in a Bayesian framework) we consider the introduction of a statistical atlas to describe structures. The problem is reformulated in a Bayesian framework, allowing a statistical formalization of coupling and cooperation. Tissue segmentation, local model regularization, structure segmentation and local affine atlas registration are then coupled in an EM framework and mutually improve. The evaluation on simulated and real images shows good results, and in particular, a robustness to non-uniformity and noise with low computational cost. Local distributed and cooperative MRF models then appear as a powerful and promising approach for medical image segmentation. (author)

  20. EEG alpha map series: brain micro-states by space-oriented adaptive segmentation.

    Science.gov (United States)

    Lehmann, D; Ozaki, H; Pal, I

    1987-09-01

    The spontaneous EEG, viewed as a series of momentary scalp field maps, shows stable map configurations (of periodically reversed polarity) for varying durations, and discontinuous changes of the configurations. For adaptive segmentation of map series into spatially stationary epochs, the maps at the times of maximal map relief are selected and spatially described by the two locations of maximal and minimal (extreme) potentials; a segment ends if over time an extreme leaves its pre-set spatial window. Over 6 subjects, the resting alpha EEG showed 210 msec mean segment duration; segments longer than 323 msec covered 50% of the total time; the most prominent segment class (1.5% of all classes) covered 20% of total time (prominence varied strongly over classes; not all possible classes occurred). Spectral power and phase of averages of adaptive and pre-determined segments demonstrated the adequacy of the strategy, and the homogeneity of adaptive segment classes by their reduced within-class variance. It is suggested that different segment classes manifest different brain functional states exerting different effects on information processing. The spatially stationary segments might be basic building blocks of brain information processing, possibly operationalizing consciousness time and offering a common phenomenology for spontaneous activity and event-related potentials. The functional significance of segments might be modes or steps of information processing or performance, tested, e.g., as reaction time.

  1. Automated Segmentation of in Vivo and Ex Vivo Mouse Brain Magnetic Resonance Images

    Directory of Open Access Journals (Sweden)

    Alize E.H. Scheenstra

    2009-01-01

    Full Text Available Segmentation of magnetic resonance imaging (MRI data is required for many applications, such as the comparison of different structures or time points, and for annotation purposes. Currently, the gold standard for automated image segmentation is nonlinear atlas-based segmentation. However, these methods are either not sufficient or highly time consuming for mouse brains, owing to the low signal to noise ratio and low contrast between structures compared with other applications. We present a novel generic approach to reduce processing time for segmentation of various structures of mouse brains, in vivo and ex vivo. The segmentation consists of a rough affine registration to a template followed by a clustering approach to refine the rough segmentation near the edges. Compared with manual segmentations, the presented segmentation method has an average kappa index of 0.7 for 7 of 12 structures in in vivo MRI and 11 of 12 structures in ex vivo MRI. Furthermore, we found that these results were equal to the performance of a nonlinear segmentation method, but with the advantage of being 8 times faster. The presented automatic segmentation method is quick and intuitive and can be used for image registration, volume quantification of structures, and annotation.

  2. Advance MRI for pediatric brain tumors with emphasis on clinical benefits

    Energy Technology Data Exchange (ETDEWEB)

    Goo, Hyun Woo; Ra, Young Shin [Asan Medical Center, University of Ulsan College of Medicine, Seoul(Korea, Republic of)

    2017-01-15

    Conventional anatomic brain MRI is often limited in evaluating pediatric brain tumors, the most common solid tumors and a leading cause of death in children. Advanced brain MRI techniques have great potential to improve diagnostic performance in children with brain tumors and overcome diagnostic pitfalls resulting from diverse tumor pathologies as well as nonspecific or overlapped imaging findings. Advanced MRI techniques used for evaluating pediatric brain tumors include diffusion-weighted imaging, diffusion tensor imaging, functional MRI, perfusion imaging, spectroscopy, susceptibility-weighted imaging, and chemical exchange saturation transfer imaging. Because pediatric brain tumors differ from adult counterparts in various aspects, MRI protocols should be designed to achieve maximal clinical benefits in pediatric brain tumors. In this study, we review advanced MRI techniques and interpretation algorithms for pediatric brain tumors.

  3. Anti-angiogenic therapy in pediatric brain tumors : An effective strategy?

    NARCIS (Netherlands)

    Sie, Mariska; den Dunnen, Wilfred F. A.; Hoving, Eelco W.; de Bont, Eveline S. J. M.

    Brain tumors are still the leading cause of cancer morbidity and mortality among children, despite different therapeutic options including neurosurgery, chemotherapy and radiation. As angiogenesis is highly crucial in brain tumor growth and progression, numerous clinical trials evaluating diverse

  4. Investigating Contingency Risk Factors of Brain Tumor in Children and Adolescents

    Directory of Open Access Journals (Sweden)

    A Nazemi

    2014-12-01

    Conclusion: According to research results, several preventable and predictable factors are linked to pediatric brain tumors. Therefore, children prone to brain tumors are recommended to be examined and screened for these risk factors.

  5. Why does Jack, and not Jill, break his crown? Sex disparity in brain tumors.

    Science.gov (United States)

    Sun, Tao; Warrington, Nicole M; Rubin, Joshua B

    2012-01-25

    It is often reported that brain tumors occur more frequently in males, and that males suffer a worse outcome from brain tumors than females. If correct, these observations suggest that sex plays a fundamental role in brain tumor biology. The following review of the literature regarding primary and metastatic brain tumors, reveals that brain tumors do occur more frequently in males compared to females regardless of age, tumor histology, or region of the world. Sexually dimorphic mechanisms that might control tumor cell biology, as well as immune and brain microenvironmental responses to cancer, are explored as the basis for this sex disparity. Elucidating the mechanisms by which sex chromosomes and sex hormones impact on brain tumorigenesis and progression will advance our understanding of basic cancer biology and is likely to be essential for optimizing the care of brain tumor patients.

  6. SU-E-J-224: Multimodality Segmentation of Head and Neck Tumors

    International Nuclear Information System (INIS)

    Aristophanous, M; Yang, J; Beadle, B

    2014-01-01

    Purpose: Develop an algorithm that is able to automatically segment tumor volume in Head and Neck cancer by integrating information from CT, PET and MR imaging simultaneously. Methods: Twenty three patients that were recruited under an adaptive radiotherapy protocol had MR, CT and PET/CT scans within 2 months prior to start of radiotherapy. The patients had unresectable disease and were treated either with chemoradiotherapy or radiation therapy alone. Using the Velocity software, the PET/CT and MR (T1 weighted+contrast) scans were registered to the planning CT using deformable and rigid registration respectively. The PET and MR images were then resampled according to the registration to match the planning CT. The resampled images, together with the planning CT, were fed into a multi-channel segmentation algorithm, which is based on Gaussian mixture models and solved with the expectation-maximization algorithm and Markov random fields. A rectangular region of interest (ROI) was manually placed to identify the tumor area and facilitate the segmentation process. The auto-segmented tumor contours were compared with the gross tumor volume (GTV) manually defined by the physician. The volume difference and Dice similarity coefficient (DSC) between the manual and autosegmented GTV contours were calculated as the quantitative evaluation metrics. Results: The multimodality segmentation algorithm was applied to all 23 patients. The volumes of the auto-segmented GTV ranged from 18.4cc to 32.8cc. The average (range) volume difference between the manual and auto-segmented GTV was −42% (−32.8%–63.8%). The average DSC value was 0.62, ranging from 0.39 to 0.78. Conclusion: An algorithm for the automated definition of tumor volume using multiple imaging modalities simultaneously was successfully developed and implemented for Head and Neck cancer. This development along with more accurate registration algorithms can aid physicians in the efforts to interpret the multitude of

  7. Technical Note: A deep learning based auto segmentation of rectal tumors in MR images.

    Science.gov (United States)

    Wang, Jiazhou; Lu, Jiayu; Qin, Gan; Shen, Lijun; Sun, Yiqun; Ying, Hongmei; Zhang, Zhen; Hu, Weigang

    2018-04-16

    Manual contouring of gross tumor volumes (GTV) is a crucial and time-consuming process in rectum cancer radiotherapy. This study aims to develop a simple deep learning based auto segmentation algorithm to segment rectal tumors on T2 weighted MR images. MRI scans (3T, T2-weighted) of 93 patients with locally advanced (cT3-4 and/or cN1-2) rectal cancer treated with neoadjuvant chemoradiotherapy followed by surgery were enrolled in this study. A 2D U-net similar network was established as a training model. The model was trained in two phases to increase efficiency. These phases were tumor recognition and tumor segmentation. An opening (erosion and dilation) process was implemented to smooth contours after segmentation. Data were randomly separated into training (90%) and validation (10%) datasets for a 10-folder cross-validation. Additionally, 20 patients were double contoured for performance evaluation. Four indices were calculated to evaluate the similarity of automated and manual segmentation, including Hausdorff distance (HD), average surface distance (ASD), Dice index (DSC), and Jaccard index (JSC). The DSC, JSC, HD, ASD (mean±SD) were 0.74±0.14, 0.60±0.16, 20.44±13.35 and 3.25±1.69mm for validation dataset; and these indices were 0.71±0.13, 0.57±0.15, 14.91±7.62 and 2.67±1.46mm between two human radiation oncologists, respectively. No significant difference has been observed between automated segmentation and manual segmentation considering DSC (p=0.42), JSC (p=0.35), HD (p=0.079) and ASD (p=0.16). However, significant difference was found for HD (p=0.0027) without opening process. This study showed that a simple deep learning neural network can perform segmentation for rectum cancer based on MRI T2 images with results comparable to a human. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

  8. A review of atlas-based segmentation for magnetic resonance brain images.

    Science.gov (United States)

    Cabezas, Mariano; Oliver, Arnau; Lladó, Xavier; Freixenet, Jordi; Cuadra, Meritxell Bach

    2011-12-01

    Normal and abnormal brains can be segmented by registering the target image with an atlas. Here, an atlas is defined as the combination of an intensity image (template) and its segmented image (the atlas labels). After registering the atlas template and the target image, the atlas labels are propagated to the target image. We define this process as atlas-based segmentation. In recent years, researchers have investigated registration algorithms to match atlases to query subjects and also strategies for atlas construction. In this paper we present a review of the automated approaches for atlas-based segmentation of magnetic resonance brain images. We aim to point out the strengths and weaknesses of atlas-based methods and suggest new research directions. We use two different criteria to present the methods. First, we refer to the algorithms according to their atlas-based strategy: label propagation, multi-atlas methods, and probabilistic techniques. Subsequently, we classify the methods according to their medical target: the brain and its internal structures, tissue segmentation in healthy subjects, tissue segmentation in fetus, neonates and elderly subjects, and segmentation of damaged brains. A quantitative comparison of the results reported in the literature is also presented. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

  9. 18F-fluorothymidine kinetics of malignant brain tumors

    International Nuclear Information System (INIS)

    Schiepers, Christiaan; Chen, Wei; Dahlbom, Magnus; Huang, Sung-Cheng; Cloughesy, Timothy; Hoh, Carl K.

    2007-01-01

    18 F-labeled deoxy-fluorothymidine (FLT), a marker of cellular proliferation, has been used in PET tumor imaging. Here, the FLT kinetics of malignant brain tumors were investigated. Seven patients with high-grade tumors and two patients with metastases had 12 studies. After 1.5 MBq/kg 18 F-FLT had been administered intravenously, dynamic PET studies were acquired for 75 min. Images were reconstructed with iterative algorithms, and corrections applied for attenuation and scatter. Parametric images were generated with factor analysis, and vascular input and tumor output functions were derived. Compartmental models were used to estimate the rate constants. The standard three-compartment model appeared appropriate to describe 18 F-FLT uptake. Corrections for blood volume, metabolites, and partial volume were necessary. Kinetic parameters were correlated with tumor pathology and clinical follow-up data. Two groups could be distinguished: lesions that were tumor predominant (TumP) and lesions that were treatment change predominant (TrcP). Both groups had a widely varying k 1 (transport across the damaged BBB, range 0.02-0.2). Group TrcP had a relatively low k 3 (phosphorylation rate, range 0.017-0.027), whereas k 3 varied sevenfold in group TumP (range 0.015-0.11); the k 3 differences were significant (p 3 /(k 2 +k 3 )] was able to separate the two groups (p 18 F-FLT kinetics in malignant brain tumors. Patients could be distinguished as having: (1) tumor-predominant or (2) treatment change-predominant lesions, with significantly different phosphorylation rates. (orig.)

  10. History and evolution of brain tumor imaging: insights through radiology.

    Science.gov (United States)

    Castillo, Mauricio

    2014-11-01

    This review recounts the history of brain tumor diagnosis from antiquity to the present and, indirectly, the history of neuroradiology. Imaging of the brain has from the beginning held an enormous interest because of the inherent difficulty of this endeavor due to the presence of the skull. Because of this, most techniques when newly developed have always been used in neuroradiology and, although some have proved to be inappropriate for this purpose, many were easily incorporated into the specialty. The first major advance in modern neuroimaging was contrast agent-enhanced computed tomography, which permitted accurate anatomic localization of brain tumors and, by virtue of contrast enhancement, malignant ones. The most important advances in neuroimaging occurred with the development of magnetic resonance imaging and diffusion-weighted sequences that allowed an indirect estimation of tumor cellularity; this was further refined by the development of perfusion and permeability mapping. From its beginnings with indirect and purely anatomic imaging techniques, neuroradiology now uses a combination of anatomic and physiologic techniques that will play a critical role in biologic tumor imaging and radiologic genomics.

  11. Segmentation of brain parenchymal regions into gray matter and white matter with Alzheimer's disease

    International Nuclear Information System (INIS)

    Tokunaga, Chiaki; Yoshiura, Takashi; Yamashita, Yasuo; Magome, Taiki; Honda, Hiroshi; Arimura, Hidetaka; Toyofuku, Fukai; Ohki, Masafumi

    2010-01-01

    It is very difficult and time consuming for neuroradiologists to estimate the degree of cerebral atrophy based on the volume of cortical regions etc. Our purpose of this study was to develop an automated segmentation of the brain parenchyma into gray and white matter regions with Alzheimer's disease (AD) in three-dimensional (3D) T1-weighted MR images. Our proposed method consisted of extraction of a brain parenchymal region based on a brain model matching and segmentation of the brain parenchyma into gray and white matter regions based on a fuzzy c-means (FCM) algorithm. We applied our proposed method to MR images of the whole brains obtained from 9 cases, including 4 clinically AD cases and 5 control cases. The mean volume percentage of a cortical region (41.7%) to a brain parenchymal region in AD patients was smaller than that (45.2%) in the control subjects (p=0.000462). (author)

  12. A Multiatlas Approach for Segmenting Subcortical Brain Structures using Local Patch Distance

    Directory of Open Access Journals (Sweden)

    Neela RAMAMOORTHI

    2015-12-01

    Full Text Available In the diagnosis and treatment of various diseases, often segmenting the brain structures from MRI data is the key step. Since there are larger variations in the anatomical structures of the brain, segmentation becomes a crucial process. Using only the intensity information is not enough to segment structures since two or more structures may share the same tissues. Recently, the use of multiple pre-labeled images called atlases or templates are used in the process of segmentation of image data. Both single atlas and multiple atlases can be used. However, using multiple atlases in the segmentation process proves a dominant method in segmenting brain structures with challenging and overlapping structures. In this paper, we propose two multi atlas segmentation methods: Local Patch Distance Segmentation (LPDS and Weighted Local Patch Distance Segmentation (WLPDS. These methods use local patch distance in the label fusion step. LPDS uses local patch distance to find the best patch match for label propagation. WLPDS uses local patch distance to calculate local weights. The brain MRI images from the MICCAI 2012 segmentation challenge are chosen for experimental purposes. These datasets are publicly available and can be downloaded from MIDAS. The proposed techniques are compared with existing fusion methods such as majority voting and weighted majority voting using the similarity measures such as Dice overlap (DC, Jaccard coefficient (JC and Kappa statistics. For 20 test data sets, LPDS gives DICE=0.95±0.05, JACCARD=0.91±0.04 and KAPPA=0.94±0.07. WLPDS gives DICE=0.98±0.02, JACCARD=0.92±0.03 and KAPPA=0.95±0.04.

  13. Microvessel organization and structure in experimental brain tumors: microvessel populations with distinctive structural and functional properties.

    Science.gov (United States)

    Schlageter, K E; Molnar, P; Lapin, G D; Groothuis, D R

    1999-11-01

    We studied microvessel organization in five brain tumor models (ENU, MSV, RG-2, S635cl15, and D-54MG) and normal brain, including microvessel diameter (LMVD), intermicrovessel distance (IMVD), microvessel density (MVD), surface area (S(v)), and orientation. LMVD and IMVD were larger and MVD was lower in tumors than normal brain. S(v) in tumors overlapped normal brain values and orientation was random in both tumors and brain. ENU and RG-2 tumors and brain were studied by electron microscopy. Tumor microvessel wall was thicker than that of brain. ENU and normal brain microvessels were continuous and nonfenestrated. RG-2 microvessels contained fenestrations and endothelial gaps; the latter had a maximum major axis of 3.0 microm. Based on anatomic measurements, the pore area of RG-2 tumors was estimated at 7.4 x 10(-6) cm(2) g(-1) from fenestrations and 3.5 x 10(-5) cm(2) g(-1) from endothelial gaps. Increased permeability of RG-2 microvessels to macromolecules is most likely attributable to endothelial gaps. Three microvessel populations may occur in brain tumors: (1) continuous nonfenestrated, (2) continuous fenestrated, and (3) discontinuous (with or without fenestrations). The first group may be unique to brain tumors; the latter two are similar to microvessels found in systemic tumors. Since structure-function properties of brain tumor microvessels will affect drug delivery, studies of microvessel function should be incorporated into clinical trials of brain tumor therapy, especially those using macromolecules. Copyright 1999 Academic Press.

  14. Radiation therapy of 9L rat brain tumors

    International Nuclear Information System (INIS)

    Henderson, S.D.; Kimler, B.F.; Morantz, R.A.

    1981-01-01

    The effects of radiation therapy on normal rats and on rats burdened with 9L brain tumors have been studied. The heads of normal rats were x-irradiated with single exposures ranging from 1000 R to 2700 R. Following acute exposures greater than 2100 R, all animals died in 8 to 12 days. Approximately 30% of the animals survived beyond 12 days over the range of 1850 to 1950 R; following exposures less than 1850 R, all animals survived the acute radiation effects, and median survival times increased with decreasing exposure. Three fractionated radiation schedules were also studied: 2100 R or 3000 R in 10 equal fractions, and 3000 R in 6 equal fractions, each schedule being administered over a 2 week period. The first schedule produced a MST of greater than 1 1/2 years; the other schedules produced MSTs that were lower. It was determined that by applying a factor of 1.9, similar survival responses of normal rats were obtained with single as with fractionated radiation exposures. Animals burdened with 9L gliosarcoma brain tumors normally died of the disease process within 18 to 28 days ater tumor inoculation. Both single and fractionated radiation therapy resulted in a prolongation of survival of tumor-burdened rats. This prolongation was found to be linearly dependent upon the dose; but only minimally dependent upon the time after inoculation at which therapy was initiated, or upon the fractionation schedule that was used. As with normal animals, similar responses were obtained with single as with fractionated exposures when a factor (1.9) was applied. All tumor-bearing animals died prior to the time that death was observed in normal, irradiated rats. Thus, the 9L gliosarcoma rat brain tumor model can be used for the pre-clinical experimental investigation of new therapeutic schedules involving radiation therapy and adjuvant therapies

  15. Radiosurgery in the management of pediatric brain tumors

    International Nuclear Information System (INIS)

    Hodgson, David C.; Goumnerova, Liliana C.; Loeffler, Jay S.; Dutton, Sharon; Black, Peter McL; Alexander, Eben; Xu Ronghui; Kooy, Hanne; Silver, Barbara; Tarbell, Nancy J.

    2001-01-01

    Objective: To describe the outcome of pediatric brain tumor patients following stereotactic radiosurgery (SRS), and factors associated with progression-free survival. Methods: We reviewed the outcome of 90 children treated with SRS for recurrent (n=62) or residual (n=28) brain tumors over a 10-year period. Median follow-up from SRS was 24 months for all patients and 55.5 months for the 34 patients currently alive. Results: The median progression-free survival (PFS) for all patients was 13 months. Median PFS according to tumor histology was medulloblastoma = 11 months, ependymoma 8.5 months, glioblastoma and anaplastic astrocytoma = 12 months. Median PFS in patients treated to a single lesion was 15.4 months. No patient undergoing SRS to more than 1 lesion survived disease free beyond 2 years. After adjusting for histology and other clinical factors, SRS for tumor recurrence (RR=2.49) and the presence of > 1 lesion (RR=2.3) were associated with a significantly increased rate of progression (p<0.05). Three-year actuarial local control (LC) was as follows: medulloblastoma = 57%, ependymoma = 29%, anaplastic astrocytoma/glioblastoma = 60%, other histologies = 56%. Nineteen patients with radionecrosis and progressive neurologic symptoms underwent reoperation after an interval of 0.6-62 months following SRS. Pathology revealed necrosis with no evidence of tumor in 9 of these cases. Conclusion: SRS can be given safely to selected children with brain tumors. SRS appears to reduce the proportion of first failures occurring locally and is associated with better outcome when given as a part of initial management. Some patients with unresectable relapsed disease can be salvaged with SRS. SRS to multiple lesions does not appear to be curative. Serious neurologic symptoms requiring reoperation is infrequently caused by radionecrosis alone

  16. Technological progress in radiation therapy for brain tumors

    LENUS (Irish Health Repository)

    Vernimmen, Frederik Jozef

    2014-01-01

    To achieve a good therapeutic ratio the radiation dose to the tumor should be as high as possible with the lowest possible dose to the surrounding normal tissue. This is especially the case for brain tumors. Technological ad- vancements in diagnostic imaging, dose calculations, and radiation delivery systems, combined with a better un- derstanding of the pathophysiology of brain tumors have led to improvements in the therapeutic results. The widely used technology of delivering 3-D conformal therapy with photon beams (gamma rays) produced by Li-near Accelerators has progressed into the use of Intensity modulated radiation therapy (IMRT). Particle beams have been used for several decades for radiotherapy because of their favorable depth dose characteristics. The introduction of clinically dedicated proton beam therapy facilities has improved the access for cancer patients to this treatment. Proton therapy is of particular interest for pediatric malignancies. These technical improvements are further enhanced by the evolution in tumor physiology imaging which allows for improved delineation of the tumor. This in turn opens the potential to adjust the radiation dose to maximize the radiobiological effects. The advances in both imaging and radiation therapy delivery will be discussed.

  17. Collecting and Storing Blood and Brain Tumor Tissue Samples From Children With Brain Tumors

    Science.gov (United States)

    2017-12-11

    Childhood Atypical Teratoid/Rhabdoid Tumor; Childhood Central Nervous System Germ Cell Tumor; Childhood Choroid Plexus Tumor; Childhood Craniopharyngioma; Childhood Grade I Meningioma; Childhood Grade II Meningioma; Childhood Grade III Meningioma; Childhood High-grade Cerebral Astrocytoma; Childhood Infratentorial Ependymoma; Childhood Low-grade Cerebral Astrocytoma; Childhood Oligodendroglioma; Childhood Supratentorial Ependymoma; Newly Diagnosed Childhood Ependymoma; Recurrent Childhood Cerebellar Astrocytoma; Recurrent Childhood Cerebral Astrocytoma; Recurrent Childhood Ependymoma; Recurrent Childhood Medulloblastoma; Recurrent Childhood Supratentorial Primitive Neuroectodermal Tumor; Recurrent Childhood Visual Pathway and Hypothalamic Glioma; Recurrent Childhood Visual Pathway Glioma

  18. Adverse effect after external radiotherapy for brain tumors

    International Nuclear Information System (INIS)

    Yoshii, Yoshihiko; Takano, Shingo; Yanaka, Kiyoyuki

    1989-01-01

    This report discusses the effects on normal brain tissue of radiotherapy in relation to age and irradiation dose as determined from whole-brain sections of the autopsied brains with tumors. Twenty four patients (7 glioblastomas, 2 benign gliomas, 12 brain metastases, 2 malignant lymphomas, and 1 pituitary adenoma) older than 65 years (aged), and 17 younger than 65 years (non-aged) were treated by cobalt- or linear accelerator radiotherapy. Nine patients without brain disease (4 aged and 5 non-aged) were used as a control group. The histological findings were evaluated by grading the small and capillary vessels, fibrinoid necrosis, and myelination in the white matter in whole-brain sections. Those findings were compared to the irradiation doses within all radiation fields in whole-brain sections corresponding to CT scans. Hyalinization of the small vessels was observed within the postradiation 12 months in fields exposed to total doses of less than 800 neuret. Hyalinization of the capillary vessels was greater in the irradiated group than in the control group. Demyelination was observed within the postradiation 12 months in fields irradiated by more than 800 neuret in aged patients and in fields irradiated by less than 800 neuret in non-aged patients. Fibrinoid necrosis was observed after the post-radiation 12 months in fields irradiated by less than 800 neuret in aged patients and in fields irradiated by more than 800 neuret in non-aged patients. It is worth noting that in non-aged patients with brain tumors, adverse effects of radiotherapy on vessels and parenchyma were very high even in low-dose radiation areas; and in aged patients fibrinoid necrosis, which indicates irreversible damage of vessels, was observed in low-dose radiation areas. (author)

  19. Brain congenital tumors of atypical presentation. Tumores cerebrales congenitos de presentacion atipica

    Energy Technology Data Exchange (ETDEWEB)

    Borden Ferre, F.; Menor Serrano, F.; Martinez Fernandez, M.; Moreno Flores, A.; Poyatos, C. (Hospital La Fe. Valencia (Spain))

    1994-01-01

    We present four cases of brain tumor within the first year of life, with atypical clinical and radiological onset. Two astrocytomas of the visual pathway presented with visual changes without involving the ventricular system. The other two, not histologically confirmed, were located in the medial portion of the temporal lobe, the first sign of which was a cyanotic crisis.

  20. Automatic outer and inner breast tissue segmentation using multi-parametric MRI images of breast tumor patients.

    Science.gov (United States)

    Thakran, Snekha; Chatterjee, Subhajit; Singhal, Meenakshi; Gupta, Rakesh Kumar; Singh, Anup

    2018-01-01

    The objectives of the study were to develop a framework for automatic outer and inner breast tissue segmentation using multi-parametric MRI images of the breast tumor patients; and to perform breast density and tumor tissue analysis. MRI of the breast was performed on 30 patients at 3T-MRI. T1, T2 and PD-weighted(W) images, with and without fat saturation(WWFS), and dynamic-contrast-enhanced(DCE)-MRI data were acquired. The proposed automatic segmentation approach was performed in two steps. In step-1, outer segmentation of breast tissue from rest of body parts was performed on structural images (T2-W/T1-W/PD-W without fat saturation images) using automatic landmarks detection technique based on operations like profile screening, Otsu thresholding, morphological operations and empirical observation. In step-2, inner segmentation of breast tissue into fibro-glandular(FG), fatty and tumor tissue was performed. For validation of breast tissue segmentation, manual segmentation was carried out by two radiologists and similarity coefficients(Dice and Jaccard) were computed for outer as well as inner tissues. FG density and tumor volume were also computed and analyzed. The proposed outer and inner segmentation approach worked well for all the subjects and was validated by two radiologists. The average Dice and Jaccard coefficients value for outer segmentation using T2-W images, obtained by two radiologists, were 0.977 and 0.951 respectively. These coefficient values for FG tissue were 0.915 and 0.875 respectively whereas for tumor tissue, values were 0.968 and 0.95 respectively. The volume of segmented tumor ranged over 2.1 cm3-7.08 cm3. The proposed approach provided automatic outer and inner breast tissue segmentation, which enables automatic calculations of breast tissue density and tumor volume. This is a complete framework for outer and inner breast segmentation method for all structural images.

  1. Automatic outer and inner breast tissue segmentation using multi-parametric MRI images of breast tumor patients.

    Directory of Open Access Journals (Sweden)

    Snekha Thakran

    Full Text Available The objectives of the study were to develop a framework for automatic outer and inner breast tissue segmentation using multi-parametric MRI images of the breast tumor patients; and to perform breast density and tumor tissue analysis. MRI of the breast was performed on 30 patients at 3T-MRI. T1, T2 and PD-weighted(W images, with and without fat saturation(WWFS, and dynamic-contrast-enhanced(DCE-MRI data were acquired. The proposed automatic segmentation approach was performed in two steps. In step-1, outer segmentation of breast tissue from rest of body parts was performed on structural images (T2-W/T1-W/PD-W without fat saturation images using automatic landmarks detection technique based on operations like profile screening, Otsu thresholding, morphological operations and empirical observation. In step-2, inner segmentation of breast tissue into fibro-glandular(FG, fatty and tumor tissue was performed. For validation of breast tissue segmentation, manual segmentation was carried out by two radiologists and similarity coefficients(Dice and Jaccard were computed for outer as well as inner tissues. FG density and tumor volume were also computed and analyzed. The proposed outer and inner segmentation approach worked well for all the subjects and was validated by two radiologists. The average Dice and Jaccard coefficients value for outer segmentation using T2-W images, obtained by two radiologists, were 0.977 and 0.951 respectively. These coefficient values for FG tissue were 0.915 and 0.875 respectively whereas for tumor tissue, values were 0.968 and 0.95 respectively. The volume of segmented tumor ranged over 2.1 cm3-7.08 cm3. The proposed approach provided automatic outer and inner breast tissue segmentation, which enables automatic calculations of breast tissue density and tumor volume. This is a complete framework for outer and inner breast segmentation method for all structural images.

  2. Automatic outer and inner breast tissue segmentation using multi-parametric MRI images of breast tumor patients

    Science.gov (United States)

    Singhal, Meenakshi; Gupta, Rakesh Kumar

    2018-01-01

    The objectives of the study were to develop a framework for automatic outer and inner breast tissue segmentation using multi-parametric MRI images of the breast tumor patients; and to perform breast density and tumor tissue analysis. MRI of the breast was performed on 30 patients at 3T-MRI. T1, T2 and PD-weighted(W) images, with and without fat saturation(WWFS), and dynamic-contrast-enhanced(DCE)-MRI data were acquired. The proposed automatic segmentation approach was performed in two steps. In step-1, outer segmentation of breast tissue from rest of body parts was performed on structural images (T2-W/T1-W/PD-W without fat saturation images) using automatic landmarks detection technique based on operations like profile screening, Otsu thresholding, morphological operations and empirical observation. In step-2, inner segmentation of breast tissue into fibro-glandular(FG), fatty and tumor tissue was performed. For validation of breast tissue segmentation, manual segmentation was carried out by two radiologists and similarity coefficients(Dice and Jaccard) were computed for outer as well as inner tissues. FG density and tumor volume were also computed and analyzed. The proposed outer and inner segmentation approach worked well for all the subjects and was validated by two radiologists. The average Dice and Jaccard coefficients value for outer segmentation using T2-W images, obtained by two radiologists, were 0.977 and 0.951 respectively. These coefficient values for FG tissue were 0.915 and 0.875 respectively whereas for tumor tissue, values were 0.968 and 0.95 respectively. The volume of segmented tumor ranged over 2.1 cm3–7.08 cm3. The proposed approach provided automatic outer and inner breast tissue segmentation, which enables automatic calculations of breast tissue density and tumor volume. This is a complete framework for outer and inner breast segmentation method for all structural images. PMID:29320532

  3. Numerical Simulations of MREIT Conductivity Imaging for Brain Tumor Detection

    Science.gov (United States)

    Meng, Zi Jun; Sajib, Saurav Z. K.; Chauhan, Munish; Sadleir, Rosalind J.; Kim, Hyung Joong; Kwon, Oh In; Woo, Eung Je

    2013-01-01

    Magnetic resonance electrical impedance tomography (MREIT) is a new modality capable of imaging the electrical properties of human body using MRI phase information in conjunction with external current injection. Recent in vivo animal and human MREIT studies have revealed unique conductivity contrasts related to different physiological and pathological conditions of tissues or organs. When performing in vivo brain imaging, small imaging currents must be injected so as not to stimulate peripheral nerves in the skin, while delivery of imaging currents to the brain is relatively small due to the skull's low conductivity. As a result, injected imaging currents may induce small phase signals and the overall low phase SNR in brain tissues. In this study, we present numerical simulation results of the use of head MREIT for brain tumor detection. We used a realistic three-dimensional head model to compute signal levels produced as a consequence of a predicted doubling of conductivity occurring within simulated tumorous brain tissues. We determined the feasibility of measuring these changes in a time acceptable to human subjects by adding realistic noise levels measured from a candidate 3 T system. We also reconstructed conductivity contrast images, showing that such conductivity differences can be both detected and imaged. PMID:23737862

  4. Capsulated Metallic Debris Tumor Mass Mimicking Adjacent Segment Disease: A Case Report.

    Science.gov (United States)

    Li, Yi-Chen; Yang, Shih-Chieh; Hsu, Chao-Tien; Tu, Yuan-Kun

    2016-12-01

    A case report. To inform the spine surgeons another cause of late complications after instrumented spinal fusion surgery. Posterior lumbar instrumented fusion has been widely applied as an effective procedure for treating patients with degenerative lumbar spine disease. The development of pathology at the mobile segment adjacent to the lumbar spinal fusion has been termed as adjacent segment disease. Most patients with adjacent segment disease present with recurrent back pain, sciatica, intermittent claudication, or even muscle weakness. Herein, we report the case of a 58-year-old man with posterior lumbar instrumented fusion at L4-L5 who complained of recurrent neurological symptoms mimicking adjacent instability and stenosis. In addition to severe adjacent stenosis at L3-L4, preoperative magnetic resonance imaging showed an intraspinal extradural tumor-like mass with compression of the neurological elements. The well-capsulated tumor mass was gently dissected and meticulously excised without injury to the adhesive dura or nerve roots. The tumor specimen was fixed in formalin, and then decalcified and tinted using several special stains, which conformed metallic wear debris, resulting in foreign body reaction. The metallic wear particulates may initiate a cascade of immune and inflammatory responses. Therefore, attention should be paid to patients who are found to have loosening of the implants at the metal-metal or metal-bone interface.

  5. Awake Craniotomy for Tumor Resection: Further Optimizing Therapy of Brain Tumors.

    Science.gov (United States)

    Mehdorn, H Maximilian; Schwartz, Felix; Becker, Juliane

    2017-01-01

    In recent years more and more data have emerged linking the most radical resection to prolonged survival in patients harboring brain tumors. Since total tumor resection could increase postoperative morbidity, many methods have been suggested to reduce the risk of postoperative neurological deficits: awake craniotomy with the possibility of continuous patient-surgeon communication is one of the possibilities of finding out how radical a tumor resection can possibly be without causing permanent harm to the patient.In 1994 we started to perform awake craniotomy for glioma resection. In 2005 the use of intraoperative high-field magnetic resonance imaging (MRI) was included in the standard tumor therapy protocol. Here we review our experience in performing awake surgery for gliomas, gained in 219 patients.Patient selection by the operating surgeon and a neuropsychologist is of primary importance: the patient should feel as if they are part of the surgical team fighting against the tumor. The patient will undergo extensive neuropsychological testing, functional MRI, and fiber tractography in order to define the relationship between the tumor and the functionally relevant brain areas. Attention needs to be given at which particular time during surgery the intraoperative MRI is performed. Results from part of our series (without and with ioMRI scan) are presented.

  6. Peritumoral hemorrhage after radiosurgery for metastatic brain tumor

    International Nuclear Information System (INIS)

    Motozaki, Takahiko; Ban, Sadahiko; Yamamoto, Toyoshiro; Hamasaki, Masatake.

    1994-01-01

    An unusual case of peritumoral hemorrhage after radiosurgery for the treatment of metastatic brain tumor is reported. This 64-year-old woman had a history of breast cancer and underwent right mastectomy in 1989. She remained well until January 1993, when she started to have headache, nausea and speech disturbance, and was hospitalized on February 25, 1993. Neurological examination disclosed right hemiparesis and bilateral papilledema. CT scan and MR imaging showed a solitary round mass lesion in the left basal ganglia region. It was a well-demarcated, highly enhanced mass, 37 mm in diameter. Cerebral angiography confirmed a highly vascular mass lesion in the same location. She was treated with radiosurgery on March 8 (maximum dose was 20 Gy in the center and 10 Gy in the peripheral part of the tumor). After radiosurgery, she had an uneventful course and clinical and radiosurgical improvement could be detected. Her neurological symptoms and signs gradually improved and reduction of the tumor size and perifocal edema could be seen one month after radiosurgery. However, 6 weeks after radiosurgery, she suddenly developed semicoma and right hemiplegia. CT scan disclosed a massive peritumoral hemorrhage. Then, emergency craniotomy, evacuation of the hematoma and total removal of the tumor were performed on April 24. Histopathological diagnosis was adenocarcinoma. It was the same finding as that of the previous breast cancer. Histopathological examination revealed necrosis without tumor cells in the center and residual tumor cells in the peripheral part of the tumor. It is postulated that peritumoral hemorrhage was caused by hemodynamic changes in the vascular-rich tumor after radiosurgery and breakdown of the fragile abnormal vessels in the peripheral part of the tumor. (author)

  7. Follow-up segmentation of lung tumors in PET and CT data

    Science.gov (United States)

    Opfer, Roland; Kabus, Sven; Schneider, Torben; Carlsen, Ingwer C.; Renisch, Steffen; Sabczynski, Jörg

    2009-02-01

    Early response assessment of cancer therapy is a crucial component towards a more effective and patient individualized cancer therapy. Integrated PET/CT systems provide the opportunity to combine morphologic with functional information. We have developed algorithms which allow the user to track both tumor volume and standardized uptake value (SUV) measurements during the therapy from series of CT and PET images, respectively. To prepare for tumor volume estimation we have developed a new technique for a fast, flexible, and intuitive 3D definition of meshes. This initial surface is then automatically adapted by means of a model-based segmentation algorithm and propagated to each follow-up scan. If necessary, manual corrections can be added by the user. To determine SUV measurements a prioritized region growing algorithm is employed. For an improved workflow all algorithms are embedded in a PET/CT therapy monitoring software suite giving the clinician a unified and immediate access to all data sets. Whenever the user clicks on a tumor in a base-line scan, the courses of segmented tumor volumes and SUV measurements are automatically identified and displayed to the user as a graph plot. According to each course, the therapy progress can be classified as complete or partial response or as progressive or stable disease. We have tested our methods with series of PET/CT data from 9 lung cancer patients acquired at Princess Margaret Hospital in Toronto. Each patient underwent three PET/CT scans during a radiation therapy. Our results indicate that a combination of mean metabolic activity in the tumor with the PET-based tumor volume can lead to an earlier response detection than a purely volume based (CT diameter) or purely functional based (e.g. SUV max or SUV mean) response measures. The new software seems applicable for easy, faster, and reproducible quantification to routinely monitor tumor therapy.

  8. Rapid fully automatic segmentation of subcortical brain structures by shape-constrained surface adaptation.

    Science.gov (United States)

    Wenzel, Fabian; Meyer, Carsten; Stehle, Thomas; Peters, Jochen; Siemonsen, Susanne; Thaler, Christian; Zagorchev, Lyubomir

    2018-03-09

    This work presents a novel approach for the rapid segmentation of clinically relevant subcortical brain structures in T1-weighted MRI by utilizing a shape-constrained deformable surface model. In contrast to other approaches for segmenting brain structures, its design allows for parallel segmentation of individual brain structures within a flexible and robust hierarchical framework such that accurate adaptation and volume computation can be achieved within a minute of processing time. Furthermore, adaptation is driven by local and not global contrast, potentially relaxing requirements with respect to preprocessing steps such as bias-field correction. Detailed evaluation experiments on more than 1000 subjects, including comparisons to FSL FIRST and FreeSurfer as well as a clinical assessment, demonstrate high accuracy and test-retest consistency of the presented segmentation approach, leading, for example, to an average segmentation error of less than 0.5 mm. The presented approach might be useful in both, research as well as clinical routine, for automated segmentation and volume quantification of subcortical brain structures in order to increase confidence in the diagnosis of neuro-degenerative disorders, such as Alzheimer's disease, Parkinson's disease, Multiple Sclerosis, or clinical applications for other neurologic and psychiatric diseases. Copyright © 2018 Elsevier B.V. All rights reserved.

  9. Deep convolutional neural networks for multi-modality isointense infant brain image segmentation.

    Science.gov (United States)

    Zhang, Wenlu; Li, Rongjian; Deng, Houtao; Wang, Li; Lin, Weili; Ji, Shuiwang; Shen, Dinggang

    2015-03-01

    The segmentation of infant brain tissue images into white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) plays an important role in studying early brain development in health and disease. In the isointense stage (approximately 6-8 months of age), WM and GM exhibit similar levels of intensity in both T1 and T2 MR images, making the tissue segmentation very challenging. Only a small number of existing methods have been designed for tissue segmentation in this isointense stage; however, they only used a single T1 or T2 images, or the combination of T1 and T2 images. In this paper, we propose to use deep convolutional neural networks (CNNs) for segmenting isointense stage brain tissues using multi-modality MR images. CNNs are a type of deep models in which trainable filters and local neighborhood pooling operations are applied alternatingly on the raw input images, resulting in a hierarchy of increasingly complex features. Specifically, we used multi-modality information from T1, T2, and fractional anisotropy (FA) images as inputs and then generated the segmentation maps as outputs. The multiple intermediate layers applied convolution, pooling, normalization, and other operations to capture the highly nonlinear mappings between inputs and outputs. We compared the performance of our approach with that of the commonly used segmentation methods on a set of manually segmented isointense stage brain images. Results showed that our proposed model significantly outperformed prior methods on infant brain tissue segmentation. In addition, our results indicated that integration of multi-modality images led to significant performance improvement. Copyright © 2014 Elsevier Inc. All rights reserved.

  10. Irradiated large segment allografts in limb saving surgery for extremity tumor - Philippine experience

    International Nuclear Information System (INIS)

    Wang, E.H.M.; Agcaoili, N.; Turqueza, M.S.

    1999-01-01

    Limb saving surgery has only recently become an option in the Phillipines. This has given a better comprehension of oncologic principles and from the refinement of bone-reconstruction procedures. Foremost among the latter is the use of large segment bone allografts. Large-segment allografts (LSA) are available from the Tissue and Bone Bank of the University of the Philippines (UP). After harvest, these bones are processed at the Bank, radiation-sterilized at the Philippine Nuclear Research Institute, and then stored in a -80 degree C deep freezer. We present our 4-year experience (Jan 93 - Dec 96) with LSA for limb saving surgery in musculoskeletal tumors. All patients included had: (1) malignant or aggressive extremity tumors; (2) surgery performed by the UP - Musculoskeletal Tumor Unit (UP-MUST Unit); (3) reconstructions utilizing irradiated large-segment allografts from the UP Tissue and Bone Bank; and (4) follow-up of at least one year or until death. Tumors included osteosarcoma (6) giant cell tumors (11), and metastatic lesions (3). Age ranged from 16-64 years old; 13 males and 7 females. Bones involved were the femur (12) tibia (5) and humerus (3). Average defect length was 15 cm and surgeries performed were intercalary replacement (5), resection arthrodesis (11), hemicondylar allograft (3), and allograft-prosthesis-composite (1). Follow-up ranged was from 17- 60 months or until death. Fifteen (1 5) were alive with NED (no evidence of disease), 3 were dead (2 of disease 1 of other causes), and 2 were AWED (alive with evidence of disease). Functional evaluation using the criteria of the International Society of Limb Salvage (ISOLS) was performed on 18 patients. This averaged 27.5 out of 30 points (92%) for 15 patients. Many having returned to their previous work and recreation. The 3 failures were due to infections in 2 cases (both of whom opted for amputations but who have not been fit with prostheses), and a fracture (secondary to a fall) in one case. Limb

  11. Late sequelae in children treated for brain tumors and leukemia

    International Nuclear Information System (INIS)

    Jereb, B.; Petric-Grabnar, G.; Zadravec-Zaletel, L.; Korenjak, R.; Krzisnik, C.; Anzic, J.; Stare, J.

    1994-01-01

    Forty-two survivors treated at an age of 2-16 years for brain tumors or leukemia were, 4-21 years after treatment, subjected to an extensive follow-up investigation, including physical examination and interview; 35 of them also had endocrinological and 33 psychological evaluation. Hormonal deficiencies were found in about two-thirds of patients and were most common in those treated for brain tumors. The great majority had verbal intelligence quotient (VIQ) within normal range. Also, the performance intelligence quotients (PIQ) were normal in most patients. However, the results suggested that the primary intellectual capacity in children treated for cancer was not being fully utilized, their PIQ being on the average higher than their VIQ; this tendency was especially pronounced in the leukemia patients. (orig.)

  12. Extracellular Vesicles in Brain Tumors and Neurodegenerative Diseases

    Directory of Open Access Journals (Sweden)

    Federica Ciregia

    2017-08-01

    Full Text Available Extracellular vesicles (EVs can be classified into apoptotic bodies, microvesicles (MVs, and exosomes, based on their origin or size. Exosomes are the smallest and best characterized vesicles which derived from the endosomal system. These vesicles are released from many different cell types including neuronal cells and their functions in the nervous system are investigated. They have been proposed as novel means for intercellular communication, which takes part not only to the normal neuronal physiology but also to the transmission of pathogenic proteins. Indeed, exosomes are fundamental to assemble and transport proteins during development, but they can also transfer neurotoxic misfolded proteins in pathogenesis. The present review will focus on their roles in neurological diseases, specifically brain tumors, such as glioblastoma (GBM, neuroblastoma (NB, medulloblastoma (MB, and metastatic brain tumors and chronic neurodegenerative diseases, such as Alzheimer, Parkinson, multiple sclerosis (MS, amyotrophic lateral sclerosis (ALS, Huntington, and Prion diseseases highlighting their involvement in spreading neurotoxicity, in therapeutics, and in pathogenesis.

  13. Specific features of epilepsy in children with brain tumors

    Directory of Open Access Journals (Sweden)

    G. V. Kalmykova

    2015-01-01

    Full Text Available Objective: to study the specific features of epilepsy in children and adolescents with brain tumors and to define the optimal tactics of management and antiepileptic therapy after surgical treatment. Patients and methods. Sixty-one patients aged 5 months to 15 years were examined. All the patients were diagnosed as having a brain tumor found in the presence of symptomatic epilepsy. They were all followed up for 5 years postsurgery or during their lifetime (in case of death. Comprehensive examination encompassing the assessment of history data and concomitant complaints, brain magnetic resonance imaging, video-EEC monitoring, and the neurological status (the presence of cognitive impairments and eye ground changes was done in all the cases. The probability of epileptic seizures in the clinical presentation of the disease, their semiology, and frequency were studied. Results and discussion. Epileptic seizures were the major complaint in all the patients at the first visit to their doctor. The disease occurred with status epilepticus in 9% of the patients. Different types of generalized seizures were more common (53%; p≥0.05. The tumor was located above the tentorium of the cerebellum in most examinees (77% and beneath it in the others (23%; p≤0.05. The significant clinical sign of a brain tumor in the epileptic children is focal neurological symptoms (72% of the cases. MRI was performed in children who had no focal neurological symptoms in the late periods. There was cerebrospinal fluid hypertension in 51% of the patients (p≥0.05 and cognitive impairments in 33% (p<0.05. The maximum number (74% of children with psycho-speech disorders and cognitive impairments were registered in the age group of 7–15 years. Eye ground changes characteristic of intracranial hypertension were identified in 19 epileptic children; they occurred in 27 patients more than 1 year after the onset of seizures. The late (few months-to-14 years diagnosis of a brain

  14. Automatic tissue segmentation of neonate brain MR Images with subject-specific atlases

    Science.gov (United States)

    Cherel, Marie; Budin, Francois; Prastawa, Marcel; Gerig, Guido; Lee, Kevin; Buss, Claudia; Lyall, Amanda; Zaldarriaga Consing, Kirsten; Styner, Martin

    2015-03-01

    Automatic tissue segmentation of the neonate brain using Magnetic Resonance Images (MRI) is extremely important to study brain development and perform early diagnostics but is challenging due to high variability and inhomogeneity in contrast throughout the image due to incomplete myelination of the white matter tracts. For these reasons, current methods often totally fail or give unsatisfying results. Furthermore, most of the subcortical midbrain structures are misclassified due to a lack of contrast in these regions. We have developed a novel method that creates a probabilistic subject-specific atlas based on a population atlas currently containing a number of manually segmented cases. The generated subject-specific atlas is sharp and adapted to the subject that is being processed. We then segment brain tissue classes using the newly created atlas with a single-atlas expectation maximization based method. Our proposed method leads to a much lower failure rate in our experiments. The overall segmentation results are considerably improved when compared to using a non-subject-specific, population average atlas. Additionally, we have incorporated diffusion information obtained from Diffusion Tensor Images (DTI) to improve the detection of white matter that is not visible at this early age in structural MRI (sMRI) due to a lack of myelination. Although this necessitates the acquisition of an additional sequence, the diffusion information improves the white matter segmentation throughout the brain, especially for the mid-brain structures such as the corpus callosum and the internal capsule.

  15. Heavy Metals and Epigenetic Alterations in Brain Tumors

    OpenAIRE

    Caffo, Maria; Caruso, Gerardo; Fata, Giuseppe La; Barresi, Valeria; Visalli, Maria; Venza, Mario; Venza, Isabella

    2014-01-01

    Heavy metals and their derivatives can cause various diseases. Numerous studies have evaluated the possible link between exposure to heavy metals and various cancers. Recent data show a correlation between heavy metals and aberration of genetic and epigenetic patterns. From a literature search we noticed few experimental and epidemiological studies that evaluate a possible correlation between heavy metals and brain tumors. Gliomas arise due to genetic and epigenetic alterations of glial cells...

  16. Combined chemotherapy and immunotherapy against experimental malignant brain tumors

    OpenAIRE

    Fritzell, Sara

    2013-01-01

    Glioblastoma multiforme (GBM) is the most common and aggressive primary malignant brain tumor in adults. Despite standard treatment including surgery, radiotherapy and temozolomide (TMZ)-based chemotherapy, the prognosis for GBM patients is dismal, and there is a need for novel treatments. One possible therapeutic treatment modality presented here is immunotherapy, either alone or combined with intratumoral TMZ. In this doctoral thesis, I report enhanced cure of rats and mice with mal...

  17. Telomere length modulation in human astroglial brain tumors.

    Directory of Open Access Journals (Sweden)

    Domenico La Torre

    Full Text Available BACKGROUND: Telomeres alteration during carcinogenesis and tumor progression has been described in several cancer types. Telomeres length is stabilized by telomerase (h-TERT and controlled by several proteins that protect telomere integrity, such as the Telomere Repeat-binding Factor (TRF 1 and 2 and the tankyrase-poli-ADP-ribose polymerase (TANKs-PARP complex. OBJECTIVE: To investigate telomere dysfunction in astroglial brain tumors we analyzed telomeres length, telomerase activity and the expression of a panel of genes controlling the length and structure of telomeres in tissue samples obtained in vivo from astroglial brain tumors with different grade of malignancy. MATERIALS AND METHODS: Eight Low Grade Astrocytomas (LGA, 11 Anaplastic Astrocytomas (AA and 11 Glioblastoma Multiforme (GBM samples were analyzed. Three samples of normal brain tissue (NBT were used as controls. Telomeres length was assessed through Southern Blotting. Telomerase activity was evaluated by a telomere repeat amplification protocol (TRAP assay. The expression levels of TRF1, TRF2, h-TERT and TANKs-PARP complex were determined through Immunoblotting and RT-PCR. RESULTS: LGA were featured by an up-regulation of TRF1 and 2 and by shorter telomeres. Conversely, AA and GBM were featured by a down-regulation of TRF1 and 2 and an up-regulation of both telomerase and TANKs-PARP complex. CONCLUSIONS: In human astroglial brain tumours, up-regulation of TRF1 and TRF2 occurs in the early stages of carcinogenesis determining telomeres shortening and genomic instability. In a later stage, up-regulation of PARP-TANKs and telomerase activation may occur together with an ADP-ribosylation of TRF1, causing a reduced ability to bind telomeric DNA, telomeres elongation and tumor malignant progression.

  18. Novel active contour model based on multi-variate local Gaussian distribution for local segmentation of MR brain images

    Science.gov (United States)

    Zheng, Qiang; Li, Honglun; Fan, Baode; Wu, Shuanhu; Xu, Jindong

    2017-12-01

    Active contour model (ACM) has been one of the most widely utilized methods in magnetic resonance (MR) brain image segmentation because of its ability of capturing topology changes. However, most of the existing ACMs only consider single-slice information in MR brain image data, i.e., the information used in ACMs based segmentation method is extracted only from one slice of MR brain image, which cannot take full advantage of the adjacent slice images' information, and cannot satisfy the local segmentation of MR brain images. In this paper, a novel ACM is proposed to solve the problem discussed above, which is based on multi-variate local Gaussian distribution and combines the adjacent slice images' information in MR brain image data to satisfy segmentation. The segmentation is finally achieved through maximizing the likelihood estimation. Experiments demonstrate the advantages of the proposed ACM over the single-slice ACM in local segmentation of MR brain image series.

  19. Automated fetal brain segmentation from 2D MRI slices for motion correction.

    Science.gov (United States)

    Keraudren, K; Kuklisova-Murgasova, M; Kyriakopoulou, V; Malamateniou, C; Rutherford, M A; Kainz, B; Hajnal, J V; Rueckert, D

    2014-11-01

    Motion correction is a key element for imaging the fetal brain in-utero using Magnetic Resonance Imaging (MRI). Maternal breathing can introduce motion, but a larger effect is frequently due to fetal movement within the womb. Consequently, imaging is frequently performed slice-by-slice using single shot techniques, which are then combined into volumetric images using slice-to-volume reconstruction methods (SVR). For successful SVR, a key preprocessing step is to isolate fetal brain tissues from maternal anatomy before correcting for the motion of the fetal head. This has hitherto been a manual or semi-automatic procedure. We propose an automatic method to localize and segment the brain of the fetus when the image data is acquired as stacks of 2D slices with anatomy misaligned due to fetal motion. We combine this segmentation process with a robust motion correction method, enabling the segmentation to be refined as the reconstruction proceeds. The fetal brain localization process uses Maximally Stable Extremal Regions (MSER), which are classified using a Bag-of-Words model with Scale-Invariant Feature Transform (SIFT) features. The segmentation process is a patch-based propagation of the MSER regions selected during detection, combined with a Conditional Random Field (CRF). The gestational age (GA) is used to incorporate prior knowledge about the size and volume of the fetal brain into the detection and segmentation process. The method was tested in a ten-fold cross-validation experiment on 66 datasets of healthy fetuses whose GA ranged from 22 to 39 weeks. In 85% of the tested cases, our proposed method produced a motion corrected volume of a relevant quality for clinical diagnosis, thus removing the need for manually delineating the contours of the brain before motion correction. Our method automatically generated as a side-product a segmentation of the reconstructed fetal brain with a mean Dice score of 93%, which can be used for further processing. Copyright

  20. Straight trajectory planning for keyhole neurosurgery in sheep with automatic brain structures segmentation

    Science.gov (United States)

    Favaro, Alberto; Lad, Akash; Formenti, Davide; Zani, Davide Danilo; De Momi, Elena

    2017-03-01

    In a translational neuroscience/neurosurgery perspective, sheep are considered good candidates to study because of the similarity between their brain and the human one. Automatic planning systems for safe keyhole neurosurgery maximize the probe/catheter distance from vessels and risky structures. This work consists in the development of a trajectories planner for straight catheters placement intended to be used for investigating the drug diffusivity mechanisms in sheep brain. Automatic brain segmentation of gray matter, white matter and cerebrospinal fluid is achieved using an online available sheep atlas. Ventricles, midbrain and cerebellum segmentation have been also carried out. The veterinary surgeon is asked to select a target point within the white matter to be reached by the probe and to define an entry area on the brain cortex. To mitigate the risk of hemorrhage during the insertion process, which can prevent the success of the insertion procedure, the trajectory planner performs a curvature analysis of the brain cortex and wipes out from the poll of possible entry points the sulci, as part of brain cortex where superficial blood vessels are naturally located. A limited set of trajectories is then computed and presented to the surgeon, satisfying an optimality criteria based on a cost function which considers the distance from critical brain areas and the whole trajectory length. The planner proved to be effective in defining rectilinear trajectories accounting for the safety constraints determined by the brain morphology. It also demonstrated a short computational time and good capability in segmenting gyri and sulci surfaces.

  1. Heavy metals and epigenetic alterations in brain tumors.

    Science.gov (United States)

    Caffo, Maria; Caruso, Gerardo; Fata, Giuseppe La; Barresi, Valeria; Visalli, Maria; Venza, Mario; Venza, Isabella

    2014-12-01

    Heavy metals and their derivatives can cause various diseases. Numerous studies have evaluated the possible link between exposure to heavy metals and various cancers. Recent data show a correlation between heavy metals and aberration of genetic and epigenetic patterns. From a literature search we noticed few experimental and epidemiological studies that evaluate a possible correlation between heavy metals and brain tumors. Gliomas arise due to genetic and epigenetic alterations of glial cells. Changes in gene expression result in the alteration of the cellular division process. Epigenetic alterations in brain tumors include the hypermethylation of CpG group, hypomethylation of specific genes, aberrant activation of genes, and changes in the position of various histones. Heavy metals are capable of generating reactive oxygen assumes that key functions in various pathological mechanisms. Alteration of homeostasis of metals could cause the overproduction of reactive oxygen species and induce DNA damage, lipid peroxidation, and alteration of proteins. In this study we summarize the possible correlation between heavy metals, epigenetic alterations and brain tumors. We report, moreover, the review of relevant literature.

  2. Sigma and opioid receptors in human brain tumors

    Energy Technology Data Exchange (ETDEWEB)

    Thomas, G.E.; Szuecs, M.; Mamone, J.Y.; Bem, W.T.; Rush, M.D.; Johnson, F.E.; Coscia, C.J. (St. Louis Univ. School of Medicine, MO (USA))

    1990-01-01

    Human brain tumors and nude mouse-borne human neuroblastomas and gliomas were analyzed for sigma and opioid receptor content. Sigma binding was assessed using ({sup 3}H) 1, 3-di-o-tolylguanidine (DTG), whereas opioid receptor subtypes were measured with tritiated forms of the following: {mu}, (D-ala{sup 2}, mePhe{sup 4}, gly-ol{sup 5}) enkephalin (DAMGE); {kappa}, ethylketocyclazocine (EKC) or U69,593; {delta}, (D-pen{sup 2}, D-pen{sup 5}) enkephalin (DPDPE) or (D-ala{sup 2}, D-leu{sup 5}) enkephalin (DADLE) with {mu} suppressor present. Binding parameters were estimated by homologous displacement assays followed by analysis using the LIGAND program. Sigma binding was detected in 15 of 16 tumors examined with very high levels found in a brain metastasis from an adenocarcinoma of lung and a human neuroblastoma (SK-N-MC) passaged in nude mice. {kappa} opioid receptor binding was detected in 4 of 4 glioblastoma multiforme specimens and 2 of 2 human astrocytoma cell lines tested but not in the other brain tumors analyzed.

  3. Epidemiology of brain tumors in childhood--a review

    International Nuclear Information System (INIS)

    Baldwin, Rachel Tobias; Preston-Martin, Susan

    2004-01-01

    Malignant brain tumors are the leading cause of cancer death among children and the second most common type of pediatric cancer. Despite several decades of epidemiologic investigation, the etiology of childhood brain tumors (CBT) is still largely unknown. A few genetic syndromes and ionizing radiation are established risk factors. Many environmental exposures and infectious agents have been suspected of playing a role in the development of CBT. This review, based on a search of the medical literature through August 2003, summarizes the epidemiologic evidence to date. The types of exposures discussed include ionizing radiation, N-nitroso compounds (NOC), pesticides, tobacco smoke, electromagnetic frequencies (EMF), infectious agents, medications, and parental occupational exposures. We have chosen to focus on perinatal exposures and review some of the recent evidence indicating that such exposures may play a significant role in the causation of CBT. The scientific community is rapidly learning more about the molecular mechanisms by which carcinogenesis occurs and how the brain develops. We believe that advances in genetic and molecular biologic technology, including improved histologic subtyping of tumors, will be of huge importance in the future of epidemiologic research and will lead to a more comprehensive understanding of CBT etiology. We discuss some of the early findings using these technologies

  4. A Hybrid DE-RGSO-ELM for Brain Tumor Tissue Categorization in 3D Magnetic Resonance Images

    Directory of Open Access Journals (Sweden)

    K. Kothavari

    2014-01-01

    Full Text Available Medical diagnostics, a technique used for visualizing the internal structures and functions of human body, serves as a scientific tool to assist physicians and involves direct use of digital imaging system analysis. In this scenario, identification of brain tumors is complex in the diagnostic process. Magnetic resonance imaging (MRI technique is noted to best assist tissue contrast for anatomical details and also carries out mechanisms for investigating the brain by functional imaging in tumor predictions. Considering 3D MRI model, analyzing the anatomy features and tissue characteristics of brain tumor is complex in nature. Henceforth, in this work, feature extraction is carried out by computing 3D gray-level cooccurence matrix (3D GLCM and run-length matrix (RLM and feature subselection for dimensionality reduction is performed with basic differential evolution (DE algorithm. Classification is performed using proposed extreme learning machine (ELM, with refined group search optimizer (RGSO technique, to select the best parameters for better simplification and training of the classifier for brain tissue and tumor characterization as white matter (WM, gray matter (GM, cerebrospinal fluid (CSF, and tumor. Extreme learning machine outperforms the standard binary linear SVM and BPN for medical image classifier and proves better in classifying healthy and tumor tissues. The comparison between the algorithms proves that the mean and standard deviation produced by volumetric feature extraction analysis are higher than the other approaches. The proposed work is designed for pathological brain tumor classification and for 3D MRI tumor image segmentation. The proposed approaches are applied for real time datasets and benchmark datasets taken from dataset repositories.

  5. Automated segmentation of tumors on bone scans using anatomy-specific thresholding

    Science.gov (United States)

    Chu, Gregory H.; Lo, Pechin; Kim, Hyun J.; Lu, Peiyun; Ramakrishna, Bharath; Gjertson, David; Poon, Cheryce; Auerbach, Martin; Goldin, Jonathan; Brown, Matthew S.

    2012-03-01

    Quantification of overall tumor area on bone scans may be a potential biomarker for treatment response assessment and has, to date, not been investigated. Segmentation of bone metastases on bone scans is a fundamental step for this response marker. In this paper, we propose a fully automated computerized method for the segmentation of bone metastases on bone scans, taking into account characteristics of different anatomic regions. A scan is first segmented into anatomic regions via an atlas-based segmentation procedure, which involves non-rigidly registering a labeled atlas scan to the patient scan. Next, an intensity normalization method is applied to account for varying levels of radiotracer dosing levels and scan timing. Lastly, lesions are segmented via anatomic regionspecific intensity thresholding. Thresholds are chosen by receiver operating characteristic (ROC) curve analysis against manual contouring by board certified nuclear medicine physicians. A leave-one-out cross validation of our method on a set of 39 bone scans with metastases marked by 2 board-certified nuclear medicine physicians yielded a median sensitivity of 95.5%, and specificity of 93.9%. Our method was compared with a global intensity thresholding method. The results show a comparable sensitivity and significantly improved overall specificity, with a p-value of 0.0069.

  6. mTHPC-mediated photodynamic diagnosis of malignant brain tumors

    International Nuclear Information System (INIS)

    Zimmermann, A.

    2001-03-01

    Radical tumor resection is the basis for prolonged survival of patients suffering from malignant brain tumors such as glioblastoma multiform. We have carried out a phase II study involving 22 patients with malignant brain tumors to assess the feasibility and the effectiveness of the combination of intraoperative photodynamic diagnosis (PDD) and fluorescence-guided resection (FGR) mediated by the second generation photosensitizer meta-tetrahydroxyphenylchlorin (mTHPC). In addition, intraoperative photodynamic therapy (PDT) was performed. Several commercially available fluorescence diagnostic systems were investigated for their applicability for clinical practice. We have adapted and optimized a diagnostic system which includes a surgical microscope, an excitation light source (filtered to 370-440 nm), a video camera detection system, and a spectrometer for clear identification of the mTHPC fluorescence emission at 652 nm. Especially in regions of faint fluorescence it turned out to be essential to maximize the spectral information by optimizing and matching the spectral properties of all components, such as excitation source, camera and color filters. In summary, based on 138 tissue samples derived from 22 tumor specimens we have been able to achieve a sensitivity of 87.9 % and a specificity of 95.7 %. This study demonstrates that mTHPC-mediated intraoperative fluorescence-guided resection followed by photodynamic therapy is a feasible concept. (author)

  7. Prospective study of neuropsychological sequelae in children with brain tumors

    International Nuclear Information System (INIS)

    Bordeaux, J.D.; Dowell, R.E. Jr.; Copeland, D.R.; Fletcher, J.M.; Francis, D.J.; van Eys, J.

    1988-01-01

    Surgery and radiotherapy are the primary modalities of treatment for pediatric brain tumors. Despite the widespread use of these treatments, little is known of their acute effects (within one year posttreatment) on neuropsychological functions. An understanding of acute treatment effects may provide valuable feedback to neurosurgeons and a baseline against which delayed sequelae may be evaluated. This study compares pre- and posttherapy neuropsychological test performance of pediatric brain tumor patients categorized into two groups on the basis of treatment modalities: surgery (n = 7) and radiotherapy (n = 7). Treatment groups were composed of children aged 56 to 196 months at the time of evaluation with heterogeneous tumor diagnoses and locations. Comparisons of pretherapy findings with normative values using confidence intervals indicated that both groups performed within the average range on most measures. Outstanding deficits at baseline were observed on tests of fine-motor, psychomotor, and timed language skills, and are likely to be attributable to tumor-related effects. Comparisons of pre- versus posttherapy neuropsychological test findings indicated no significant interval changes for either group. Results suggest that surgery and radiotherapy are not associated with acute effects on neuropsychological functions

  8. Peritumoral hemorrhage immediately after radiosurgery for metastatic brain tumor

    International Nuclear Information System (INIS)

    Uchino, Masafumi; Kitajima, Satoru; Miyazaki, Chikao; Otsuka, Takashi; Seiki, Yoshikatsu; Shibata, Iekado

    2003-01-01

    We report a case of a 44-year-old woman with metastatic brain tumors who suffered peri-tumoral hemorrhage soon after stereotactic radiosurgery (SRS). She had been suffering from breast cancer with multiple systemic metastasis. She started to have headache, nausea, dizziness and speech disturbance 1 month before admission. There was no bleeding tendency in the hematological examination and the patient was normotensive. Neurological examination disclosed headache and slightly aphasia. Magnetic resonance imaging showed a large round mass lesion in the left temporal lobe. It was a well-demarcated, highly enhanced mass, 45 mm in diameter. SRS was performed on four lesions in a single session (Main mass: maximum dose was 30 Gy in the center and 20 Gy in the margin of the tumor. Others: maximum 25 Gy margin 20 Gy). After radiosurgery, she had severe headache, nausea and vomiting and showed progression of aphasia. CT scan revealed a peritumoral hemorrhage. Conservative therapy was undertaken and the patient's symptoms improved. After 7 days, she was discharged, able to walk. The patient died of extensive distant metastasis 5 months after SRS. Acute transient swelling following conventional radiotherapy is a well-documented phenomenon. However, the present case indicates that such an occurrence is also possible in SRS. We have hypothesized that acute reactions such as brain swelling occur due to breakdown of the fragile vessels of the tumor or surrounding tissue. (author)

  9. 3D Liver Tumor Segmentation in CT Images Using Improved Fuzzy C-Means and Graph Cuts.

    Science.gov (United States)

    Wu, Weiwei; Wu, Shuicai; Zhou, Zhuhuang; Zhang, Rui; Zhang, Yanhua

    2017-01-01

    Three-dimensional (3D) liver tumor segmentation from Computed Tomography (CT) images is a prerequisite for computer-aided diagnosis, treatment planning, and monitoring of liver cancer. Despite many years of research, 3D liver tumor segmentation remains a challenging task. In this paper, an efficient semiautomatic method was proposed for liver tumor segmentation in CT volumes based on improved fuzzy C -means (FCM) and graph cuts. With a single seed point, the tumor volume of interest (VOI) was extracted using confidence connected region growing algorithm to reduce computational cost. Then, initial foreground/background regions were labeled automatically, and a kernelized FCM with spatial information was incorporated in graph cuts segmentation to increase segmentation accuracy. The proposed method was evaluated on the public clinical dataset (3Dircadb), which included 15 CT volumes consisting of various sizes of liver tumors. We achieved an average volumetric overlap error (VOE) of 29.04% and Dice similarity coefficient (DICE) of 0.83, with an average processing time of 45 s per tumor. The experimental results showed that the proposed method was accurate for 3D liver tumor segmentation with a reduction of processing time.

  10. Differential role of tumor necrosis factor receptors in mouse brain inflammatory responses in cryolesion brain injury

    DEFF Research Database (Denmark)

    Quintana, Albert; Giralt, Mercedes; Rojas, Santiago

    2005-01-01

    Tumor necrosis factor-alpha (TNF-alpha) is one of the mediators dramatically increased after traumatic brain injury that leads to the activation, proliferation, and hypertrophy of mononuclear, phagocytic cells and gliosis. Eventually, TNF-alpha can induce both apoptosis and necrosis via...... by TNFR1 deficiency. Overall, these results suggest that TNFR1 is involved in the early establishment of the inflammatory response and that its deficiency causes a decreased inflammatory response and tissue damage following brain injury....

  11. Tumor Metabolism, the Ketogenic Diet and β-Hydroxybutyrate: Novel Approaches to Adjuvant Brain Tumor Therapy.

    Science.gov (United States)

    Woolf, Eric C; Syed, Nelofer; Scheck, Adrienne C

    2016-01-01

    Malignant brain tumors are devastating despite aggressive treatments such as surgical resection, chemotherapy and radiation therapy. The average life expectancy of patients with newly diagnosed glioblastoma is approximately ~18 months. It is clear that increased survival of brain tumor patients requires the design of new therapeutic modalities, especially those that enhance currently available treatments and/or limit tumor growth. One novel therapeutic arena is the metabolic dysregulation that results in an increased need for glucose in tumor cells. This phenomenon suggests that a reduction in tumor growth could be achieved by decreasing glucose availability, which can be accomplished through pharmacological means or through the use of a high-fat, low-carbohydrate ketogenic diet (KD). The KD, as the name implies, also provides increased blood ketones to support the energy needs of normal tissues. Preclinical work from a number of laboratories has shown that the KD does indeed reduce tumor growth in vivo . In addition, the KD has been shown to reduce angiogenesis, inflammation, peri-tumoral edema, migration and invasion. Furthermore, this diet can enhance the activity of radiation and chemotherapy in a mouse model of glioma, thus increasing survival. Additional studies in vitro have indicated that increasing ketones such as β-hydroxybutyrate (βHB) in the absence of glucose reduction can also inhibit cell growth and potentiate the effects of chemotherapy and radiation. Thus, while we are only beginning to understand the pluripotent mechanisms through which the KD affects tumor growth and response to conventional therapies, the emerging data provide strong support for the use of a KD in the treatment of malignant gliomas. This has led to a limited number of clinical trials investigating the use of a KD in patients with primary and recurrent glioma.

  12. Tumor Metabolism, the Ketogenic Diet and β-Hydroxybutyrate: Novel Approaches to Adjuvant Brain Tumor Therapy

    Science.gov (United States)

    Woolf, Eric C.; Syed, Nelofer; Scheck, Adrienne C.

    2016-01-01

    Malignant brain tumors are devastating despite aggressive treatments such as surgical resection, chemotherapy and radiation therapy. The average life expectancy of patients with newly diagnosed glioblastoma is approximately ~18 months. It is clear that increased survival of brain tumor patients requires the design of new therapeutic modalities, especially those that enhance currently available treatments and/or limit tumor growth. One novel therapeutic arena is the metabolic dysregulation that results in an increased need for glucose in tumor cells. This phenomenon suggests that a reduction in tumor growth could be achieved by decreasing glucose availability, which can be accomplished through pharmacological means or through the use of a high-fat, low-carbohydrate ketogenic diet (KD). The KD, as the name implies, also provides increased blood ketones to support the energy needs of normal tissues. Preclinical work from a number of laboratories has shown that the KD does indeed reduce tumor growth in vivo. In addition, the KD has been shown to reduce angiogenesis, inflammation, peri-tumoral edema, migration and invasion. Furthermore, this diet can enhance the activity of radiation and chemotherapy in a mouse model of glioma, thus increasing survival. Additional studies in vitro have indicated that increasing ketones such as β-hydroxybutyrate (βHB) in the absence of glucose reduction can also inhibit cell growth and potentiate the effects of chemotherapy and radiation. Thus, while we are only beginning to understand the pluripotent mechanisms through which the KD affects tumor growth and response to conventional therapies, the emerging data provide strong support for the use of a KD in the treatment of malignant gliomas. This has led to a limited number of clinical trials investigating the use of a KD in patients with primary and recurrent glioma. PMID:27899882

  13. Tumor metabolism, the ketogenic diet and β-hydroxybutyrate: novel approaches to adjuvant brain tumor therapy

    Directory of Open Access Journals (Sweden)

    Eric C. Woolf

    2016-11-01

    Full Text Available Malignant brain tumors are devastating despite aggressive treatments such as surgical resection, chemotherapy and radiation therapy. The average life expectancy of patients with newly diagnosed glioblastoma is approximately ~18 months. It is clear that increased survival of brain tumor patients requires the design of new therapeutic modalities, especially those that enhance currently available treatments and/or limit tumor growth. One novel therapeutic arena is the metabolic dysregulation that results in an increased need for glucose in tumor cells. This phenomenon suggests that a reduction in tumor growth could be achieved by decreasing glucose availability, which can be accomplished through pharmacological means or through the use of a high-fat, low-carbohydrate ketogenic diet (KD. The KD, as the name implies, also provides increased blood ketones to support the energy needs of normal tissues. Preclinical work from a number of laboratories has shown that the KD does indeed reduce tumor growth in vivo. In addition, the KD has been shown to reduce angiogenesis, inflammation, peri-tumoral edema, migration and invasion. Furthermore, this diet can enhance the activity of radiation and chemotherapy in a mouse model of glioma, thus increasing survival. Additional studies in vitro have indicated that increasing ketones such as β-hydroxybutyrate in the absence of glucose reduction can also inhibit cell growth and potentiate the effects of chemotherapy and radiation. Thus, while we are only beginning to understand the pluripotent mechanisms through which the KD affects tumor growth and response to conventional therapies, the emerging data provide strong support for the use of a KD in the treatment of malignant gliomas. This has led to a limited number of clinical trials investigating the use of a KD in patients with primary and recurrent glioma.

  14. Complications of ventricular entry during craniotomy for brain tumor resection.

    Science.gov (United States)

    John, Jessin K; Robin, Adam M; Pabaney, Aqueel H; Rammo, Richard A; Schultz, Lonni R; Sadry, Neema S; Lee, Ian Y

    2017-08-01

    OBJECTIVE Recent studies have demonstrated that periventricular tumor location is associated with poorer survival and that tumor location near the ventricle limits the extent of resection. This finding may relate to the perception that ventricular entry leads to further complications and thus surgeons may choose to perform less aggressive resection in these areas. However, there is little support for this view in the literature. This study seeks to determine whether ventricular entry is associated with more complications during craniotomy for brain tumor resection. METHODS A retrospective analysis of patients who underwent craniotomy for tumor resection at Henry Ford Hospital between January 2010 and November 2012 was conducted. A total of 183 cases were reviewed with attention to operative entry into the ventricular system, postoperative use of an external ventricular drain (EVD), subdural hematoma, hydrocephalus, and symptomatic intraventricular hemorrhage (IVH). RESULTS Patients in whom the ventricles were entered had significantly higher rates of any complication (46% vs 21%). Complications included development of subdural hygroma, subdural hematoma, intraventricular hemorrhage, subgaleal collection, wound infection, urinary tract infection/deep venous thrombosis, hydrocephalus, and ventriculoperitoneal (VP) shunt placement. Specifically, these patients had significantly higher rates of EVD placement (23% vs 1%, p entry (11% vs 0%, p = 0.001) with 3 of 4 of these patients having a large ventricular entry (defined here as entry greater than a pinhole [entry). Furthermore, in a subset of glioblastoma patients with and without ventricular entry, Kaplan-Meier estimates for survival demonstrated a median survival time of 329 days for ventricular entry compared with 522 days for patients with no ventricular entry (HR 1.13, 95% CI 0.65-1.96; p = 0.67). CONCLUSIONS There are more complications associated with ventricular entry during brain tumor resection than in

  15. Brain lesion segmentation through image synthesis and outlier detection

    Directory of Open Access Journals (Sweden)

    Christopher Bowles

    2017-01-01

    Full Text Available Cerebral small vessel disease (SVD can manifest in a number of ways. Many of these result in hyperintense regions visible on T2-weighted magnetic resonance (MR images. The automatic segmentation of these lesions has been the focus of many studies. However, previous methods tended to be limited to certain types of pathology, as a consequence of either restricting the search to the white matter, or by training on an individual pathology. Here we present an unsupervised abnormality detection method which is able to detect abnormally hyperintense regions on FLAIR regardless of the underlying pathology or location. The method uses a combination of image synthesis, Gaussian mixture models and one class support vector machines, and needs only be trained on healthy tissue. We evaluate our method by comparing segmentation results from 127 subjects with SVD with three established methods and report significantly superior performance across a number of metrics.

  16. Segmentation of Brain MRI Using SOM-FCM-Based Method and 3D Statistical Descriptors

    Directory of Open Access Journals (Sweden)

    Andrés Ortiz

    2013-01-01

    Full Text Available Current medical imaging systems provide excellent spatial resolution, high tissue contrast, and up to 65535 intensity levels. Thus, image processing techniques which aim to exploit the information contained in the images are necessary for using these images in computer-aided diagnosis (CAD systems. Image segmentation may be defined as the process of parcelling the image to delimit different neuroanatomical tissues present on the brain. In this paper we propose a segmentation technique using 3D statistical features extracted from the volume image. In addition, the presented method is based on unsupervised vector quantization and fuzzy clustering techniques and does not use any a priori information. The resulting fuzzy segmentation method addresses the problem of partial volume effect (PVE and has been assessed using real brain images from the Internet Brain Image Repository (IBSR.

  17. Development and Implementation of a Corriedale Ovine Brain Atlas for Use in Atlas-Based Segmentation.

    Directory of Open Access Journals (Sweden)

    Kishan Andre Liyanage

    Full Text Available Segmentation is the process of partitioning an image into subdivisions and can be applied to medical images to isolate anatomical or pathological areas for further analysis. This process can be done manually or automated by the use of image processing computer packages. Atlas-based segmentation automates this process by the use of a pre-labelled template and a registration algorithm. We developed an ovine brain atlas that can be used as a model for neurological conditions such as Parkinson's disease and focal epilepsy. 17 female Corriedale ovine brains were imaged in-vivo in a 1.5T (low-resolution MRI scanner. 13 of the low-resolution images were combined using a template construction algorithm to form a low-resolution template. The template was labelled to form an atlas and tested by comparing manual with atlas-based segmentations against the remaining four low-resolution images. The comparisons were in the form of similarity metrics used in previous segmentation research. Dice Similarity Coefficients were utilised to determine the degree of overlap between eight independent, manual and atlas-based segmentations, with values ranging from 0 (no overlap to 1 (complete overlap. For 7 of these 8 segmented areas, we achieved a Dice Similarity Coefficient of 0.5-0.8. The amygdala was difficult to segment due to its variable location and similar intensity to surrounding tissues resulting in Dice Coefficients of 0.0-0.2. We developed a low resolution ovine brain atlas with eight clinically relevant areas labelled. This brain atlas performed comparably to prior human atlases described in the literature and to intra-observer error providing an atlas that can be used to guide further research using ovine brains as a model and is hosted online for public access.

  18. FULLY CONVOLUTIONAL NETWORKS FOR MULTI-MODALITY ISOINTENSE INFANT BRAIN IMAGE SEGMENTATION.

    Science.gov (United States)

    Nie, Dong; Wang, Li; Gao, Yaozong; Shen, Dinggang

    The segmentation of infant brain tissue images into white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) plays an important role in studying early brain development. In the isointense phase (approximately 6-8 months of age), WM and GM exhibit similar levels of intensity in both T1 and T2 MR images, resulting in extremely low tissue contrast and thus making the tissue segmentation very challenging. The existing methods for tissue segmentation in this isointense phase usually employ patch-based sparse labeling on single T1, T2 or fractional anisotropy (FA) modality or their simply-stacked combinations without fully exploring the multi-modality information. To address the challenge, in this paper, we propose to use fully convolutional networks (FCNs) for the segmentation of isointense phase brain MR images. Instead of simply stacking the three modalities, we train one network for each modality image, and then fuse their high-layer features together for final segmentation. Specifically, we conduct a convolution-pooling stream for multimodality information from T1, T2, and FA images separately, and then combine them in high-layer for finally generating the segmentation maps as the outputs. We compared the performance of our approach with that of the commonly used segmentation methods on a set of manually segmented isointense phase brain images. Results showed that our proposed model significantly outperformed previous methods in terms of accuracy. In addition, our results also indicated a better way of integrating multi-modality images, which leads to performance improvement.

  19. Hierarchical non-negative matrix factorization to characterize brain tumor heterogeneity using multi-parametric MRI.

    Science.gov (United States)

    Sauwen, Nicolas; Sima, Diana M; Van Cauter, Sofie; Veraart, Jelle; Leemans, Alexander; Maes, Frederik; Himmelreich, Uwe; Van Huffel, Sabine

    2015-12-01

    Tissue characterization in brain tumors and, in particular, in high-grade gliomas is challenging as a result of the co-existence of several intra-tumoral tissue types within the same region and the high spatial heterogeneity. This study presents a method for the detection of the relevant tumor substructures (i.e. viable tumor, necrosis and edema), which could be of added value for the diagnosis, treatment planning and follow-up of individual patients. Twenty-four patients with glioma [10 low-grade gliomas (LGGs), 14 high-grade gliomas (HGGs)] underwent a multi-parametric MRI (MP-MRI) scheme, including conventional MRI (cMRI), perfusion-weighted imaging (PWI), diffusion kurtosis imaging (DKI) and short-TE (1)H MRSI. MP-MRI parameters were derived: T2, T1 + contrast, fluid-attenuated inversion recovery (FLAIR), relative cerebral blood volume (rCBV), mean diffusivity (MD), fractional anisotropy (FA), mean kurtosis (MK) and the principal metabolites lipids (Lip), lactate (Lac), N-acetyl-aspartate (NAA), total choline (Cho), etc. Hierarchical non-negative matrix factorization (hNMF) was applied to the MP-MRI parameters, providing tissue characterization on a patient-by-patient and voxel-by-voxel basis. Tissue-specific patterns were obtained and the spatial distribution of each tissue type was visualized by means of abundance maps. Dice scores were calculated by comparing tissue segmentation derived from hNMF with the manual segmentation by a radiologist. Correlation coefficients were calculated between each pathologic tissue source and the average feature vector within the corresponding tissue region. For the patients with HGG, mean Dice scores of 78%, 85% and 83% were obtained for viable tumor, the tumor core and the complete tumor region. The mean correlation coefficients were 0.91 for tumor, 0.97 for necrosis and 0.96 for edema. For the patients with LGG, a mean Dice score of 85% and mean correlation coefficient of 0.95 were found for the tumor region. hNMF was

  20. Automatic segmentation of brain MRIs and mapping neuroanatomy across the human lifespan

    Science.gov (United States)

    Keihaninejad, Shiva; Heckemann, Rolf A.; Gousias, Ioannis S.; Rueckert, Daniel; Aljabar, Paul; Hajnal, Joseph V.; Hammers, Alexander

    2009-02-01

    A robust model for the automatic segmentation of human brain images into anatomically defined regions across the human lifespan would be highly desirable, but such structural segmentations of brain MRI are challenging due to age-related changes. We have developed a new method, based on established algorithms for automatic segmentation of young adults' brains. We used prior information from 30 anatomical atlases, which had been manually segmented into 83 anatomical structures. Target MRIs came from 80 subjects (~12 individuals/decade) from 20 to 90 years, with equal numbers of men, women; data from two different scanners (1.5T, 3T), using the IXI database. Each of the adult atlases was registered to each target MR image. By using additional information from segmentation into tissue classes (GM, WM and CSF) to initialise the warping based on label consistency similarity before feeding this into the previous normalised mutual information non-rigid registration, the registration became robust enough to accommodate atrophy and ventricular enlargement with age. The final segmentation was obtained by combination of the 30 propagated atlases using decision fusion. Kernel smoothing was used for modelling the structural volume changes with aging. Example linear correlation coefficients with age were, for lateral ventricular volume, rmale=0.76, rfemale=0.58 and, for hippocampal volume, rmale=-0.6, rfemale=-0.4 (allρ<0.01).

  1. Improved labeling of subcortical brain structures in atlas-based segmentation of magnetic resonance images.

    Science.gov (United States)

    Yousefi, Siamak; Kehtarnavaz, Nasser; Gholipour, Ali

    2012-07-01

    Precise labeling of subcortical structures plays a key role in functional neurosurgical applications. Labels from an atlas image are propagated to a patient image using atlas-based segmentation. Atlas-based segmentation is highly dependent on the registration framework used to guide the atlas label propagation. This paper focuses on atlas-based segmentation of subcortical brain structures and the effect of different registration methods on the generated subcortical labels. A single-step and three two-step registration methods appearing in the literature based on affine and deformable registration algorithms in the ANTS and FSL algorithms are considered. Experiments are carried out with two atlas databases of IBSR and LPBA40. Six segmentation metrics consisting of Dice overlap, relative volume error, false positive, false negative, surface distance, and spatial extent are used for evaluation. Segmentation results are reported individually and as averages for nine subcortical brain structures. Based on two statistical tests, the results are ranked. In general, among four different registration strategies investigated in this paper, a two-step registration consisting of an initial affine registration followed by a deformable registration applied to subcortical structures provides superior segmentation outcomes. This method can be used to provide an improved labeling of the subcortical brain structures in MRIs for different applications.

  2. Topology polymorphism graph for lung tumor segmentation in PET-CT images

    Science.gov (United States)

    Cui, Hui; Wang, Xiuying; Zhou, Jianlong; Eberl, Stefan; Yin, Yong; Feng, Dagan; Fulham, Michael

    2015-06-01

    Accurate lung tumor segmentation is problematic when the tumor boundary or edge, which reflects the advancing edge of the tumor, is difficult to discern on chest CT or PET. We propose a ‘topo-poly’ graph model to improve identification of the tumor extent. Our model incorporates an intensity graph and a topology graph. The intensity graph provides the joint PET-CT foreground similarity to differentiate the tumor from surrounding tissues. The topology graph is defined on the basis of contour tree to reflect the inclusion and exclusion relationship of regions. By taking into account different topology relations, the edges in our model exhibit topological polymorphism. These polymorphic edges in turn affect the energy cost when crossing different topology regions under a random walk framework, and hence contribute to appropriate tumor delineation. We validated our method on 40 patients with non-small cell lung cancer where the tumors were manually delineated by a clinical expert. The studies were separated into an ‘isolated’ group (n = 20) where the lung tumor was located in the lung parenchyma and away from associated structures / tissues in the thorax and a ‘complex’ group (n = 20) where the tumor abutted / involved a variety of adjacent structures and had heterogeneous FDG uptake. The methods were validated using Dice’s similarity coefficient (DSC) to measure the spatial volume overlap and Hausdorff distance (HD) to compare shape similarity calculated as the maximum surface distance between the segmentation results and the manual delineations. Our method achieved an average DSC of 0.881  ±  0.046 and HD of 5.311  ±  3.022 mm for the isolated cases and DSC of 0.870  ±  0.038 and HD of 9.370  ±  3.169 mm for the complex cases. Student’s t-test showed that our model outperformed the other methods (p-values <0.05).

  3. Assessment of functional status in children with brain tumors

    International Nuclear Information System (INIS)

    Sugita, Yasuo; Kobayashi, Seiichi; Uegaki, Masami; Katayama, Masahiko; Miyagi, Jun; Iryo, Osamu; Shigemori, Minoru; Kuramoto, Shinken; Ootsubo, Masaaki

    1987-01-01

    Thirty children treated for brain tumors between 1978 - 1985 at Kurume university hospital were evaluated for alternation in intellectual, emotional, and social function. They were 15 males and 15 females, aged 3 to 16 years, on the averaged 1.7 years after treatment. Twenty-eight children had no neurological deficits and 2 children had slight neurological deficits. It was possible for twenty-eight children to be evaluated for intelligence quotient by Wechsler Intelligence Scale for Children-revised and Tanaka-Binet. The median score and standard deviation of intelligence quotient (IQ) test in children with brain tumors were as follows; verbal IQ: 84 ± 16, performance IQ: 77 ± 20, full scale IQ: 80 ± 20. There children with brain tumors obtained significant low IQ scores than children (t-test, P < 0.01). Twenty-one (72 %) children showed subnormal IQ scores (IQ < 90) and 7 children showed normal IQ scores (IQ ≥ 90). Concerning social and emotional function, twelve children (45.7 %) showed abnormal behaviour. The median scores and standard deviation of IQ scores in cranial irradiated patients were as follows; verbal IQ: 79 ± 13, performance IQ: 71 ± 15, full scale IQ: 71 ± 14. Especially, ten of twelve cranial irradiated patients showed subnormal IQ scores. Also, cranial irradiated patients obtained significant low IQ scores than non-cranial irradiated patients (t-test, P < 0.05). Serial evaluation of three cranial irradiated patients revealed further deterioration without recurrence of tumor and hydrocephalus. The results are discussed to: (1) the effects and mechanism of cranial irradiation on cognitive development: (2) the relationship between cognitive dysfunction and irradiation methods. The effects and mechanism of cranial irradiation on cognitive dysfunction is considered to be not only injury of cortex but also injury of fiber tracts. Also, cognitive dysfunction is apt to be related to age of irradiated patients. (J.P.N.)

  4. Intraparenchymal hemorrhage segmentation from clinical head CT of patients with traumatic brain injury

    Science.gov (United States)

    Roy, Snehashis; Wilkes, Sean; Diaz-Arrastia, Ramon; Butman, John A.; Pham, Dzung L.

    2015-03-01

    Quantification of hemorrhages in head computed tomography (CT) images from patients with traumatic brain injury (TBI) has potential applications in monitoring disease progression and better understanding of the patho-physiology of TBI. Although manual segmentations can provide accurate measures of hemorrhages, the processing time and inter-rater variability make it infeasible for large studies. In this paper, we propose a fully automatic novel pipeline for segmenting intraparenchymal hemorrhages (IPH) from clinical head CT images. Unlike previous methods of model based segmentation or active contour techniques, we rely on relevant and matching examples from already segmented images by trained raters. The CT images are first skull-stripped. Then example patches from an "atlas" CT and its manual segmentation are used to learn a two-class sparse dictionary for hemorrhage and normal tissue. Next, for a given "subject" CT, a subject patch is modeled as a sparse convex combination of a few atlas patches from the dictionary. The same convex combination is applied to the atlas segmentation patches to generate a membership for the hemorrhages at each voxel. Hemorrhages are segmented from 25 subjects with various degrees of TBI. Results are compared with segmentations obtained from an expert rater. A median Dice coefficient of 0.85 between automated and manual segmentations is achieved. A linear fit between automated and manual volumes show a slope of 1.0047, indicating a negligible bias in volume estimation.

  5. Fast and robust multi-atlas segmentation of brain magnetic resonance images

    DEFF Research Database (Denmark)

    Lötjönen, Jyrki Mp; Wolz, Robin; Koikkalainen, Juha R

    2010-01-01

    We introduce an optimised pipeline for multi-atlas brain MRI segmentation. Both accuracy and speed of segmentation are considered. We study different similarity measures used in non-rigid registration. We show that intensity differences for intensity normalised images can be used instead...... of standard normalised mutual information in registration without compromising the accuracy but leading to threefold decrease in the computation time. We study and validate also different methods for atlas selection. Finally, we propose two new approaches for combining multi-atlas segmentation and intensity...... modelling based on segmentation using expectation maximisation (EM) and optimisation via graph cuts. The segmentation pipeline is evaluated with two data cohorts: IBSR data (N=18, six subcortial structures: thalamus, caudate, putamen, pallidum, hippocampus, amygdala) and ADNI data (N=60, hippocampus...

  6. BrainSegNet: a convolutional neural network architecture for automated segmentation of human brain structures.

    Science.gov (United States)

    Mehta, Raghav; Majumdar, Aabhas; Sivaswamy, Jayanthi

    2017-04-01

    Automated segmentation of cortical and noncortical human brain structures has been hitherto approached using nonrigid registration followed by label fusion. We propose an alternative approach for this using a convolutional neural network (CNN) which classifies a voxel into one of many structures. Four different kinds of two-dimensional and three-dimensional intensity patches are extracted for each voxel, providing local and global (context) information to the CNN. The proposed approach is evaluated on five different publicly available datasets which differ in the number of labels per volume. The obtained mean Dice coefficient varied according to the number of labels, for example, it is [Formula: see text] and [Formula: see text] for datasets with the least (32) and the most (134) number of labels, respectively. These figures are marginally better or on par with those obtained with the current state-of-the-art methods on nearly all datasets, at a reduced computational time. The consistently good performance of the proposed method across datasets and no requirement for registration make it attractive for many applications where reduced computational time is necessary.

  7. Ensemble Semi-supervised Frame-work for Brain Magnetic Resonance Imaging Tissue Segmentation.

    Science.gov (United States)

    Azmi, Reza; Pishgoo, Boshra; Norozi, Narges; Yeganeh, Samira

    2013-04-01

    Brain magnetic resonance images (MRIs) tissue segmentation is one of the most important parts of the clinical diagnostic tools. Pixel classification methods have been frequently used in the image segmentation with two supervised and unsupervised approaches up to now. Supervised segmentation methods lead to high accuracy, but they need a large amount of labeled data, which is hard, expensive, and slow to obtain. Moreover, they cannot use unlabeled data to train classifiers. On the other hand, unsupervised segmentation methods have no prior knowledge and lead to low level of performance. However, semi-supervised learning which uses a few labeled data together with a large amount of unlabeled data causes higher accuracy with less trouble. In this paper, we propose an ensemble semi-supervised frame-work for segmenting of brain magnetic resonance imaging (MRI) tissues that it has been used results of several semi-supervised classifiers simultaneously. Selecting appropriate classifiers has a significant role in the performance of this frame-work. Hence, in this paper, we present two semi-supervised algorithms expectation filtering maximization and MCo_Training that are improved versions of semi-supervised methods expectation maximization and Co_Training and increase segmentation accuracy. Afterward, we use these improved classifiers together with graph-based semi-supervised classifier as components of the ensemble frame-work. Experimental results show that performance of segmentation in this approach is higher than both supervised methods and the individual semi-supervised classifiers.

  8. Automated Segmentation of Cerebellum Using Brain Mask and Partial Volume Estimation Map

    Directory of Open Access Journals (Sweden)

    Dong-Kyun Lee

    2015-01-01

    Full Text Available While segmentation of the cerebellum is an indispensable step in many studies, its contrast is not clear because of the adjacent cerebrospinal fluid, meninges, and cerebra peduncle. Thus, various cerebellar segmentation methods, such as a deformable model or a template-based algorithm might exhibit incorrect segmentation of the venous sinuses and the cerebellar peduncle. In this study, we propose a fully automated procedure combining cerebellar tissue classification, a template-based approach, and morphological operations sequentially. The cerebellar region was defined approximately by removing the cerebral region from the brain mask. Then, the noncerebellar region was trimmed using a morphological operator and the brain-stem atlas was aligned to the individual brain to define the brain-stem area. The proposed method was validated with the well-known FreeSurfer and ITK-SNAP packages using the dice similarity index and recall and precision scores. As a result, the proposed method was significantly better than the other methods for the dice similarity index (0.93, FreeSurfer: 0.92, ITK-SNAP: 0.87 and precision (0.95, FreeSurfer: 0.90, ITK-SNAP: 0.93. Therefore, it could be said that the proposed method yielded a robust and accurate segmentation result. Moreover, additional postprocessing with the brain-stem atlas could improve its result.

  9. Thermal dosimetry studies of ultrasonically induced hyperthermia in normal dog brain and in experimental brain tumors

    International Nuclear Information System (INIS)

    Britt, R.H.; Pounds, D.W.; Stuart, J.S.; Lyons, B.E.; Saxer, E.L.

    1984-01-01

    In a series of 16 acute experiments on pentobarbital anesthetized dogs, thermal distributions generated by ultrasonic heating using a 1 MHz PZT transducer were compared with intensity distributions mapped in a test tank. Relatively flat distributions from 1 to 3 cm have been mapped in normal dog brain using ''shaped'' intensity distributions generated from ultrasonic emission patterns which are formed by the interaction between compressional, transverse and flexural modes activated within the crystal. In contrast, these same intensity distributions generated marked temperature variations in 3 malignant brain tumors presumably due to variations in tumor blood flow. The results of this study suggest that a practical clinical system for uniform heating of large tumor volumes with varying volumes and geometries is not an achievable goal. The author's laboratory is developing a scanning ultrasonic rapid hyperthermia treatment system which will be able to sequentially heat small volume of tumor tissue either to temperatures which will sterilize tumor or to a more conventional thermal dose. Time-temperature studies of threshold for thermal damage in normal dog brain are currently in progress

  10. Quantifying brain tissue volume in multiple sclerosis with automated lesion segmentation and filling

    Directory of Open Access Journals (Sweden)

    Sergi Valverde

    2015-01-01

    Full Text Available Lesion filling has been successfully applied to reduce the effect of hypo-intense T1-w Multiple Sclerosis (MS lesions on automatic brain tissue segmentation. However, a study of fully automated pipelines incorporating lesion segmentation and lesion filling on tissue volume analysis has not yet been performed. Here, we analyzed the % of error introduced by automating the lesion segmentation and filling processes in the tissue segmentation of 70 clinically isolated syndrome patient images. First of all, images were processed using the LST and SLS toolkits with different pipeline combinations that differed in either automated or manual lesion segmentation, and lesion filling or masking out lesions. Then, images processed following each of the pipelines were segmented into gray matter (GM and white matter (WM using SPM8, and compared with the same images where expert lesion annotations were filled before segmentation. Our results showed that fully automated lesion segmentation and filling pipelines reduced significantly the % of error in GM and WM volume on images of MS patients, and performed similarly to the images where expert lesion annotations were masked before segmentation. In all the pipelines, the amount of misclassified lesion voxels was the main cause in the observed error in GM and WM volume. However, the % of error was significantly lower when automatically estimated lesions were filled and not masked before segmentation. These results are relevant and suggest that LST and SLS toolboxes allow the performance of accurate brain tissue volume measurements without any kind of manual intervention, which can be convenient not only in terms of time and economic costs, but also to avoid the inherent intra/inter variability between manual annotations.

  11. A Method to Automate the Segmentation of the GTV and ITV for Lung Tumors

    International Nuclear Information System (INIS)

    Ehler, Eric D.; Bzdusek, Karl; Tome, Wolfgang A.

    2009-01-01

    Four-dimensional computed tomography (4D-CT) is a useful tool in the treatment of tumors that undergo significant motion. To fully utilize 4D-CT motion information in the treatment of mobile tumors such as lung cancer, autosegmentation methods will need to be developed. Using autosegmentation tools in the Pinnacle 3 v8.1t treatment planning system, 6 anonymized 4D-CT data sets were contoured. Two test indices were developed that can be used to evaluate which autosegmentation tools to apply to a given gross tumor volume (GTV) region of interest (ROI). The 4D-CT data sets had various phase binning error levels ranging from 3% to 29%. The appropriate autosegmentation method (rigid translational image registration and deformable surface mesh) was determined to properly delineate the GTV in all of the 4D-CT phases for the 4D-CT data sets with binning errors of up to 15%. The ITV was defined by 2 methods: a mask of the GTV in all 4D-CT phases and the maximum intensity projection. The differences in centroid position and volume were compared with manual segmentation studies in literature. The indices developed in this study, along with the autosegmentation tools in the treatment planning system, were able to automatically segment the GTV in the four 4D-CTs with phase binning errors of up to 15%.

  12. Psychosocial profile of pediatric brain tumor survivors with neurocognitive complaints.

    Science.gov (United States)

    de Ruiter, Marieke Anna; Schouten-van Meeteren, Antoinette Yvonne Narda; van Vuurden, Dannis Gilbert; Maurice-Stam, Heleen; Gidding, Corrie; Beek, Laura Rachel; Granzen, Bernd; Oosterlaan, Jaap; Grootenhuis, Martha Alexandra

    2016-02-01

    With more children surviving a brain tumor, neurocognitive consequences of the tumor and its treatment become apparent, which could affect psychosocial functioning. The present study therefore aimed to assess psychosocial functioning of pediatric brain tumor survivors (PBTS) in detail. Psychosocial functioning of PBTS (8-18 years) with parent-reported neurocognitive complaints was compared to normative data on health-related quality of life (HRQOL), self-esteem, psychosocial adjustment, and executive functioning (one-sample t tests) and to a sibling control group on fatigue (independent-samples t test). Self-, parent-, and teacher-report questionnaires were included, where appropriate, providing complementary information. Eighty-two PBTS (mean age 13.4 years, SD 3.2, 49 % males) and 43 healthy siblings (mean age 14.3, SD 2.4, 40 % males) were included. As compared to the normative population, PBTS themselves reported decreased physical, psychological, and generic HRQOL (d = 0.39-0.62, p self-esteem and psychosocial adjustment seemed not to be affected. Parents of PBTS reported more psychosocial (d = 0.81, p Teachers indicated more psychosocial adjustment problems for female PBTS aged 8-11 years than for the female normative population (d = 0.69, p teachers. Systematic screening of psychosocial functioning is necessary so that tailored support from professionals can be offered to PBTS with neurocognitive complaints.

  13. Proton and carbon ion radiotherapy for primary brain tumors and tumors of the skull base

    International Nuclear Information System (INIS)

    Combs, Stephanie E.; Kessel, Kerstin; Habermehl, Daniel; Debus, Jurgen; Haberer, Thomas; Jaekel, Oliver

    2013-01-01

    To analyze clinical concepts, toxicity and treatment outcome in patients with brain and skull base tumors treated with photons and particle therapy. Material and methods: In total 260 patients with brain tumors and tumors of the skull base were treated at the Heidelberg Ion Therapy Center (HIT). Patients enrolled in and randomized within prospective clinical trials as well as bony or soft tissue tumors are not included in this analysis. Treatment was delivered as protons, carbon ions, or combinations of photons and a carbon ion boost. All patients are included in a tight follow-up program. The median follow-up time is 12 months (range 2-39 months). Results: Main histologies included meningioma (n = 107) for skull base lesions, pituitary adenomas (n = 14), low-grade gliomas (n = 51) as well as high-grade gliomas (n = 55) for brain tumors. In all patients treatment could be completed without any unexpected severe toxicities. No side effects > CTC Grade III were observed. To date, no severe late toxicities were observed, however, for endpoints such as secondary malignancies or neuro cognitive side effects follow-up time still remains too short. Local recurrences were mainly seen in the group of high-grade gliomas or atypical meningiomas; for benign skull base meningiomas, to date, no recurrences were observed during follow-up. Conclusion: The specific benefit of particle therapy will potentially reduce the risk of secondary malignancies as well as improve neuro cognitive outcome and quality of life (QOL); thus, longer follow-up will be necessary to confirm these endpoints. Indication-specific trials on meningiomas and gliomas are underway to elucidate the role of protons and carbon ions in these indications

  14. Proton and carbon ion radiotherapy for primary brain tumors and tumors of the skull base

    Energy Technology Data Exchange (ETDEWEB)

    Combs, Stephanie E.; Kessel, Kerstin; Habermehl, Daniel; Debus, Jurgen [Univ. Hospital of Heidelberg, Dept. of Radiation Oncology, Heidelberg (Germany)], e-mail: Stephanie.Combs@med.uni-heidelberg.de; Haberer, Thomas [Heidelberger Ionenstrahl Therapiezentrum (HIT), Heidelberg (Germany); Jaekel, Oliver [Univ. Hospital of Heidelberg, Dept. of Radiation Oncology, Heidelberg (Germany); Heidelberger Ionenstrahl Therapiezentrum (HIT), Heidelberg (Germany)

    2013-10-15

    To analyze clinical concepts, toxicity and treatment outcome in patients with brain and skull base tumors treated with photons and particle therapy. Material and methods: In total 260 patients with brain tumors and tumors of the skull base were treated at the Heidelberg Ion Therapy Center (HIT). Patients enrolled in and randomized within prospective clinical trials as well as bony or soft tissue tumors are not included in this analysis. Treatment was delivered as protons, carbon ions, or combinations of photons and a carbon ion boost. All patients are included in a tight follow-up program. The median follow-up time is 12 months (range 2-39 months). Results: Main histologies included meningioma (n = 107) for skull base lesions, pituitary adenomas (n = 14), low-grade gliomas (n = 51) as well as high-grade gliomas (n = 55) for brain tumors. In all patients treatment could be completed without any unexpected severe toxicities. No side effects > CTC Grade III were observed. To date, no severe late toxicities were observed, however, for endpoints such as secondary malignancies or neuro cognitive side effects follow-up time still remains too short. Local recurrences were mainly seen in the group of high-grade gliomas or atypical meningiomas; for benign skull base meningiomas, to date, no recurrences were observed during follow-up. Conclusion: The specific benefit of particle therapy will potentially reduce the risk of secondary malignancies as well as improve neuro cognitive outcome and quality of life (QOL); thus, longer follow-up will be necessary to confirm these endpoints. Indication-specific trials on meningiomas and gliomas are underway to elucidate the role of protons and carbon ions in these indications.

  15. Round Randomized Learning Vector Quantization for Brain Tumor Imaging

    Directory of Open Access Journals (Sweden)

    Siti Norul Huda Sheikh Abdullah

    2016-01-01

    Full Text Available Brain magnetic resonance imaging (MRI classification into normal and abnormal is a critical and challenging task. Owing to that, several medical imaging classification techniques have been devised in which Learning Vector Quantization (LVQ is amongst the potential. The main goal of this paper is to enhance the performance of LVQ technique in order to gain higher accuracy detection for brain tumor in MRIs. The classical way of selecting the winner code vector in LVQ is to measure the distance between the input vector and the codebook vectors using Euclidean distance function. In order to improve the winner selection technique, round off function is employed along with the Euclidean distance function. Moreover, in competitive learning classifiers, the fitting model is highly dependent on the class distribution. Therefore this paper proposed a multiresampling technique for which better class distribution can be achieved. This multiresampling is executed by using random selection via preclassification. The test data sample used are the brain tumor magnetic resonance images collected from Universiti Kebangsaan Malaysia Medical Center and UCI benchmark data sets. Comparative studies showed that the proposed methods with promising results are LVQ1, Multipass LVQ, Hierarchical LVQ, Multilayer Perceptron, and Radial Basis Function.

  16. MR Vascular Fingerprinting in Stroke and Brain Tumors Models.

    Science.gov (United States)

    Lemasson, B; Pannetier, N; Coquery, N; Boisserand, Ligia S B; Collomb, Nora; Schuff, N; Moseley, M; Zaharchuk, G; Barbier, E L; Christen, T

    2016-11-24

    In this study, we evaluated an MRI fingerprinting approach (MRvF) designed to provide high-resolution parametric maps of the microvascular architecture (i.e., blood volume fraction, vessel diameter) and function (blood oxygenation) simultaneously. The method was tested in rats (n = 115), divided in 3 models: brain tumors (9 L, C6, F98), permanent stroke, and a control group of healthy animals. We showed that fingerprinting can robustly distinguish between healthy and pathological brain tissues with different behaviors in tumor and stroke models. In particular, fingerprinting revealed that C6 and F98 glioma models have similar signatures while 9 L present a distinct evolution. We also showed that it is possible to improve the results of MRvF and obtain supplemental information by changing the numerical representation of the vascular network. Finally, good agreement was found between MRvF and conventional MR approaches in healthy tissues and in the C6, F98, and permanent stroke models. For the 9 L glioma model, fingerprinting showed blood oxygenation measurements that contradict results obtained with a quantitative BOLD approach. In conclusion, MR vascular fingerprinting seems to be an efficient technique to study microvascular properties in vivo. Multiple technical improvements are feasible and might improve diagnosis and management of brain diseases.

  17. Therapeutic Potential of Curcumin for the Treatment of Brain Tumors

    Directory of Open Access Journals (Sweden)

    Neil V. Klinger

    2016-01-01

    Full Text Available Brain malignancies currently carry a poor prognosis despite the current multimodal standard of care that includes surgical resection and adjuvant chemotherapy and radiation. As new therapies are desperately needed, naturally occurring chemical compounds have been studied for their potential chemotherapeutic benefits and low toxicity profile. Curcumin, found in the rhizome of turmeric, has extensive therapeutic promise via its antioxidant, anti-inflammatory, and antiproliferative properties. Preclinical in vitro and in vivo data have shown it to be an effective treatment for brain tumors including glioblastoma multiforme. These effects are potentiated by curcumin’s ability to induce G2/M cell cycle arrest, activation of apoptotic pathways, induction of autophagy, disruption of molecular signaling, inhibition of invasion, and metastasis and by increasing the efficacy of existing chemotherapeutics. Further, clinical data suggest that it has low toxicity in humans even at large doses. Curcumin is a promising nutraceutical compound that should be evaluated in clinical trials for the treatment of human brain tumors.

  18. The fibrinolytic system facilitates tumor cell migration across the blood-brain barrier in experimental melanoma brain metastasis

    International Nuclear Information System (INIS)

    Perides, George; Zhuge, Yuzheng; Lin, Tina; Stins, Monique F; Bronson, Roderick T; Wu, Julian K

    2006-01-01

    Patients with metastatic tumors to the brain have a very poor prognosis. Increased metastatic potential has been associated with the fibrinolytic system. We investigated the role of the fibrinolytic enzyme plasmin in tumor cell migration across brain endothelial cells and growth of brain metastases in an experimental metastatic melanoma model. Metastatic tumors to the brain were established by direct injection into the striatum or by intracarotid injection of B16F10 mouse melanoma cells in C57Bl mice. The role of plasminogen in the ability of human melanoma cells to cross a human blood-brain barrier model was studied on a transwell system. Wild type mice treated with the plasmin inhibitor epsilon-aminocaproic acid (EACA) and plg -/- mice developed smaller tumors and survived longer than untreated wild type mice. Tumors metastasized to the brain of wild type mice treated with EACA and plg -/- less efficiently than in untreated wild type mice. No difference was observed in the tumor growth in any of the three groups of mice. Human melanoma cells were able to cross the human blood-brain barrier model in a plasmin dependent manner. Plasmin facilitates the development of tumor metastasis to the brain. Inhibition of the fibrinolytic system could be considered as means to prevent tumor metastasis to the brain

  19. A semiautomatic CT-based ensemble segmentation of lung tumors: comparison with oncologists’ delineations and with the surgical specimen

    Science.gov (United States)

    Velazquez, Emmanuel Rios; Aerts, Hugo J. W. L.; Gu, Yuhua; Goldgof, Dmitry B.; De Ruysscher, Dirk; Dekker, Andre; Korn, René; Gillies, Robert J.; Lambin, Philippe

    2013-01-01

    Purpose To assess the clinical relevance of a semiautomatic CT-based ensemble segmentation method, by comparing it to pathology and to CT/PET manual delineations by five independent radiation oncologists in non-small cell lung cancer (NSCLC). Materials and Methods For twenty NSCLC patients (stage Ib – IIIb) the primary tumor was delineated manually on CT/PET scans by five independent radiation oncologists and segmented using a CT based semi-automatic tool. Tumor volume and overlap fractions between manual and semiautomatic-segmented volumes were compared. All measurements were correlated with the maximal diameter on macroscopic examination of the surgical specimen. Imaging data is available on www.cancerdata.org. Results High overlap fractions were observed between the semi-automatically segmented volumes and the intersection (92.5 ± 9.0, mean ± SD) and union (94.2 ± 6.8) of the manual delineations. No statistically significant differences in tumor volume were observed between the semiautomatic segmentation (71.4 ± 83.2 cm3, mean ± SD) and manual delineations (81.9 ± 94.1 cm3; p = 0.57). The maximal tumor diameter of the semiautomatic-segmented tumor correlated strongly with the macroscopic diameter of the primary tumor (r = 0.96). Conclusion Semiautomatic segmentation of the primary tumor on CT demonstrated high agreement with CT/PET manual delineations and strongly correlated with the macroscopic diameter considered the “gold standard”. This method may be used routinely in clinical practice and could be employed as a starting point for treatment planning, target definition in multi-center clinical trials or for high throughput data mining research. This method is particularly suitable for peripherally located tumors. PMID:23157978

  20. Targeting BRAF V600E and Autophagy in Pediatric Brain Tumors

    Science.gov (United States)

    2015-10-01

    for childhood central nervous system (CNS) tumors, they remain the leading cause of death in pediatric oncology . One potential therapeutic...clinical trial design for pediatric brain tumor patients harboring the mutation. Keywords: Autophagy BRAF Brain tumor Pediatric Resistance...I submitted an abstract of my most recent findings to the Society of Neuro- Oncology Pediatric Neuro- Oncology Basic and Translational Research

  1. Increased Delay Between Gadolinium Chelate Administration and T1-Weighted Magnetic Resonance Imaging Acquisition Increases Contrast-Enhancing Tumor Volumes and T1 Intensities in Brain Tumor Patients.

    Science.gov (United States)

    Piechotta, Paula L; Bonekamp, David; Sill, Martin; Wick, Antje; Wick, Wolfgang; Bendszus, Martin; Kickingereder, Philipp

    2018-04-01

    The aim of this study was to evaluate the impact of delayed T1-weighted (T1-w) MRI acquisition after gadolinium chelate administration on brain tumor volumes and T1-w intensities. Fifty-five patients with histologically confirmed, contrast-enhancing intra-axial brain tumors were analyzed in this prospective test-retest study. Patients underwent 2 consecutive 3 T MRI scans (separated by a 1-minute break) during routine follow-up with contrast-enhanced T1 (ceT1-w), T2, and FLAIR acquisition. Macrocyclic gadolinium chelate-based contrast agent was only administered before the first ceT1-w acquisition; median latency to ceT1-w acquisition was 6.72 minutes (IQR, 6.53-6.92) in the first and 16.27 minutes (IQR, 15.49-17.26) in the second scan. Changes in tumor volumes and relative ceT1-w intensities between the 2 acquisitions were quantitatively assessed following semiautomated tumor segmentation (separately for contrast-enhancement [CE], necrosis [NEC], and nonenhancing [NE] tumor). Semiautomatically segmented CE tumor volumes were significantly larger in the second acquisition (median +32% [1.2 cm]; IQR, 16%-62%; P < 0.01), which corresponded to a 10% increase in CE tumor diameter (+0.3 cm). Contrarily, NEC and NE tumor volumes were significantly smaller (median -24% [IQR, -36% to -54%], P < 0.01 for NEC and -2% [IQR, -1% to -3%], P = 0.02 for NE tumor). Bland-Altman plots confirmed a proportional bias toward higher CE and lower NEC volumes for the second ceT1-w acquisition. Relative ceT1-w intensities for both early- (regions already enhancing in the first scan) and late-enhancing (newly enhancing regions in the second scan) tumor were significantly increased in the second acquisition (by 5.8% and 27.3% [P < 0.01, respectively]). Linear-mixed effects modeling confirmed that the increase in CE volumes and CE intensities is a function of the interval between contrast agent injection and ceT1-w acquisition (P < 0.01 each). Our study indicates that the maximum extent of CE

  2. A neuropathology-based approach to epilepsy surgery in brain tumors and proposal for a new terminology use for long-term epilepsy-associated brain tumors

    NARCIS (Netherlands)

    Blumcke, Ingmar; Aronica, Eleonora; Urbach, Horst; Alexopoulos, Andreas; Gonzalez-Martinez, Jorge A.

    2014-01-01

    Every fourth patient submitted to epilepsy surgery suffers from a brain tumor. Microscopically, these neoplasms present with a wide-ranging spectrum of glial or glio-neuronal tumor subtypes. Gangliogliomas (GG) and dysembryoplastic neuroepithelial tumors (DNTs) are the most frequently recognized

  3. 4D segmentation of brain MR images with constrained cortical thickness variation.

    Directory of Open Access Journals (Sweden)

    Li Wang

    Full Text Available Segmentation of brain MR images plays an important role in longitudinal investigation of developmental, aging, disease progression changes in the cerebral cortex. However, most existing brain segmentation methods consider multiple time-point images individually and thus cannot achieve longitudinal consistency. For example, cortical thickness measured from the segmented image will contain unnecessary temporal variations, which will affect the time related change pattern and eventually reduce the statistical power of analysis. In this paper, we propose a 4D segmentation framework for the adult brain MR images with the constraint of cortical thickness variations. Specifically, we utilize local intensity information to address the intensity inhomogeneity, spatial cortical thickness constraint to maintain the cortical thickness being within a reasonable range, and temporal cortical thickness variation constraint in neighboring time-points to suppress the artificial variations. The proposed method has been tested on BLSA dataset and ADNI dataset with promising results. Both qualitative and quantitative experimental results demonstrate the advantage of the proposed method, in comparison to other state-of-the-art 4D segmentation methods.

  4. Research on segmentation based on multi-atlas in brain MR image

    Science.gov (United States)

    Qian, Yuejing

    2018-03-01

    Accurate segmentation of specific tissues in brain MR image can be effectively achieved with the multi-atlas-based segmentation method, and the accuracy mainly depends on the image registration accuracy and fusion scheme. This paper proposes an automatic segmentation method based on the multi-atlas for brain MR image. Firstly, to improve the registration accuracy in the area to be segmented, we employ a target-oriented image registration method for the refinement. Then In the label fusion, we proposed a new algorithm to detect the abnormal sparse patch and simultaneously abandon the corresponding abnormal sparse coefficients, this method is made based on the remaining sparse coefficients combined with the multipoint label estimator strategy. The performance of the proposed method was compared with those of the nonlocal patch-based label fusion method (Nonlocal-PBM), the sparse patch-based label fusion method (Sparse-PBM) and majority voting method (MV). Based on our experimental results, the proposed method is efficient in the brain MR images segmentation compared with MV, Nonlocal-PBM, and Sparse-PBM methods.

  5. Temporal filtering of longitudinal brain magnetic resonance images for consistent segmentation

    Directory of Open Access Journals (Sweden)

    Snehashis Roy

    2016-01-01

    Full Text Available Longitudinal analysis of magnetic resonance images of the human brain provides knowledge of brain changes during both normal aging as well as the progression of many diseases. Previous longitudinal segmentation methods have either ignored temporal information or have incorporated temporal consistency constraints within the algorithm. In this work, we assume that some anatomical brain changes can be explained by temporal transitions in image intensities. Once the images are aligned in the same space, the intensities of each scan at the same voxel constitute a temporal (or 4D intensity trend at that voxel. Temporal intensity variations due to noise or other artifacts are corrected by a 4D intensity-based filter that smooths the intensity values where appropriate, while preserving real anatomical changes such as atrophy. Here smoothing refers to removal of sudden changes or discontinuities in intensities. Images processed with the 4D filter can be used as a pre-processing step to any segmentation method. We show that such a longitudinal pre-processing step produces robust and consistent longitudinal segmentation results, even when applying 3D segmentation algorithms. We compare with state-of-the-art 4D segmentation algorithms. Specifically, we experimented on three longitudinal datasets containing 4–12 time-points, and showed that the 4D temporal filter is more robust and has more power in distinguishing between healthy subjects and those with dementia, mild cognitive impairment, as well as different phenotypes of multiple sclerosis.

  6. A comparative study of segmentation techniques for the quantification of brain subcortical volume.

    Science.gov (United States)

    Akudjedu, Theophilus N; Nabulsi, Leila; Makelyte, Migle; Scanlon, Cathy; Hehir, Sarah; Casey, Helen; Ambati, Srinath; Kenney, Joanne; O'Donoghue, Stefani; McDermott, Emma; Kilmartin, Liam; Dockery, Peter; McDonald, Colm; Hallahan, Brian; Cannon, Dara M

    2018-02-13

    Manual tracing of magnetic resonance imaging (MRI) represents the gold standard for segmentation in clinical neuropsychiatric research studies, however automated approaches are increasingly used due to its time limitations. The accuracy of segmentation techniques for subcortical structures has not been systematically investigated in large samples. We compared the accuracy of fully automated [(i) model-based: FSL-FIRST; (ii) patch-based: volBrain], semi-automated (FreeSurfer) and stereological (Measure®) segmentation techniques with manual tracing (ITK-SNAP) for delineating volumes of the caudate (easy-to-segment) and the hippocampus (difficult-to-segment). High resolution 1.5 T T1-weighted MR images were obtained from 177 patients with major psychiatric disorders and 104 healthy participants. The relative consistency (partial correlation), absolute agreement (intraclass correlation coefficient, ICC) and potential technique bias (Bland-Altman plots) of each technique was compared with manual segmentation. Each technique yielded high correlations (0.77-0.87, p segmentation for the caudate. For the hippocampus, stereology yielded good consistency (0.52-0.55, p segmentation of the hippocampus and using FreeSurfer for segmentation of the caudate. In a typical neuropsychiatric MRI dataset, automated segmentation techniques provide good accuracy for an easy-to-segment structure such as the caudate, whereas for the hippocampus, a reasonable correlation with volume but poor absolute agreement was demonstrated. This indicates manual or stereological volume estimation should be considered for studies that require high levels of precision such as those with small sample size.

  7. Lead, genetic susceptibility, and risk of adult brain tumors.

    Science.gov (United States)

    Rajaraman, Preetha; Stewart, Patricia A; Samet, Jonathan M; Schwartz, Brian S; Linet, Martha S; Zahm, Shelia Hoar; Rothman, Nathaniel; Yeager, Meredith; Fine, Howard A; Black, Peter M; Loeffler, Jay; Shapiro, William R; Selker, Robert G; Inskip, Peter D

    2006-12-01

    Although few etiologic factors for brain tumors have been identified, limited data suggest that lead may increase the risk of brain tumors, particularly meningioma. The ALAD G177C polymorphism affects the toxicokinetics of lead and may confer genetic susceptibility to adverse effects of lead exposure. We examined occupational exposure to lead and risk of brain tumors in a multisite, hospital-based, case-control study of 489 patients with glioma, 197 with meningioma, and 799 non-cancer controls frequency matched on hospital, age, sex, race/ethnicity, and residential proximity to hospital. ALAD genotype was assessed by a Taqman assay for 355 glioma patients, 151 meningioma patients, and 505 controls. Exposure to lead was estimated using a rigorous questionnaire-based exposure assessment strategy incorporating lead measurement and other occupational data abstracted from published articles and reports. Increased risk of meningioma with occupational lead exposure (estimated by odds ratios and 95% confidence intervals) was most apparent in individuals with the ALAD2 variant allele, for whom risk increased from 1.1 (0.3-4.5) to 5.6 (0.7-45.5) and 12.8 (1.4-120.8) for estimated cumulative lead exposures of 1 to 49 microg/m3-y, 50 to 99 microg/m3-y, and >or=100 microg/m3-y, respectively, compared with unexposed individuals (two-sided P trend = 0.06). This relationship became stronger after excluding occupational lead exposures characterized by a low confidence level or occurring in the 10 years before meningioma diagnosis. Occupational lead exposure was not associated with glioma risk. Although our results indicate that lead may be implicated in meningioma risk in genetically susceptible individuals, these results need to be interpreted with caution given the small numbers of exposed cases with a variant genotype.

  8. Local anesthetics for brain tumor resection: current perspectives

    Science.gov (United States)

    Potters, Jan-Willem

    2018-01-01

    This review summarizes the added value of local anesthetics in patients undergoing craniotomy for brain tumor resection, which is a procedure that is carried out frequently in neurosurgical practice. The procedure can be carried out under general anesthesia, sedation with local anesthesia or under local anesthesia only. Literature shows a large variation in the postoperative pain intensity ranging from no postoperative analgesia requirement in two-thirds of the patients up to a rate of 96% of the patients suffering from severe postoperative pain. The only identified causative factor predicting higher postoperative pain scores is infratentorial surgery. Postoperative analgesia can be achieved with multimodal pain management where local anesthesia is associated with lower postoperative pain intensity, reduction in opioid requirement and prevention of development of chronic pain. In awake craniotomy patients, sufficient local anesthesia is a cornerstone of the procedure. An awake craniotomy and brain tumor resection can be carried out completely under local anesthesia only. However, the use of sedative drugs is common to improve patient comfort during craniotomy and closure. Local anesthesia for craniotomy can be performed by directly blocking the six different nerves that provide the sensory innervation of the scalp, or by local infiltration of the surgical site and the placement of the pins of the Mayfield clamp. Direct nerve block has potential complications and pitfalls and is technically more challenging, but mostly requires lower total doses of the local anesthetics than the doses required in surgical-site infiltration. Due to a lack of comparative studies, there is no evidence showing superiority of one technique versus the other. Besides the use of other local anesthetics for analgesia, intravenous lidocaine administration has proven to be a safe and effective method in the prevention of coughing during emergence from general anesthesia and extubation, which

  9. Spatial organization and correlations of cell nuclei in brain tumors.

    Directory of Open Access Journals (Sweden)

    Yang Jiao

    Full Text Available Accepting the hypothesis that cancers are self-organizing, opportunistic systems, it is crucial to understand the collective behavior of cancer cells in their tumorous heterogeneous environment. In the present paper, we ask the following basic question: Is this self-organization of tumor evolution reflected in the manner in which malignant cells are spatially distributed in their heterogeneous environment? We employ a variety of nontrivial statistical microstructural descriptors that arise in the theory of heterogeneous media to characterize the spatial distributions of the nuclei of both benign brain white matter cells and brain glioma cells as obtained from histological images. These descriptors, which include the pair correlation function, structure factor and various nearest neighbor functions, quantify how pairs of cell nuclei are correlated in space in various ways. We map the centroids of the cell nuclei into point distributions to show that while commonly used local spatial statistics (e.g., cell areas and number of neighboring cells cannot clearly distinguish spatial correlations in distributions of normal and abnormal cell nuclei, their salient structural features are captured very well by the aforementioned microstructural descriptors. We show that the tumorous cells pack more densely than normal cells and exhibit stronger effective repulsions between any pair of cells. Moreover, we demonstrate that brain gliomas are organized in a collective way rather than randomly on intermediate and large length scales. The existence of nontrivial spatial correlations between the abnormal cells strongly supports the view that cancer is not an unorganized collection of malignant cells but rather a complex emergent integrated system.

  10. Spatial organization and correlations of cell nuclei in brain tumors.

    Science.gov (United States)

    Jiao, Yang; Berman, Hal; Kiehl, Tim-Rasmus; Torquato, Salvatore

    2011-01-01

    Accepting the hypothesis that cancers are self-organizing, opportunistic systems, it is crucial to understand the collective behavior of cancer cells in their tumorous heterogeneous environment. In the present paper, we ask the following basic question: Is this self-organization of tumor evolution reflected in the manner in which malignant cells are spatially distributed in their heterogeneous environment? We employ a variety of nontrivial statistical microstructural descriptors that arise in the theory of heterogeneous media to characterize the spatial distributions of the nuclei of both benign brain white matter cells and brain glioma cells as obtained from histological images. These descriptors, which include the pair correlation function, structure factor and various nearest neighbor functions, quantify how pairs of cell nuclei are correlated in space in various ways. We map the centroids of the cell nuclei into point distributions to show that while commonly used local spatial statistics (e.g., cell areas and number of neighboring cells) cannot clearly distinguish spatial correlations in distributions of normal and abnormal cell nuclei, their salient structural features are captured very well by the aforementioned microstructural descriptors. We show that the tumorous cells pack more densely than normal cells and exhibit stronger effective repulsions between any pair of cells. Moreover, we demonstrate that brain gliomas are organized in a collective way rather than randomly on intermediate and large length scales. The existence of nontrivial spatial correlations between the abnormal cells strongly supports the view that cancer is not an unorganized collection of malignant cells but rather a complex emergent integrated system.

  11. Anxiety in the preoperative phase of awake brain tumor surgery.

    Science.gov (United States)

    Ruis, Carla; Wajer, Irene Huenges; Robe, Pierre; van Zandvoort, Martine

    2017-06-01

    Awake surgery emerges as a standard of care for brain tumors located in or near eloquent areas. Levels of preoperative anxiety in patients are important, because anxiety can influence cognitive performance and participation, hence altering the outcome of the procedure. In this study we analyzed the prevalence and potential clinical predictors of anxiety in the pre-operative phase of an awake brain tumor surgery. Seventy consecutive candidates for an awake brain tumor surgery were included. All patients received a neuropsychological pre-operative work-up. The Hospital Anxiety and Depression Scale (HADS) was administrated to investigate symptoms of anxiety. Demographic and medical data were extracted from patients' charts. Linear regression analyses, multiple regression analyses, t-tests for parametric and Mann-Whitney U tests for non-parametric data were used to analyze the relation between demographic and medical variables and pre-operative anxiety. Mean score on the anxiety scale of the HADS was 6.1 (SD=4.2, range 1-19) and 25% of the patients scored on or above the cut-off for anxiety symptoms (score >7). Women reported higher levels of anxiety than men (p<0.01). Furthermore, younger patient were more anxious than older patients (p<0.05). No other variables were significantly related to pre-operative anxiety. Merely, one in every four patients reported significant anxiety symptoms in the pre-operative phase. Besides gender and age, none of the other demographic or medical factors were significantly associated with the level of anxiety. Copyright © 2017 Elsevier B.V. All rights reserved.

  12. Brain tumors and CT scans in infants and children, (4)

    International Nuclear Information System (INIS)

    Oi, Shizuo

    1983-01-01

    We have analyzed the features of individual brain and spinal tumors in infants and children. As to factors affecting prognosis, we have previously reported that the location of a tumor has more distinctive correlations with prognosis than the histological malignancy of a tumor, especially in ependymoma. Based on these results, this study was designed to analyze the CT findings on 9 brain and spinal ependymomas. Seven were seen in the cranium: 5 in the fourth ventricle and 2 in the lateral ventricle. The remaining two were found in the spine: one, in the spinal cord between C 2 and T 10 (spinal level), and the other, in the filum terminale. The ependymomas in the 4th ventricle varied in density on plain CT, peritumoral findings, contast-enhancement pattern, and other observations. At present, it seems to be difficult to distinguish ependymoma from medulloblastoma or 4th ventricular astrocytoma, which tends to have uniform characteristics. However, there are some 4th ventricular ependymomas with a tongue-like projection to the CP angle or cervical canal, the so-called ''plastic ependymoma'' according to Courville and Broussalian. In such a case, ependymoma is strongly suspected on the basis of the CT findings. In the 2 lateral ventricular ependymomas, the relations with the ventricle were rather obscure and the ventricle was collapsed, while all the 4th ventricular ependymomas had hydrocephalus. Our findings contrast with others, reported elsewhere, that lateral ventricular ependymoma advanced into the ventricle and led to prominent ventricular enlargement. Thus, it can be said that ependymoma had a wide variety of supratentorial CT findings as well as contrast-enhancement patterns. Giant tumors growing in the spinal medulla were totally removed because of their clear boundaries with the peripheral regions. A mixed density was frequently seen on CT. (author)

  13. Growth hormone deficiency in children with brain tumors

    International Nuclear Information System (INIS)

    Shalet, S.M.; Beardwell, C.G.; Morris-Jones, P.; Bamford, F.N.; Ribeiro, G.G.; Pearson, D.

    1976-01-01

    Nine children with brain tumors are described who have received various combinations of treatment, including surgery, radiotherapy, and chemotherapy. Many of the children were noted to be of short stature. Endocrine assessment was carried out from 2 to 10 years after treatment. The combined results of insulin tolerance and Bovril stimulation tests show an impaired growth hormone response in six of the nine children. Bone age is retarded in all cases, and the present height is below the 10th percentile in five of the six. The cause of this growth hormone deficiency is obscure, but further studies are in progress

  14. Imaging features of brain tumor-like lesions

    International Nuclear Information System (INIS)

    Silva, Matheus Fonseca Barbosa; Lisboa, Joao Paulo Ribeiro; Pontes, Bruno de Castro Nogueira; Guedes, Marcelo dos Santos; Silva, Marcia Lopes da; Mello, Marco Antonio Rocha

    2008-01-01

    The purpose of this study is to demonstrate the image aspects of the main pathologies of the brain that may simulate tumors. It was made a retrospective evaluation of our institution patients. The following pathologies were diagnosed: multiple sclerosis, neurosarcoidosis, neurocysticercosis, neurotoxoplasmosis, radionecrosis and stroke. Differential diagnosis among these diseases and neoplastic lesions can be difficult, though imaging technology has advanced rapidly and associated to the current knowledge of the main findings of each one of them may become this task less strenuous. (author)

  15. MAVEN: An Algorithm for Multi-Parametric Automated Segmentation of Brain Veins From Gradient Echo Acquisitions.

    Science.gov (United States)

    Monti, Serena; Cocozza, Sirio; Borrelli, Pasquale; Straub, Sina; Ladd, Mark E; Salvatore, Marco; Tedeschi, Enrico; Palma, Giuseppe

    2017-05-01

    Cerebral vein analysis provides a chance to study, from an unusual viewpoint, an entire class of brain diseases, including neurodegenerative disorders and traumatic brain injuries. Manual segmentation approaches can be used to assess vascular anatomy, but they are observer-dependent and time-consuming; therefore, automated approaches are desirable, as they also improve reproducibility. In this paper, a new, fully automated algorithm, based on structural, morphological, and relaxometric information, is proposed to segment the entire cerebral venous system from MR images. The algorithm for multi-parametric automated segmentation of brain VEiNs (MAVEN) is based on a combined investigation of multi-parametric information that allows for rejection of false positives and detection of thin vessels. The method is tested on gradient echo brain data sets acquired at 1.5, 3, and 7 T. It is compared to previous methods against manual segmentation, and its inter-scan reproducibility is assessed. The achieved accuracy and reproducibility are good, meaning that MAVEN outperforms previous methods on both quantitative and qualitative analyses. It is usable at all the field strengths explored, showing comparable accuracy scores, with no need for algorithm parameter adjustments, and thus, it is a promising candidate for the characterization of the venous tree topology.

  16. Local appearance features for robust MRI brain structure segmentation across scanning protocols

    DEFF Research Database (Denmark)

    Achterberg, H.C.; Poot, Dirk H. J.; van der Lijn, Fedde

    2013-01-01

    Segmentation of brain structures in magnetic resonance images is an important task in neuro image analysis. Several papers on this topic have shown the benefit of supervised classification based on local appearance features, often combined with atlas-based approaches. These methods require a repr...

  17. Robust kernelized local information fuzzy C-means clustering for brain magnetic resonance image segmentation.

    Science.gov (United States)

    Elazab, Ahmed; AbdulAzeem, Yousry M; Wu, Shiqian; Hu, Qingmao

    2016-03-17

    Brain tissue segmentation from magnetic resonance (MR) images is an importance task for clinical use. The segmentation process becomes more challenging in the presence of noise, grayscale inhomogeneity, and other image artifacts. In this paper, we propose a robust kernelized local information fuzzy C-means clustering algorithm (RKLIFCM). It incorporates local information into the segmentation process (both grayscale and spatial) for more homogeneous segmentation. In addition, the Gaussian radial basis kernel function is adopted as a distance metric to replace the standard Euclidean distance. The main advantages of the new algorithm are: efficient utilization of local grayscale and spatial information, robustness to noise, ability to preserve image details, free from any parameter initialization, and with high speed as it runs on image histogram. We compared the proposed algorithm with 7 soft clustering algorithms that run on both image histogram and image pixels to segment brain MR images. Experimental results demonstrate that the proposed RKLIFCM algorithm is able to overcome the influence of noise and achieve higher segmentation accuracy with low computational complexity.

  18. 3D geometric split-merge segmentation of brain MRI datasets.

    Science.gov (United States)

    Marras, Ioannis; Nikolaidis, Nikolaos; Pitas, Ioannis

    2014-05-01

    In this paper, a novel method for MRI volume segmentation based on region adaptive splitting and merging is proposed. The method, called Adaptive Geometric Split Merge (AGSM) segmentation, aims at finding complex geometrical shapes that consist of homogeneous geometrical 3D regions. In each volume splitting step, several splitting strategies are examined and the most appropriate is activated. A way to find the maximal homogeneity axis of the volume is also introduced. Along this axis, the volume splitting technique divides the entire volume in a number of large homogeneous 3D regions, while at the same time, it defines more clearly small homogeneous regions within the volume in such a way that they have greater probabilities of survival at the subsequent merging step. Region merging criteria are proposed to this end. The presented segmentation method has been applied to brain MRI medical datasets to provide segmentation results when each voxel is composed of one tissue type (hard segmentation). The volume splitting procedure does not require training data, while it demonstrates improved segmentation performance in noisy brain MRI datasets, when compared to the state of the art methods. Copyright © 2014 Elsevier Ltd. All rights reserved.

  19. Utility and limitation of radiosurgery for metastatic brain tumors

    International Nuclear Information System (INIS)

    Kagawa, Kota; Kiya, Katsuzo; Satoh, Hideki; Mizoue, Tatsuya; Matsushige, Toshinori; Araki, Hayato; Akimitsu, Tomohide

    2003-01-01

    The purpose of this study was to evaluate the utility and limitations of radiosurgery for metastatic brain lesions, and to compare the clinical results of stereotactic radiosurgery (SRS) with those of whole-brain radiation therapy (WBRT) in 45 patients with metastatic brain tumors. The patients were divided into two groups: the SRS group (22 patients) and the WBRT group (23 patients). Mean survival was not significantly different between the two groups. However, in patients with 6 or more lesions, both survival time and recurrence-free time in the SRS group were inferior to those in the WBRT group. The main complication in the SRS group was perifocal edema, while dementia was seen in the WBRT group. The bedridden period was longer in the WBRT group than in the SRS group. Death caused by brain lesions was rare in both groups. From these results, SRS preserves high quality of life longer than WBRT, but SRS should be cautiously used in patients with 6 or more lesions. (author)

  20. Aberrant paramagnetic signals outside the tumor volume on routine surveillance MRI of brain tumor patients.

    Science.gov (United States)

    Yust-Katz, Shlomit; Inbar, Edna; Michaeli, Natalia; Limon, Dror; Siegal, Tali

    2017-09-01

    Late complications of cerebral radiation therapy (RT) involve vascular injury with acquired cavernous malformation, telangiectasias and damage to vascular walls which are well recognized in children. Its incidence in adults is unknown. Blood products and iron deposition that accompany vascular injury create paramagnetic effects on MRI. This study retrospectively investigated the frequency of paramagnetic lesions on routine surveillance MRI of adult brain tumor patients. MRI studies of 115 brain tumor patients were reviewed. Only studies containing sequences of either susceptibility weighted images or gradient echo or blood oxygenation level dependent imaging were included. Lesions inside the tumor volume were not considered. 68 studies fulfilled the above criteria and included 48 patients with previous RT (35 followed for >2 years and 13 for 1 year) and 20 patients who were not treated with RT. The median age at time of irradiation was 47 years. Aberrant paramagnetic lesions were found in 23/35 (65%) patients followed for >2 years after RT and in only 1/13 (8%) patients followed for 1-year after radiation (p = 0.03). The 1-year follow-up group did not differ from the control group [2/20 (9%)]. Most lesions were within the radiation field and none of the patients had related symptomatology. The number and incidence of these lesions increased with time and amounted to 75% over 3 years post RT. MRI paramagnetic signal aberrations are common findings in adult brain tumor patients that evolve over time after RT. The clinical significance of these lesions needs further investigation.

  1. Automated and Semiautomated Segmentation of Rectal Tumor Volumes on Diffusion-Weighted MRI: Can It Replace Manual Volumetry?

    Energy Technology Data Exchange (ETDEWEB)

    Heeswijk, Miriam M. van [Department of Radiology, Maastricht University Medical Centre, Maastricht (Netherlands); Department of Surgery, Maastricht University Medical Centre, Maastricht (Netherlands); Lambregts, Doenja M.J., E-mail: d.lambregts@nki.nl [Department of Radiology, Maastricht University Medical Centre, Maastricht (Netherlands); Department of Radiology, The Netherlands Cancer Institute, Amsterdam (Netherlands); Griethuysen, Joost J.M. van [GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht (Netherlands); Department of Radiology, The Netherlands Cancer Institute, Amsterdam (Netherlands); Oei, Stanley [Department of Radiology, Maastricht University Medical Centre, Maastricht (Netherlands); Rao, Sheng-Xiang [Department of Radiology, Maastricht University Medical Centre, Maastricht (Netherlands); Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai (China); Graaff, Carla A.M. de [Department of Radiology, Maastricht University Medical Centre, Maastricht (Netherlands); Vliegen, Roy F.A. [Atrium Medical Centre Parkstad/Zuyderland Medical Centre, Heerlen (Netherlands); Beets, Geerard L. [GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht (Netherlands); Department of Surgery, The Netherlands Cancer Institute, Amsterdam (Netherlands); Papanikolaou, Nikos [Laboratory of Computational Medicine, Institute of Computer Science, FORTH, Heraklion, Crete (Greece); Beets-Tan, Regina G.H. [GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht (Netherlands); Department of Radiology, The Netherlands Cancer Institute, Amsterdam (Netherlands)

    2016-03-15

    Purpose: Diffusion-weighted imaging (DWI) tumor volumetry is promising for rectal cancer response assessment, but an important drawback is that manual per-slice tumor delineation can be highly time consuming. This study investigated whether manual DWI-volumetry can be reproduced using a (semi)automated segmentation approach. Methods and Materials: Seventy-nine patients underwent magnetic resonance imaging (MRI) that included DWI (highest b value [b1000 or b1100]) before and after chemoradiation therapy (CRT). Tumor volumes were assessed on b1000 (or b1100) DWI before and after CRT by means of (1) automated segmentation (by 2 inexperienced readers), (2) semiautomated segmentation (manual adjustment of the volumes obtained by method 1 by 2 radiologists), and (3) manual segmentation (by 2 radiologists); this last assessment served as the reference standard. Intraclass correlation coefficients (ICC) and Dice similarity indices (DSI) were calculated to evaluate agreement between different methods and observers. Measurement times (from a radiologist's perspective) were recorded for each method. Results: Tumor volumes were not significantly different among the 3 methods, either before or after CRT (P=.08 to .92). ICCs compared to manual segmentation were 0.80 to 0.91 and 0.53 to 0.66 before and after CRT, respectively, for the automated segmentation and 0.91 to 0.97 and 0.61 to 0.75, respectively, for the semiautomated method. Interobserver agreement (ICC) pre and post CRT was 0.82 and 0.59 for automated segmentation, 0.91 and 0.73 for semiautomated segmentation, and 0.91 and 0.75 for manual segmentation, respectively. Mean DSI between the automated and semiautomated method were 0.83 and 0.58 pre-CRT and post-CRT, respectively; DSI between the automated and manual segmentation were 0.68 and 0.42 and 0.70 and 0.41 between the semiautomated and manual segmentation, respectively. Median measurement time for the radiologists was 0 seconds (pre- and post-CRT) for the

  2. Automated and Semiautomated Segmentation of Rectal Tumor Volumes on Diffusion-Weighted MRI: Can It Replace Manual Volumetry?

    International Nuclear Information System (INIS)

    Heeswijk, Miriam M. van; Lambregts, Doenja M.J.; Griethuysen, Joost J.M. van; Oei, Stanley; Rao, Sheng-Xiang; Graaff, Carla A.M. de; Vliegen, Roy F.A.; Beets, Geerard L.; Papanikolaou, Nikos; Beets-Tan, Regina G.H.

    2016-01-01

    Purpose: Diffusion-weighted imaging (DWI) tumor volumetry is promising for rectal cancer response assessment, but an important drawback is that manual per-slice tumor delineation can be highly time consuming. This study investigated whether manual DWI-volumetry can be reproduced using a (semi)automated segmentation approach. Methods and Materials: Seventy-nine patients underwent magnetic resonance imaging (MRI) that included DWI (highest b value [b1000 or b1100]) before and after chemoradiation therapy (CRT). Tumor volumes were assessed on b1000 (or b1100) DWI before and after CRT by means of (1) automated segmentation (by 2 inexperienced readers), (2) semiautomated segmentation (manual adjustment of the volumes obtained by method 1 by 2 radiologists), and (3) manual segmentation (by 2 radiologists); this last assessment served as the reference standard. Intraclass correlation coefficients (ICC) and Dice similarity indices (DSI) were calculated to evaluate agreement between different methods and observers. Measurement times (from a radiologist's perspective) were recorded for each method. Results: Tumor volumes were not significantly different among the 3 methods, either before or after CRT (P=.08 to .92). ICCs compared to manual segmentation were 0.80 to 0.91 and 0.53 to 0.66 before and after CRT, respectively, for the automated segmentation and 0.91 to 0.97 and 0.61 to 0.75, respectively, for the semiautomated method. Interobserver agreement (ICC) pre and post CRT was 0.82 and 0.59 for automated segmentation, 0.91 and 0.73 for semiautomated segmentation, and 0.91 and 0.75 for manual segmentation, respectively. Mean DSI between the automated and semiautomated method were 0.83 and 0.58 pre-CRT and post-CRT, respectively; DSI between the automated and manual segmentation were 0.68 and 0.42 and 0.70 and 0.41 between the semiautomated and manual segmentation, respectively. Median measurement time for the radiologists was 0 seconds (pre- and post-CRT) for the

  3. The roles of microglia/macrophages in tumor progression of brain cancer and metastatic disease.

    Science.gov (United States)

    Wu, Shih-Ying; Watabe, Kounosuke

    2017-06-01

    Malignant brain tumors and brain metastases are highly aggressive diseases that are often resistant to treatment. Consequently, the current prognosis of patients with brain tumors and metastases is dismal. Activated microglia and macrophages are often observed in close proximity to or within the malignant tumor masses, suggesting that microglia/macrophages play an important role in brain tumor progression. Microglia, being resident macrophages of the central nervous system, form a major component of the brain immune system. They exhibit anti-tumor functions by phagocytosis and the release of cytotoxic factors. However, these microglia/macrophages can be polarized into becoming tumor-supportive and immunosuppressive cells by certain tumor-derived soluble factors, thereby promoting tumor maintenance and progression. The activated microglia/macrophages also participate in the process of tumor angiogenesis, metastasis, dormancy, and relapse. In this review, we discuss the recent literature on the dual roles of microglia/macrophages in brain tumor progression. We have also reviewed the effect of several well-known microglia/macrophages-derived molecules and signals on brain tumor progression and further discussed the potential therapeutic strategies for targeting the pro-tumor and metastatic functions of microglia/macrophages.

  4. Long-term Exposure to Ambient Air Pollution and Incidence of Brain Tumor

    DEFF Research Database (Denmark)

    Andersen, Zorana J; Pedersen, Marie; Weinmayr, Gudrun

    2018-01-01

    Background: Epidemiological evidence on the association between ambient air pollution and brain tumor risk is sparse and inconsistent. Methods: In 12 cohorts from six European countries, individual estimates of annual mean air pollution levels at the baseline residence were estimated...... of air pollutant concentrations and traffic intensity with total, malignant and nonmalignant brain tumor, in separate Cox regression models, adjusting for risk factors, and pooled cohort-specific estimates using random-effects meta-analyses. Results: Of 282,194 subjects from 12 cohorts, 466 developed...... malignant brain tumors during 12 years of follow-up. Six of the cohorts had also data on nonmalignant brain tumor, where among 106,786 subjects, 366 developed brain tumor: 176 nonmalignant and 190 malignant. We found a positive, statistically non-significant association between malignant brain tumor and PM2...

  5. Noninvasive detection of temozolomide in brain tumor xenografts by magnetic resonance spectroscopy

    DEFF Research Database (Denmark)

    Kato, Y.; Holm, David Alberg; Okollie, B.

    2010-01-01

    Poor drug delivery to brain tumors caused by aberrant tumor vasculature and a partly intact blood-brain barrier (BBB) and blood-brain tumor barrier (BTB) can significantly impair the efficacy of chemotherapy. Determining drug delivery to brain tumors is a challenging problem, and the noninvasive......MG human brain cancer. Dynamic magnetic resonance imaging (MRI) with the low-molecular-weight contrast agent, gadolinium diethylenetriaminepentaacetic acid (GdDTPA), was used to evaluate tumor vascular parameters. Carbon-13-labeled TMZ ([C-13]TMZ, 99%) was intraperitoneally administered at a dose...... of similar to 140 mg/kg (450 mg/m(2), well within the maximal clinical dose of 1000 mg/m(2) used in humans) during the course of in vivo MRS experiments. Heteronuclear multiple-quantum coherence (HMQC) MRS of brain tumors was performed before and after i.p. administration of [C-13]TMZ. Dynamic MRI...

  6. Fast and robust multi-atlas segmentation of brain magnetic resonance images

    DEFF Research Database (Denmark)

    Lötjönen, Jyrki Mp; Wolz, Robin; Koikkalainen, Juha R

    2010-01-01

    We introduce an optimised pipeline for multi-atlas brain MRI segmentation. Both accuracy and speed of segmentation are considered. We study different similarity measures used in non-rigid registration. We show that intensity differences for intensity normalised images can be used instead...... modelling based on segmentation using expectation maximisation (EM) and optimisation via graph cuts. The segmentation pipeline is evaluated with two data cohorts: IBSR data (N=18, six subcortial structures: thalamus, caudate, putamen, pallidum, hippocampus, amygdala) and ADNI data (N=60, hippocampus......). The average similarity index between automatically and manually generated volumes was 0.849 (IBSR, six subcortical structures) and 0.880 (ADNI, hippocampus). The correlation coefficient for hippocampal volumes was 0.95 with the ADNI data. The computation time using a standard multicore PC computer was about 3...

  7. A New Method for Segmentation of Multiple Sclerosis (MS Lesions on Brain MR Images

    Directory of Open Access Journals (Sweden)

    Simin Jafari

    2015-07-01

    Full Text Available Automatic segmentation of multiple sclerosis (MS lesions in brain MRI has been widely investigated in recent years with the goal of helping MS diagnosis and patient follow-up. In this study we applied gaussian mixture model (GMM to segment MS lesions in MR images. Usually, GMM is optimized using expectation-maximization (EM algorithm. One of the drawbacks of this optimization method is that, it does not convergence to optimal maximum or minimum. Starting from different initial points and saving best result, is a strategy which is used to reach the near optimal. This approach is time consuming and we used another way to initiate the EM algorithm. Also, FAST- Trimmed Likelihood Estimator (FAST-TLE algorithm was applied to determine which voxels should be rejected. The automatically segmentation outputs were scored by two specialists and the results show that our method has capability to segment the MS lesions with Dice similarity coefficient (DSC score of 0.82.

  8. Segmenting Brain Tissues from Chinese Visible Human Dataset by Deep-Learned Features with Stacked Autoencoder

    Directory of Open Access Journals (Sweden)

    Guangjun Zhao

    2016-01-01

    Full Text Available Cryosection brain images in Chinese Visible Human (CVH dataset contain rich anatomical structure information of tissues because of its high resolution (e.g., 0.167 mm per pixel. Fast and accurate segmentation of these images into white matter, gray matter, and cerebrospinal fluid plays a critical role in analyzing and measuring the anatomical structures of human brain. However, most existing automated segmentation methods are designed for computed tomography or magnetic resonance imaging data, and they may not be applicable for cryosection images due to the imaging difference. In this paper, we propose a supervised learning-based CVH brain tissues segmentation method that uses stacked autoencoder (SAE to automatically learn the deep feature representations. Specifically, our model includes two successive parts where two three-layer SAEs take image patches as input to learn the complex anatomical feature representation, and then these features are sent to Softmax classifier for inferring the labels. Experimental results validated the effectiveness of our method and showed that it outperformed four other classical brain tissue detection strategies. Furthermore, we reconstructed three-dimensional surfaces of these tissues, which show their potential in exploring the high-resolution anatomical structures of human brain.

  9. A multiparametric and multiscale approach to automated segmentation of brain veins.

    Science.gov (United States)

    Monti, S; Palma, G; Borrelli, P; Tedeschi, E; Cocozza, S; Salvatore, M; Mancini, M

    2015-08-01

    Cerebral vein analysis provides a fundamental tool to study brain diseases such as neurodegenerative disorders or traumatic brain injuries. In order to assess the vascular anatomy, manual segmentation approaches can be used but are observer-dependent and time-consuming. In the present work, a fully automated cerebral vein segmentation method is proposed, based on a multiscale and multiparametric approach. The combined investigation of the R2(*)- and a Vesselness probability-map was used to obtain a fast and highly reliable classification of venous voxels. A semiquantitative analysis showed that our approach outperformed the previous state-of-the-art algorithm both in sensitivity and specificity. Inclusion of this tool within a parametric brain framework may therefore pave the way for a quantitative study of the intracranial venous system.

  10. Automated tissue segmentation of MR brain images in the presence of white matter lesions.

    Science.gov (United States)

    Valverde, Sergi; Oliver, Arnau; Roura, Eloy; González-Villà, Sandra; Pareto, Deborah; Vilanova, Joan C; Ramió-Torrentà, Lluís; Rovira, Àlex; Lladó, Xavier

    2017-01-01

    Over the last few years, the increasing interest in brain tissue volume measurements on clinical settings has led to the development of a wide number of automated tissue segmentation methods. However, white matter lesions are known to reduce the performance of automated tissue segmentation methods, which requires manual annotation of the lesions and refilling them before segmentation, which is tedious and time-consuming. Here, we propose a new, fully automated T1-w/FLAIR tissue segmentation approach designed to deal with images in the presence of WM lesions. This approach integrates a robust partial volume tissue segmentation with WM outlier rejection and filling, combining intensity and probabilistic and morphological prior maps. We evaluate the performance of this method on the MRBrainS13 tissue segmentation challenge database, which contains images with vascular WM lesions, and also on a set of Multiple Sclerosis (MS) patient images. On both databases, we validate the performance of our method with other state-of-the-art techniques. On the MRBrainS13 data, the presented approach was at the time of submission the best ranked unsupervised intensity model method of the challenge (7th position) and clearly outperformed the other unsupervised pipelines such as FAST and SPM12. On MS data, the differences in tissue segmentation between the images segmented with our method and the same images where manual expert annotations were used to refill lesions on T1-w images before segmentation were lower or similar to the best state-of-the-art pipeline incorporating automated lesion segmentation and filling. Our results show that the proposed pipeline achieved very competitive results on both vascular and MS lesions. A public version of this approach is available to download for the neuro-imaging community. Copyright © 2016 Elsevier B.V. All rights reserved.

  11. Improving Brain Magnetic Resonance Image (MRI) Segmentation via a Novel Algorithm based on Genetic and Regional Growth

    Science.gov (United States)

    A., Javadpour; A., Mohammadi

    2016-01-01

    Background Regarding the importance of right diagnosis in medical applications, various methods have been exploited for processing medical images solar. The method of segmentation is used to analyze anal to miscall structures in medical imaging. Objective This study describes a new method for brain Magnetic Resonance Image (MRI) segmentation via a novel algorithm based on genetic and regional growth. Methods Among medical imaging methods, brains MRI segmentation is important due to high contrast of non-intrusive soft tissue and high spatial resolution. Size variations of brain tissues are often accompanied by various diseases such as Alzheimer’s disease. As our knowledge about the relation between various brain diseases and deviation of brain anatomy increases, MRI segmentation is exploited as the first step in early diagnosis. In this paper, regional growth method and auto-mate selection of initial points by genetic algorithm is used to introduce a new method for MRI segmentation. Primary pixels and similarity criterion are automatically by genetic algorithms to maximize the accuracy and validity in image segmentation. Results By using genetic algorithms and defining the fixed function of image segmentation, the initial points for the algorithm were found. The proposed algorithms are applied to the images and results are manually selected by regional growth in which the initial points were compared. The results showed that the proposed algorithm could reduce segmentation error effectively. Conclusion The study concluded that the proposed algorithm could reduce segmentation error effectively and help us to diagnose brain diseases. PMID:27672629

  12. School liaison program for children with brain tumors.

    Science.gov (United States)

    Bruce, Beth S; Newcombe, Janice; Chapman, Ann

    2012-01-01

    A school liaison program that familiarized teachers with the implications of each child's brain tumor treatment with respect to learning, behavior, and socialization was implemented. The study explored the experiences of nine families and their teachers and health staff who participated in the program. The successes and challenges of the program were captured through interviews that were audio-taped and transcribed verbatim for data analysis. Individualized programs were negotiated between families and education staff to address behavioral, academic, and social needs of each child. Children were able to learn to their ability rather than be judged on the achievements of their respective grade levels. Parents reported that the program strengthened their advocacy skills and improved the children's social and learning achievements. Teachers reported an improved ability to provide more comprehensive educational programming suited to the child's needs. Overall, most children in the program achieved or exceeded their initial academic, social, and behavioral expectations. The school liaison program demonstrated significant potential to enhance the learning experience for children with brain tumors.

  13. Metastatic Brain Tumors: A Retrospective Review in East Azarbyjan (Tabriz

    Directory of Open Access Journals (Sweden)

    Zinat Miabi

    2011-02-01

    Full Text Available A set of one hundred and twenty nine patients with known primary malignancy and suspected brain metastasis was reviewed in present study. The patients were selected among patients presented to the MRI section of Imam Khomeini Hospital or a private MRI center in Tabriz (Iran. Primary tumor site, clinical manifestations, number and site of lesions were identified in this patient population. The primary tumor site was breast in 55 patients (42.6%, followed by lung (40.3%, kidney (7.7%, colorectal (4.6%, lymphoma (3.1% and melanoma (1.5%. Most patients were presented with features of increased intracranial pressure (headaches and vomiting, seizures and focal neurologic signs. Single brain metastasis occurred in 16.3% of patients, while multiple lesions accounted for 83.7% of patients. Ninety seven patients had supratentorial metastases (75.2%. Twenty cases (15.5% had metastases in both compartments. Infratentorial lesions were observed only in twelve patients (9.3%.

  14. Stereotactic interstitial brachytherapy for the treatment of oligodendroglial brain tumors

    Energy Technology Data Exchange (ETDEWEB)

    El Majdoub, Faycal; Neudorfer, Clemens; Maarouf, Mohammad [University Hospital of Cologne, Department of Stereotaxy and Functional Neurosurgery, Cologne (Germany); University of Witten/Herdecke, Department of Stereotaxy and Functional Neurosurgery, Center of Neurosurgery, Cologne-Merheim Medical Center (CMMC), Cologne (Germany); Blau, Tobias; Deckert, Martina [University Hospital of Cologne, Department of Neuropathology, Cologne (Germany); Hellmich, Martin [University Hospital of Cologne, Institute of Statistics, Informatics and Epidemiology, Cologne (Germany); Buehrle, Christian [University Hospital of Cologne, Department of Stereotaxy and Functional Neurosurgery, Cologne (Germany); Sturm, Volker [University Hospital of Cologne, Department of Stereotaxy and Functional Neurosurgery, Cologne (Germany); University Hospital of Wurzburg, Department of Neurosurgery, Wuerzburg (Germany)

    2015-12-15

    We evaluated the treatment of oligodendroglial brain tumors with interstitial brachytherapy (IBT) using {sup 125}iodine seeds ({sup 125}I) and analyzed prognostic factors. Between January 1991 and December 2010, 63 patients (median age 43.3 years, range 20.8-63.4 years) suffering from oligodendroglial brain tumors were treated with {sup 125}I IBT either as primary, adjuvantly after incomplete resection, or as salvage therapy after tumor recurrence. Possible prognostic factors influencing disease progression and survival were retrospectively investigated. The actuarial 2-, 5-, and 10-year overall and progression-free survival rates after IBT for WHO II tumors were 96.9, 96.9, 89.8 % and 96.9, 93.8, 47.3 %; for WHO III tumors 90.3, 77, 54.9 % and 80.6, 58.4, 45.9 %, respectively. Magnetic resonance imaging demonstrated complete remission in 2 patients, partial remission in 13 patients, stable disease in 17 patients and tumor progression in 31 patients. Median time to progression for WHO II tumors was 87.6 months and for WHO III tumors 27.8 months. Neurological status improved in 10 patients and remained stable in 20 patients, while 9 patients deteriorated. There was no treatment-related mortality. Treatment-related morbidity was transient in 11 patients. WHO II, KPS ≥ 90 %, frontal location, and tumor surface dose > 50 Gy were associated with increased overall survival (p ≤ 0.05). Oligodendroglioma and frontal location were associated with a prolonged progression-free survival (p ≤ 0.05). Our study indicates that IBT achieves local control rates comparable to surgery and radio-/chemotherapy treatment, is minimally invasive, and safe. Due to the low rate of side effects, IBT may represent an attractive option as part of a multimodal treatment schedule, being supplementary to microsurgery or as a salvage therapy after chemotherapy and conventional irradiation. (orig.) [German] Die Behandlung oligodendroglialer Hirntumoren durch die interstitielle Brachytherapie

  15. Brain-inspired speech segmentation for automatic speech recognition using the speech envelope as a temporal reference

    OpenAIRE

    Byeongwook Lee; Kwang-Hyun Cho

    2016-01-01

    Speech segmentation is a crucial step in automatic speech recognition because additional speech analyses are performed for each framed speech segment. Conventional segmentation techniques primarily segment speech using a fixed frame size for computational simplicity. However, this approach is insufficient for capturing the quasi-regular structure of speech, which causes substantial recognition failure in noisy environments. How does the brain handle quasi-regular structured speech and maintai...

  16. Gliomatosis cerebri: no evidence for a separate brain tumor entity.

    Science.gov (United States)

    Herrlinger, Ulrich; Jones, David T W; Glas, Martin; Hattingen, Elke; Gramatzki, Dorothee; Stuplich, Moritz; Felsberg, Jörg; Bähr, Oliver; Gielen, Gerrit H; Simon, Matthias; Wiewrodt, Dorothee; Schabet, Martin; Hovestadt, Volker; Capper, David; Steinbach, Joachim P; von Deimling, Andreas; Lichter, Peter; Pfister, Stefan M; Weller, Michael; Reifenberger, Guido

    2016-02-01

    Gliomatosis cerebri (GC) is presently considered a distinct astrocytic glioma entity according to the WHO classification for CNS tumors. It is characterized by widespread, typically bilateral infiltration of the brain involving three or more lobes. Genetic studies of GC have to date been restricted to the analysis of individual glioma-associated genes, which revealed mutations in the isocitrate dehydrogenase 1 (IDH1) and tumor protein p53 (TP53) genes in subsets of patients. Here, we report on a genome-wide analysis of DNA methylation and copy number aberrations in 25 GC patients. Results were compared with those obtained for 105 patients with various types of conventional, i.e., non-GC gliomas including diffuse astrocytic gliomas, oligodendrogliomas and glioblastomas. In addition, we assessed the prognostic role of methylation profiles and recurrent DNA copy number aberrations in GC patients. Our data reveal that the methylation profiles in 23 of the 25 GC tumors corresponded to either IDH mutant astrocytoma (n = 6), IDH mutant and 1p/19q codeleted oligodendroglioma (n = 5), or IDH wild-type glioblastoma including various molecular subgroups, i.e., H3F3A-G34 mutant (n = 1), receptor tyrosine kinase 1 (RTK1, n = 4), receptor tyrosine kinase 2 (classic) (RTK2, n = 2) or mesenchymal (n = 5) glioblastoma groups. Two tumors showed methylation profiles of normal brain tissue due to low tumor cell content. While histological grading (WHO grade IV vs. WHO grade II and III) was not prognostic, the molecular classification as classic/RTK2 or mesenchymal glioblastoma was associated with worse overall survival. Multivariate Cox regression analysis revealed MGMT promoter methylation as a positive prognostic factor. Taken together, DNA-based large-scale molecular profiling indicates that GC comprises a genetically and epigenetically heterogeneous group of diffuse gliomas that carry DNA methylation and copy number profiles closely matching the common molecularly

  17. Semisupervised learning using denoising autoencoders for brain lesion detection and segmentation.

    Science.gov (United States)

    Alex, Varghese; Vaidhya, Kiran; Thirunavukkarasu, Subramaniam; Kesavadas, Chandrasekharan; Krishnamurthi, Ganapathy

    2017-10-01

    The work explores the use of denoising autoencoders (DAEs) for brain lesion detection, segmentation, and false-positive reduction. Stacked denoising autoencoders (SDAEs) were pretrained using a large number of unlabeled patient volumes and fine-tuned with patches drawn from a limited number of patients ([Formula: see text], 40, 65). The results show negligible loss in performance even when SDAE was fine-tuned using 20 labeled patients. Low grade glioma (LGG) segmentation was achieved using a transfer learning approach in which a network pretrained with high grade glioma data was fine-tuned using LGG image patches. The networks were also shown to generalize well and provide good segmentation on unseen BraTS 2013 and BraTS 2015 test data. The manuscript also includes the use of a single layer DAE, referred to as novelty detector (ND). ND was trained to accurately reconstruct nonlesion patches. The reconstruction error maps of test data were used to localize lesions. The error maps were shown to assign unique error distributions to various constituents of the glioma, enabling localization. The ND learns the nonlesion brain accurately as it was also shown to provide good segmentation performance on ischemic brain lesions in images from a different database.

  18. Brain volumetric measures in alcoholics: a comparison of two segmentation methods

    Directory of Open Access Journals (Sweden)

    Marlene Oscar-Berman

    2011-02-01

    Full Text Available Marlene Oscar-Berman1–4, Janet Song5,61Department of Psychiatry, 2Department of Neurology, 3Department of Anatomy and Neurobiology, Boston University School of Medicine, Boston, MA, USA; 4VA Boston Healthcare System, Boston, MA, USA; 5Research Scientist Institute, Center for Excellence in Education, Massachusetts Institute of Technology, Cambridge, MA, USA; 6Harvard College, Cambridge, MA, USAAbstract: Measures of regional brain volumes, which can be derived from magnetic resonance imaging (MRI images by dividing a brain into its constituent parts, can be used as structural indicators of many different neuroanatomical diseases and disorders, including alcoholism. Reducing the time and cost required for brain segmentation would greatly facilitate both clinical and research endeavors. In the present study, we compared two segmentation methods to measure brain volumes in alcoholic and nonalcoholic control subjects: 1 an automated system (FreeSurfer and 2 a semi-automated, supervised system (Cardviews, developed by the Center for Morphometric Analysis [CMA] at Massachusetts General Hospital, which requires extensive staff and oversight. The participants included 32 abstinent alcoholics (19 women and 37 demographically matched, nonalcoholic controls (17 women. Brain scans were acquired in a 3 Tesla MRI scanner. The FreeSurfer and CMA methods showed good agreement for the lateral ventricles, cerebral white matter, caudate, and thalamus. In general, the larger the brain structure, the closer the agreement between the methods, except for the cerebral cortex, which showed large between-method differences. However, several other discrepancies existed between the FreeSurfer and CMA volume measures of alcoholics’ brains. The CMA volumes, but not FreeSurfer, demonstrated that the thalamus, caudate, and putamen were significantly smaller in male alcoholics as compared with male controls. Additionally, the hippocampus was significantly smaller in alcoholic

  19. 3D Segmentation of Rodent Brain Structures Using Hierarchical Shape Priors and Deformable Models

    Science.gov (United States)

    Zhang, Shaoting; Huang, Junzhou; Uzunbas, Mustafa; Shen, Tian; Delis, Foteini; Huang, Xiaolei; Volkow, Nora; Thanos, Panayotis; Metaxas, Dimitris N.

    2016-01-01

    In this paper, we propose a method to segment multiple rodent brain structures simultaneously. This method combines deformable models and hierarchical shape priors within one framework. The deformation module employs both gradient and appearance information to generate image forces to deform the shape. The shape prior module uses Principal Component Analysis to hierarchically model the multiple structures at both global and local levels. At the global level, the statistics of relative positions among different structures are modeled. At the local level, the shape statistics within each structure is learned from training samples. Our segmentation method adaptively employs both priors to constrain the intermediate deformation result. This prior constraint improves the robustness of the model and benefits the segmentation accuracy. Another merit of our prior module is that the size of the training data can be small, because the shape prior module models each structure individually and combines them using global statistics. This scheme can preserve shape details better than directly applying PCA on all structures. We use this method to segment rodent brain structures, such as the cerebellum, the left and right striatum, and the left and right hippocampus. The experiments show that our method works effectively and this hierarchical prior improves the segmentation performance. PMID:22003750

  20. Intraoperative brain tumor resection cavity characterization with conoscopic holography

    Science.gov (United States)

    Simpson, Amber L.; Burgner, Jessica; Chen, Ishita; Pheiffer, Thomas S.; Sun, Kay; Thompson, Reid C.; Webster, Robert J., III; Miga, Michael I.

    2012-02-01

    Brain shift compromises the accuracy of neurosurgical image-guided interventions if not corrected by either intraoperative imaging or computational modeling. The latter requires intraoperative sparse measurements for constraining and driving model-based compensation strategies. Conoscopic holography, an interferometric technique that measures the distance of a laser light illuminated surface point from a fixed laser source, was recently proposed for non-contact surface data acquisition in image-guided surgery and is used here for validation of our modeling strategies. In this contribution, we use this inexpensive, hand-held conoscopic holography device for intraoperative validation of our computational modeling approach to correcting for brain shift. Laser range scan, instrument swabbing, and conoscopic holography data sets were collected from two patients undergoing brain tumor resection therapy at Vanderbilt University Medical Center. The results of our study indicate that conoscopic holography is a promising method for surface acquisition since it requires no contact with delicate tissues and can characterize the extents of structures within confined spaces. We demonstrate that for two clinical cases, the acquired conoprobe points align with our model-updated images better than the uncorrected images lending further evidence that computational modeling approaches improve the accuracy of image-guided surgical interventions in the presence of soft tissue deformations.

  1. Comparative planning study for proton radiotherapy of benign brain tumors

    Energy Technology Data Exchange (ETDEWEB)

    Cozzi, L.; Nicolini, G.; Fogliata, A. [Medical Physics, Oncology Inst. of Southern Switzerland, Bellinzona (Switzerland); Clivio, A.; Vanetti, E. [Medical Physics, Oncology Inst. of Southern Switzerland, Bellinzona (Switzerland); Medical Physics Specialization School, Univ. of Milan (Italy)

    2006-07-15

    Purpose: a comparative study of different systems for proton-based radiotherapy was conducted. Material and methods: the Paul Scherrer Institute method for spot scanning was compared with the systems for passive scattering from the helax-TMS and the varian eclipse. Twelve cases of ''benign'' brain tumors were considered (meningiomas, neurinomas, and hypophyseal adenomas). Organs at risk included chiasm, brainstem, eyes and optic nerves as well as the not otherwise specified healthy brain tissue in view of long-term toxicity. Results: the results showed that high target coverage was achievable (V{sub 90} > 98% for all systems). Plans designed with the spot-scanning technique presented the minimum involvement of healthy tissue (e.g., the lowest maximum significant dose to healthy brain [25.6 Gy] or the lowest conformity index [CI{sub 95} = 1.3], between 38% and 46% lower than for the other techniques). Conclusion: in this study, no definitive indication of superiority of any technique can be drawn but spot scanning can better conform dose distributions and minimize the irradiation of healthy volumes at medium to low dose levels, a factor of interest when long life expectancy is considered. (orig.)

  2. Comparison of immune microenvironments between primary tumors and brain metastases in patients with breast cancer.

    Science.gov (United States)

    Ogiya, Rin; Niikura, Naoki; Kumaki, Nobue; Yasojima, Hiroyuki; Iwasa, Tsutomu; Kanbayashi, Chizuko; Oshitanai, Risa; Tsuneizumi, Michiko; Watanabe, Ken-Ichi; Matsui, Akira; Fujisawa, Tomomi; Saji, Shigehira; Masuda, Norikazu; Tokuda, Yutaka; Iwata, Hiroji

    2017-11-28

    Immune checkpoint inhibitors are reported to be effective in patients with brain metastases. However, detailed characteristics of the brain metastasis immune microenvironment remain unexplored. The median tumor-infiltrating lymphocyte (TIL) category in brain metastases was 5% (1-70%). In 46 pair-matched samples, the percentages of TILs were significantly higher in primary breast tumors than in brain metastases (paired t-test, P L1, PD-L2, and HLA class I was also performed. There are significantly fewer TILs in brain metastases than in primary breast tumors.

  3. MR spectroscopic evaluation of brain tissue damage after treatment for pediatric brain tumors.

    Science.gov (United States)

    Blamek, Sławomir; Larysz, Dawid; Ficek, Kornelia; Sokół, Maria; Miszczyk, Leszek; Tarnawski, Rafał

    2010-01-01

    The aim of this study was to evaluate the metabolic profile of uninvolved brain tissue after treatment for pediatric brain tumors. A group of 24 patients aged 4-18 years was analyzed after combined treatment for brain tumors. In this group, there were nine medulloblastomas, seven low-grade gliomas, three high-grade gliomas, two ependymomas and three children with conservatively treated diffuse brainstem gliomas. Short echo-time (TE = 30 ms) point-resolved spectra were acquired using a 2 T clinical scanner (Elscint Prestige). The ratios of signal intensities for N-acetylaspartate (NAA), choline (Cho), myo-inositol (mI), lactate (Lac), and lipids (Lip) were calculated using the creatine (Cr) signal as an internal reference. The spectra were acquired both from the tumor bed and from contralateral uninvolved brain tissue; only control spectra were analyzed. The first examination was made between the third and sixth month after therapy (24 spectra), the second examination occurred 8-12 months after treatment (15 spectra available), and the third was performed approximately 18 months after completion of therapy (eight spectra available). The results were compared using the t-test for dependent samples. At all time points, the metabolite ratios showed alterations indicating brain tissue damage. The most important were the decrease of NAA/Cr and increase of Lac/Cr and Lip/Cr ratios. The mean NAA/Cr values were 0.91, 0.91, and 0.86, respectively, for the three examinations, while the Lac/Cr and Lip/Cr values were 1.66, 2.11, 1.19 and 12.24, 12.05, 5.69, respectively. Interestingly, in children with supratentorial tumors, a significant increase in NAA/Cr value was observed (from 0.82 to 1.11 in the first and second examinations, respectively; p = 0.0487), which may be indicative of neuronal function recovery. MRS examinations of uninvolved brain tissue indicate long-lasting metabolic disturbances. However, the NAA/Cr ratio increase may be a sign of at least partial recovery

  4. Brain connectivity study of brain tumor patients using MR-PET data: preliminary results

    Energy Technology Data Exchange (ETDEWEB)

    Mendes, Ana Carina [Institute of Biophysics and Biomedical Engineering, Faculty of Sciences of the University of Lisbon (Portugal); Ribeiro, Andre Santos [Institute of Biophysics and Biomedical Engineering, Faculty of Sciences of the University of Lisbon (Portugal); Centre for Neuropsychopharmacology, Division of Brain Sciences, Department of Medicine, Imperial College London, London (United Kingdom); Oros-Peusquens, Ana Maria; Langen, Karl Josef; Shah, Jon [Institute of Neuroscience and Medicine - 4, Forschungszentrum Juelich (Germany); Ferreira, Hugo Alexandre [Institute of Biophysics and Biomedical Engineering, Faculty of Sciences of the University of Lisbon (Portugal)

    2015-05-18

    Brain activity results from anatomical and functional connections that can be disrupted or altered due to trauma or lesion. This work presents a first approach on the study of whole-brain connectivity of brain tumor patients using the Multimodal Imaging Brain Connectivity (MIBCA) toolbox. Two patients with glioblastoma lesions located in the left hemisphere (one in the motor cortex and the other in the temporal lobe) underwent simultaneous MRI and dynamic PET scans using a 3T MRI scanner with a BrainPET insert. The following data was acquired: T1-w MPRAGE (1x1x1mm{sup 3}), DTI (dir=30, b=0,800s/mm2, 2x2x2mm{sup 3}), and dynamic 18F-FET PET. The MIBCA toolbox was used to automatically pre-process MRI-PET data and to derive imaging and connectivity metrics from the multimodal data. Computed metrics included: cortical thickness from T1-w data; mean diffusivity (MD), fractional anisotropy (FA), node degree, clustering coefficient and pairwise ROI fibre tracking (structural connectivity) from DTI data; and standardized uptake value (SUV) from PET data. For all the metrics, the differences between left and right hemispherical structures were obtained, followed by a 25% threshold (except for SUV thresholded at 15%). Data was visualized in a connectogram, and both structural connectivity and metrics were studied in regions surrounding lesions. Preliminary results showed increased SUV values in regions surrounding the tumor for both patients. Patients also showed changes in structural connectivity involving these regions and also other more spatially distant regions such as the putamen and the pallidum, including decreased number of fibers between the subcortical structures themselves and with frontal regions. These findings suggest that the presence of a tumor may alter both local and more distant structural connections. Presently, a larger patient sample is being studied along with the inclusion of a control group to test the consistency of the findings.

  5. Re-irradiation for metastatic brain tumors with whole-brain radiotherapy

    International Nuclear Information System (INIS)

    Akiba, Takeshi; Kunieda, Etsuo; Kogawa, Asuka; Komatsu, Tetsuya; Tamai, Yoshifumi; Ohizumi, Yukio

    2012-01-01

    The objective of this study was to determine whether second whole-brain irradiation is beneficial for patients previously treated with whole-brain irradiation. A retrospective analysis was done for 31 patients with brain metastases who had undergone re-irradiation. Initial whole-brain irradiation was performed with 30 Gy/10 fractions for 87% of these patients. Whole-brain re-irradiation was performed with 30 Gy/10 fractions for 42% of these patients (3-40 Gy/1-20 fractions). Three patients underwent a third whole-brain irradiation. The median interval between the initial irradiation and re-irradiation was 10 months (range: 2-69 months). The median survival time after re-irradiation was 4 months (range: 1-21 months). The symptomatic improvement rate after re-irradiation was 68%, and the partial and complete tumor response rate was 55%. Fifty-two percent of the patients developed Grade 1 acute reactions. On magnetic resonance imaging, brain atrophy was observed in 36% of these patients after the initial irradiation and 74% after re-irradiation. Grade ≥2 encephalopathy or cognitive disturbance was observed in 10 patients (32%) after re-irradiation. Based on univariate analysis, significant factors related to survival after re-irradiation were the location of the primary cancer (P=0.003) and the Karnofsky performance status at the time of re-irradiation (P=0.008). A Karnofsky performance status ≥70 was significant based on multivariate analysis (P=0.050). Whole-brain re-irradiation for brain metastases placed only a slight burden on patients and was effective for symptomatic improvement. However, their remaining survival time was limited and the incidence of cognitive disturbance was rather high. (author)

  6. Deformable templates guided discriminative models for robust 3D brain MRI segmentation.

    Science.gov (United States)

    Liu, Cheng-Yi; Iglesias, Juan Eugenio; Tu, Zhuowen

    2013-10-01

    Automatically segmenting anatomical structures from 3D brain MRI images is an important task in neuroimaging. One major challenge is to design and learn effective image models accounting for the large variability in anatomy and data acquisition protocols. A deformable template is a type of generative model that attempts to explicitly match an input image with a template (atlas), and thus, they are robust against global intensity changes. On the other hand, discriminative models combine local image features to capture complex image patterns. In this paper, we propose a robust brain image segmentation algorithm that fuses together deformable templates and informative features. It takes advantage of the adaptation capability of the generative model and the classification power of the discriminative models. The proposed algorithm achieves both robustness and efficiency, and can be used to segment brain MRI images with large anatomical variations. We perform an extensive experimental study on four datasets of T1-weighted brain MRI data from different sources (1,082 MRI scans in total) and observe consistent improvement over the state-of-the-art systems.

  7. Multigrid Nonlocal Gaussian Mixture Model for Segmentation of Brain Tissues in Magnetic Resonance Images

    Directory of Open Access Journals (Sweden)

    Yunjie Chen

    2016-01-01

    Full Text Available We propose a novel segmentation method based on regional and nonlocal information to overcome the impact of image intensity inhomogeneities and noise in human brain magnetic resonance images. With the consideration of the spatial distribution of different tissues in brain images, our method does not need preestimation or precorrection procedures for intensity inhomogeneities and noise. A nonlocal information based Gaussian mixture model (NGMM is proposed to reduce the effect of noise. To reduce the effect of intensity inhomogeneity, the multigrid nonlocal Gaussian mixture model (MNGMM is proposed to segment brain MR images in each nonoverlapping multigrid generated by using a new multigrid generation method. Therefore the proposed model can simultaneously overcome the impact of noise and intensity inhomogeneity and automatically classify 2D and 3D MR data into tissues of white matter, gray matter, and cerebral spinal fluid. To maintain the statistical reliability and spatial continuity of the segmentation, a fusion strategy is adopted to integrate the clustering results from different grid. The experiments on synthetic and clinical brain MR images demonstrate the superior performance of the proposed model comparing with several state-of-the-art algorithms.

  8. Tumor cell survival dependence on helical tomotherapy, continuous arc and segmented dose delivery

    Energy Technology Data Exchange (ETDEWEB)

    Yang Wensha; Wang Li; Larner, James; Read, Paul; Benedict, Stan; Sheng Ke [Department of Radiation Oncology, University of Virginia, VA (United States)], E-mail: ks2mc@virginia.edu

    2009-11-07

    The temporal pattern of radiation delivery has been shown to influence the tumor cell survival fractions for the same radiation dose. To study the effect more specifically for state of the art rotational radiation delivery modalities, 2 Gy of radiation dose was delivered to H460 lung carcinoma, PC3 prostate cancer cells and MCF-7 breast tumor cells by helical tomotherapy (HT), seven-field LINAC (7F), and continuous dose delivery (CDD) over 2 min that simulates volumetric rotational arc therapy. Cell survival was measured by the clonogenic assay. The number of viable H460 cell colonies was 23.2 {+-} 14.4% and 27.7 {+-} 15.6% lower when irradiated by CDD compared with HT and 7F, respectively, and the corresponding values were 36.8 {+-} 18.9% and 35.3 {+-} 18.9% lower for MCF7 cells (p < 0.01). The survival of PC3 was also lower when irradiated by CDD than by HT or 7F but the difference was not as significant (p = 0.06 and 0.04, respectively). The higher survival fraction from HT delivery was unexpected because 90% of the 2 Gy was delivered in less than 1 min at a significantly higher dose rate than the other two delivery techniques. The results suggest that continuous dose delivery at a constant dose rate results in superior in vitro tumor cell killing compared with prolonged, segmented or variable dose rate delivery.

  9. Tumor cell survival dependence on helical tomotherapy, continuous arc and segmented dose delivery

    International Nuclear Information System (INIS)

    Yang Wensha; Wang Li; Larner, James; Read, Paul; Benedict, Stan; Sheng Ke

    2009-01-01

    The temporal pattern of radiation delivery has been shown to influence the tumor cell survival fractions for the same radiation dose. To study the effect more specifically for state of the art rotational radiation delivery modalities, 2 Gy of radiation dose was delivered to H460 lung carcinoma, PC3 prostate cancer cells and MCF-7 breast tumor cells by helical tomotherapy (HT), seven-field LINAC (7F), and continuous dose delivery (CDD) over 2 min that simulates volumetric rotational arc therapy. Cell survival was measured by the clonogenic assay. The number of viable H460 cell colonies was 23.2 ± 14.4% and 27.7 ± 15.6% lower when irradiated by CDD compared with HT and 7F, respectively, and the corresponding values were 36.8 ± 18.9% and 35.3 ± 18.9% lower for MCF7 cells (p < 0.01). The survival of PC3 was also lower when irradiated by CDD than by HT or 7F but the difference was not as significant (p = 0.06 and 0.04, respectively). The higher survival fraction from HT delivery was unexpected because 90% of the 2 Gy was delivered in less than 1 min at a significantly higher dose rate than the other two delivery techniques. The results suggest that continuous dose delivery at a constant dose rate results in superior in vitro tumor cell killing compared with prolonged, segmented or variable dose rate delivery.

  10. Determination of intra-axial brain tumors cellularity through the analysis of T2 Relaxation time of brain tumors before surgery using MATLAB software.

    Science.gov (United States)

    Abdolmohammadi, Jamil; Shafiee, Mohsen; Faeghi, Fariborz; Arefan, Douman; Zali, Alireza; Motiei-Langroudi, Rouzbeh; Farshidfar, Zahra; Nazarlou, Ali Kiani; Tavakkoli, Ali; Yarham, Mohammad

    2016-08-01

    Timely diagnosis of brain tumors could considerably affect the process of patient treatment. To do so, para-clinical methods, particularly MRI, cannot be ignored. MRI has so far answered significant questions regarding tumor characteristics, as well as helping neurosurgeons. In order to detect the tumor cellularity, neuro-surgeons currently have to sample specimens by biopsy and then send them to the pathology unit. The aim of this study is to determine the tumor cellularity in the brain. In this cross-sectional study, 32 patients (18 males and 14 females from 18-77 y/o) were admitted to the neurosurgery department of Shohada-E Tajrish Hospital in Tehran, Iran from April 2012 to February 2014. In addition to routine pulse sequences, T2W Multi echo pulse sequences were taken and the images were analyzed using the MATLAB software to determine the brain tumor cellularity, compared with the biopsy. These findings illustrate the need for more T2 relaxation time decreases, the higher classes of tumors will stand out in the designed table. In this study, the results show T2 relaxation time with a 85% diagnostic weight, compared with the biopsy, to determine the brain tumor cellularity (p<0.05). Our results indicate that the T2 relaxation time feature is the best method to distinguish and present the degree of intra-axial brain tumors cellularity (85% accuracy compared to biopsy). The use of more data is recommended in order to increase the percent accuracy of this techniques.

  11. Selective targeting of brain tumors with gold nanoparticle-induced radiosensitization.

    Directory of Open Access Journals (Sweden)

    Daniel Y Joh

    Full Text Available Successful treatment of brain tumors such as glioblastoma multiforme (GBM is limited in large part by the cumulative dose of Radiation Therapy (RT that can be safely given and the blood-brain barrier (BBB, which limits the delivery of systemic anticancer agents into tumor tissue. Consequently, the overall prognosis remains grim. Herein, we report our pilot studies in cell culture experiments and in an animal model of GBM in which RT is complemented by PEGylated-gold nanoparticles (GNPs. GNPs significantly increased cellular DNA damage inflicted by ionizing radiation in human GBM-derived cell lines and resulted in reduced clonogenic survival (with dose-enhancement ratio of ~1.3. Intriguingly, combined GNP and RT also resulted in markedly increased DNA damage to brain blood vessels. Follow-up in vitro experiments confirmed that the combination of GNP and RT resulted in considerably increased DNA damage in brain-derived endothelial cells. Finally, the combination of GNP and RT increased survival of mice with orthotopic GBM tumors. Prior treatment of mice with brain tumors resulted in increased extravasation and in-tumor deposition of GNP, suggesting that RT-induced BBB disruption can be leveraged to improve the tumor-tissue targeting of GNP and thus further optimize the radiosensitization of brain tumors by GNP. These exciting results together suggest that GNP may be usefully integrated into the RT treatment of brain tumors, with potential benefits resulting from increased tumor cell radiosensitization to preferential targeting of tumor-associated vasculature.

  12. Deep models for brain EM image segmentation: novel insights and improved performance.

    Science.gov (United States)

    Fakhry, Ahmed; Peng, Hanchuan; Ji, Shuiwang

    2016-08-01

    Accurate segmentation of brain electron microscopy (EM) images is a critical step in dense circuit reconstruction. Although deep neural networks (DNNs) have been widely used in a number of applications in computer vision, most of these models that proved to be effective on image classification tasks cannot be applied directly to EM image segmentation, due to the different objectives of these tasks. As a result, it is desirable to develop an optimized architecture that uses the full power of DNNs and tailored specifically for EM image segmentation. In this work, we proposed a novel design of DNNs for this task. We trained a pixel classifier that operates on raw pixel intensities with no preprocessing to generate probability values for each pixel being a membrane or not. Although the use of neural networks in image segmentation is not completely new, we developed novel insights and model architectures that allow us to achieve superior performance on EM image segmentation tasks. Our submission based on these insights to the 2D EM Image Segmentation Challenge achieved the best performance consistently across all the three evaluation metrics. This challenge is still ongoing and the results in this paper are as of June 5, 2015. https://github.com/ahmed-fakhry/dive : sji@eecs.wsu.edu. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

  13. Using Ferumoxytol-Enhanced MRI to Measure Inflammation in Patients With Brain Tumors or Other Conditions of the CNS

    Science.gov (United States)

    2017-08-30

    Brain Injury; Central Nervous System Degenerative Disorder; Central Nervous System Infectious Disorder; Central Nervous System Vascular Malformation; Hemorrhagic Cerebrovascular Accident; Ischemic Cerebrovascular Accident; Primary Brain Neoplasm; Brain Cancer; Brain Tumors

  14. Random Walk and Graph Cut for Co-Segmentation of Lung Tumor on PET-CT Images.

    Science.gov (United States)

    Ju, Wei; Xiang, Dehui; Xiang, Deihui; Zhang, Bin; Wang, Lirong; Kopriva, Ivica; Chen, Xinjian

    2015-12-01

    Accurate lung tumor delineation plays an important role in radiotherapy treatment planning. Since the lung tumor has poor boundary in positron emission tomography (PET) images and low contrast in computed tomography (CT) images, segmentation of tumor in the PET and CT images is a challenging task. In this paper, we effectively integrate the two modalities by making fully use of the superior contrast of PET images and superior spatial resolution of CT images. Random walk and graph cut method is integrated to solve the segmentation problem, in which random walk is utilized as an initialization tool to provide object seeds for graph cut segmentation on the PET and CT images. The co-segmentation problem is formulated as an energy minimization problem which is solved by max-flow/min-cut method. A graph, including two sub-graphs and a special link, is constructed, in which one sub-graph is for the PET and another is for CT, and the special link encodes a context term which penalizes the difference of the tumor segmentation on the two modalities. To fully utilize the characteristics of PET and CT images, a novel energy representation is devised. For the PET, a downhill cost and a 3D derivative cost are proposed. For the CT, a shape penalty cost is integrated into the energy function which helps to constrain the tumor region during the segmentation. We validate our algorithm on a data set which consists of 18 PET-CT images. The experimental results indicate that the proposed method is superior to the graph cut method solely using the PET or CT is more accurate compared with the random walk method, random walk co-segmentation method, and non-improved graph cut method.

  15. Radiotherapy, especially at young age, increases the risk for de novo brain tumors in patients treated for pituitary tumors

    NARCIS (Netherlands)

    Burman, Pia; Van Beek, André P.; Biller, Beverly M.K.; Camacho-Hubner, Cecilia; Mattsson, Anders F.

    Background: Excess mortality due to de novo malignant brain tumors was recently found in a national study of patients with hypopituitarism following treatment of pituitary tumors. Here, we examined a larger multi-national cohort to corroborate and extend this observation. Objective: To investigate

  16. Brain Magnetic Resonance Imaging After High-Dose Chemotherapy and Radiotherapy for Childhood Brain Tumors

    International Nuclear Information System (INIS)

    Spreafico, Filippo; Gandola, Lorenza; Marchiano, Alfonso; Simonetti, Fabio; Poggi, Geraldina; Adduci, Anna; Clerici, Carlo Alfredo; Luksch, Roberto; Biassoni, Veronica; Meazza, Cristina; Catania, Serena; Terenziani, Monica; Musumeci, Renato; Fossati-Bellani, Franca; Massimino, Maura

    2008-01-01

    Purpose: Brain necrosis or other subacute iatrogenic reactions has been recognized as a potential complication of radiotherapy (RT), although the possible synergistic effects of high-dose chemotherapy and RT might have been underestimated. Methods and Materials: We reviewed the clinical and radiologic data of 49 consecutive children with malignant brain tumors treated with high-dose thiotepa and autologous hematopoietic stem cell rescue, preceded or followed by RT. The patients were assessed for neurocognitive tests to identify any correlation with magnetic resonance imaging (MRI) anomalies. Results: Of the 49 children, 18 (6 of 25 with high-grade gliomas and 12 of 24 with primitive neuroectodermal tumors) had abnormal brain MRI findings occurring a median of 8 months (range, 2-39 months) after RT and beginning to regress a median of 13 months (range, 2-26 months) after onset. The most common lesion pattern involved multiple pseudonodular, millimeter-size, T 1 -weighted unevenly enhancing, and T 2 -weighted hyperintense foci. Four patients with primitive neuroectodermal tumors also had subdural fluid leaks, with meningeal enhancement over the effusion. One-half of the patients had symptoms relating to the new radiographic findings. The MRI lesion-free survival rate was 74% ± 6% at 1 year and 57% ± 8% at 2 years. The number of marrow ablative courses correlated significantly to the incidence of radiographic anomalies. No significant difference was found in intelligent quotient scores between children with and without radiographic changes. Conclusion: Multiple enhancing cerebral lesions were frequently seen on MRI scans soon after high-dose chemotherapy and RT. Such findings pose a major diagnostic challenge in terms of their differential diagnosis vis-a-vis recurrent tumor. Their correlation with neurocognitive results deserves further investigation

  17. Improving efficacy of metastatic tumor segmentation to facilitate early prediction of ovarian cancer patients' response to chemotherapy

    Science.gov (United States)

    Danala, Gopichandh; Wang, Yunzhi; Thai, Theresa; Gunderson, Camille C.; Moxley, Katherine M.; Moore, Kathleen; Mannel, Robert S.; Cheng, Samuel; Liu, Hong; Zheng, Bin; Qiu, Yuchen

    2017-02-01

    Accurate tumor segmentation is a critical step in the development of the computer-aided detection (CAD) based quantitative image analysis scheme for early stage prognostic evaluation of ovarian cancer patients. The purpose of this investigation is to assess the efficacy of several different methods to segment the metastatic tumors occurred in different organs of ovarian cancer patients. In this study, we developed a segmentation scheme consisting of eight different algorithms, which can be divided into three groups: 1) Region growth based methods; 2) Canny operator based methods; and 3) Partial differential equation (PDE) based methods. A number of 138 tumors acquired from 30 ovarian cancer patients were used to test the performance of these eight segmentation algorithms. The results demonstrate each of the tested tumors can be successfully segmented by at least one of the eight algorithms without the manual boundary correction. Furthermore, modified region growth, classical Canny detector, and fast marching, and threshold level set algorithms are suggested in the future development of the ovarian cancer related CAD schemes. This study may provide meaningful reference for developing novel quantitative image feature analysis scheme to more accurately predict the response of ovarian cancer patients to the chemotherapy at early stage.

  18. Games of lives in surviving childhood brain tumors.

    Science.gov (United States)

    Chen, Chin-Mi; Chen, Yueh-Chih; Haase, Joan E

    2008-06-01

    The purpose of this phenomenological study was to describe the commonality of the lived experience of adolescent and young adult survivors (AYAS) of brain tumors in Taiwan from a sociocultural perspective. Seven AYAS aged 13 to 22 years, who had survived 5 to 10 years from the time of diagnosis, participated in this study. In consideration of their emotional duress, each participant was interviewed only once. The data revealed an essential structure: the game of life. The essential structure included six themes as follows: (a) no longer playing well, (b) wandering on the outer edges of social life, (c) helplessly struggling with role obligations, (d) rationally regulating the meaning of surviving, (e) winning a new social face, and (f) mastering the game of life. The findings suggest how nurses might help AYAS to succeed in psychosocial adjustment.

  19. Optimization of brain tumor dose using intensity modulated radiation therapy

    International Nuclear Information System (INIS)

    Elbakery, Andaria Elhanfi Elmaki

    2016-04-01

    Intensity Modulated Radiation Therapy (IMRT) become an essential technique that achieves the goal of radiation therapy, maximum target dose and minimum dose for healthy tissues. Dose optimization was applied for brain tumor through as set of constrains given to the software. Fifteen patients were selected for IMRT planning and delineation was done using special software (fical). All data was tranferred to the treatment planning system. Kon Rad planning system was used in this work. The planning was evaluated with homogeneity index and dose volume histogram ( DVH). The 0ptimization was achieved from converge of target volume with 5% as maxim dose and 95% as the minimum dose. The homogeneity index that calculated for most of patients was approcimately equal to 1. It means that converge was good and the optimization fulfilled. For organs at risk (OAR) the dose was below the tolerances and the mean dose and maxim dose were calculated. (Author)

  20. Segmentation-free direct tumor volume and metabolic activity estimation from PET scans.

    Science.gov (United States)

    Taghanaki, Saeid Asgari; Duggan, Noirin; Ma, Hillgan; Hou, Xinchi; Celler, Anna; Benard, Francois; Hamarneh, Ghassan

    2018-01-01

    Tumor volume and metabolic activity are two robust imaging biomarkers for predicting early therapy response in F-fluorodeoxyglucose (FDG) positron emission tomography (PET), which is a modality to image the distribution of radiotracers and thereby observe functional processes in the body. To date, estimation of these two biomarkers requires a lesion segmentation step. While the segmentation methods requiring extensive user interaction have obvious limitations in terms of time and reproducibility, automatically estimating activity from segmentation, which involves integrating intensity values over the volume is also suboptimal, since PET is an inherently noisy modality. Although many semi-automatic segmentation based methods have been developed, in this paper, we introduce a method which completely eliminates the segmentation step and directly estimates the volume and activity of the lesions. We trained two parallel ensemble models using locally extracted 3D patches from phantom images to estimate the activity and volume, which are derivatives of other important quantification metrics such as standardized uptake value (SUV) and total lesion glycolysis (TLG). For validation, we used 54 clinical images from the QIN Head and Neck collection on The Cancer Imaging Archive, as well as a set of 55 PET scans of the Elliptical Lung-Spine Body Phantom™with different levels of noise, four different reconstruction methods, and three different background activities, namely; air, water, and hot background. In the validation on phantom images, we achieved relative absolute error (RAE) of 5.11 % ±3.5% and 5.7 % ±5.25% for volume and activity estimation, respectively, which represents improvements of over 20% and 6% respectively, compared with the best competing methods. From the validation performed using clinical images, we found that the proposed method is capable of obtaining almost the same level of agreement with a group of trained experts, as a single trained

  1. Learning-based 3T brain MRI segmentation with guidance from 7T MRI labeling.

    Science.gov (United States)

    Deng, Minghui; Yu, Renping; Wang, Li; Shi, Feng; Yap, Pew-Thian; Shen, Dinggang

    2016-12-01

    Segmentation of brain magnetic resonance (MR) images into white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) is crucial for brain structural measurement and disease diagnosis. Learning-based segmentation methods depend largely on the availability of good training ground truth. However, the commonly used 3T MR images are of insufficient image quality and often exhibit poor intensity contrast between WM, GM, and CSF. Therefore, they are not ideal for providing good ground truth label data for training learning-based methods. Recent advances in ultrahigh field 7T imaging make it possible to acquire images with excellent intensity contrast and signal-to-noise ratio. In this paper, the authors propose an algorithm based on random forest for segmenting 3T MR images by training a series of classifiers based on reliable labels obtained semiautomatically from 7T MR images. The proposed algorithm iteratively refines the probability maps of WM, GM, and CSF via a cascade of random forest classifiers for improved tissue segmentation. The proposed method was validated on two datasets, i.e., 10 subjects collected at their institution and 797 3T MR images from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. Specifically, for the mean Dice ratio of all 10 subjects, the proposed method achieved 94.52% ± 0.9%, 89.49% ± 1.83%, and 79.97% ± 4.32% for WM, GM, and CSF, respectively, which are significantly better than the state-of-the-art methods (p-values brain MR image segmentation. © 2016 American Association of Physicists in Medicine.

  2. Perfusion magnetic resonance imaging in pediatric brain tumors

    Energy Technology Data Exchange (ETDEWEB)

    Dallery, F.; Michel, D.; Constans, J.M.; Gondry-Jouet, C. [University Hospital, Department of Radiology, Amiens (France); Bouzerar, R.; Promelle, V.; Baledent, O. [University Hospital, Department of Imaging and Biophysics, Amiens (France); Attencourt, C. [University Hospital, Departement of Pathology, Amiens (France); Peltier, J. [University Hospital, Departement of Neurosurgery, Amiens (France)

    2017-11-15

    The use of DSC-MR imaging in pediatric neuroradiology is gradually growing. However, the number of studies listed in the literature remains limited. We propose to assess the perfusion and permeability parameters in pediatric brain tumor grading. Thirty children with a brain tumor having benefited from a DSC-MR perfusion sequence have been retrospectively explored. Relative CBF and CBV were computed on the ROI with the largest lesion coverage. Assessment of the lesion's permeability was also performed through the semi-quantitative PSR parameter and the K2 model-based parameter on the whole-lesion ROI and a reduced ROI drawn on the permeability maps. A statistical comparison of high- and low-grade groups (HG, LG) as well as a ROC analysis was performed on the histogram-based parameters. Our results showed a statistically significant difference between LG and HG groups for mean rCBV (p < 10{sup -3}), rCBF (p < 10{sup -3}), and for PSR (p = 0.03) but not for the K2 factor (p = 0.5). However, the ratio K2/PSR was shown to be a strong discriminating factor between the two groups of lesions (p < 10{sup -3}). For rCBV and rCBF indicators, high values of ROC AUC were obtained (> 0.9) and mean value thresholds were observed at 1.07 and 1.03, respectively. For K2/PSR in the reduced area, AUC was also superior to 0.9. The implementation of a dynamic T2* perfusion sequence provided reliable results using an objective whole-lesion ROI. Perfusion parameters as well as a new permeability indicator could efficiently discriminate high-grade from low-grade lesions in the pediatric population. (orig.)

  3. Subject-Specific Sparse Dictionary Learning for Atlas-Based Brain MRI Segmentation.

    Science.gov (United States)

    Roy, Snehashis; He, Qing; Sweeney, Elizabeth; Carass, Aaron; Reich, Daniel S; Prince, Jerry L; Pham, Dzung L

    2015-09-01

    Quantitative measurements from segmentations of human brain magnetic resonance (MR) images provide important biomarkers for normal aging and disease progression. In this paper, we propose a patch-based tissue classification method from MR images that uses a sparse dictionary learning approach and atlas priors. Training data for the method consists of an atlas MR image, prior information maps depicting where different tissues are expected to be located, and a hard segmentation. Unlike most atlas-based classification methods that require deformable registration of the atlas priors to the subject, only affine registration is required between the subject and training atlas. A subject-specific patch dictionary is created by learning relevant patches from the atlas. Then the subject patches are modeled as sparse combinations of learned atlas patches leading to tissue memberships at each voxel. The combination of prior information in an example-based framework enables us to distinguish tissues having similar intensities but different spatial locations. We demonstrate the efficacy of the approach on the application of whole-brain tissue segmentation in subjects with healthy anatomy and normal pressure hydrocephalus, as well as lesion segmentation in multiple sclerosis patients. For each application, quantitative comparisons are made against publicly available state-of-the art approaches.

  4. Overcoming the blood-brain tumor barrier for effective glioblastoma treatment

    NARCIS (Netherlands)

    Tellingen, O. van; Yetkin-Arik, B.; Gooijer, M.C. de; Wesseling, P.; Wurdinger, T.; Vries, H.E. de

    2015-01-01

    Gliomas are the most common primary brain tumors. Particularly in adult patients, the vast majority of gliomas belongs to the heterogeneous group of diffuse gliomas, i.e. glial tumors characterized by diffuse infiltrative growth in the preexistent brain tissue. Unfortunately, glioblastoma, the most

  5. Automatic Brain Tumor Detection in T2-weighted Magnetic Resonance Images

    Czech Academy of Sciences Publication Activity Database

    Dvořák, Pavel; Kropatsch, W.G.; Bartušek, Karel

    2013-01-01

    Roč. 13, č. 5 (2013), s. 223-230 ISSN 1335-8871 R&D Projects: GA ČR GAP102/12/1104; GA MŠk ED0017/01/01 Institutional support: RVO:68081731 Keywords : Brain tumor * Brain tumor detection * Symmetry analysis Subject RIV: JA - Electronics ; Optoelectronics, Electrical Engineering Impact factor: 1.162, year: 2013

  6. Brain tumors in children and adolescents and exposure to animals and farm life

    DEFF Research Database (Denmark)

    Christensen, Jeppe Schultz; Mortensen, Laust Hvas; Röösli, Martin

    2012-01-01

    The etiology of brain tumors in children and adolescents is largely unknown, and very few environmental risk factors have been identified. The aim of this study was to examine the relationship between pre- or postnatal animal contacts or farm exposures and the risk of childhood brain tumors (CBTs...

  7. Caring for patients with brain tumor: The patient and care giver ...

    African Journals Online (AJOL)

    Background: Patients with brain tumors form a heterogeneous group in terms of clinical presentation and pathology. However, the impact of the disease on patients' families is often more homogenous and frequently quite profound. A considerable body of literature is available on the management of brain tumors and ...

  8. Cognitive deficits in long-term survivors of childhood brain tumors: Identification of predictive factors

    DEFF Research Database (Denmark)

    Reimers, Tonny Solveig; Ehrenfels, Susanne; Mortensen, Erik Lykke

    2003-01-01

    To describe cognitive function and to evaluate the association between potentially predictive factors and cognitive outcome in an unselected population of survivors of childhood brain tumors.......To describe cognitive function and to evaluate the association between potentially predictive factors and cognitive outcome in an unselected population of survivors of childhood brain tumors....

  9. Validation tools for image segmentation

    Science.gov (United States)

    Padfield, Dirk; Ross, James

    2009-02-01

    A large variety of image analysis tasks require the segmentation of various regions in an image. For example, segmentation is required to generate accurate models of brain pathology that are important components of modern diagnosis and therapy. While the manual delineation of such structures gives accurate information, the automatic segmentation of regions such as the brain and tumors from such images greatly enhances the speed and repeatability of quantifying such structures. The ubiquitous need for such algorithms has lead to a wide range of image segmentation algorithms with various assumptions, parameters, and robustness. The evaluation of such algorithms is an important step in determining their effectiveness. Therefore, rather than developing new segmentation algorithms, we here describe validation methods for segmentation algorithms. Using similarity metrics comparing the automatic to manual segmentations, we demonstrate methods for optimizing the parameter settings for individual cases and across a collection of datasets using the Design of Experiment framework. We then employ statistical analysis methods to compare the effectiveness of various algorithms. We investigate several region-growing algorithms from the Insight Toolkit and compare their accuracy to that of a separate statistical segmentation algorithm. The segmentation algorithms are used with their optimized parameters to automatically segment the brain and tumor regions in MRI images of 10 patients. The validation tools indicate that none of the ITK algorithms studied are able to outperform with statistical significance the statistical segmentation algorithm although they perform reasonably well considering their simplicity.

  10. Distal Femur Allograft Prosthetic Composite Reconstruction for Short Proximal Femur Segments following Tumor Resection

    Directory of Open Access Journals (Sweden)

    Bryan S. Moon

    2013-01-01

    Full Text Available Short metaphyseal segments remaining after distal femoral tumor resection pose a unique challenge. Limb sparing options include a short stemmed modular prosthesis, total endoprosthetic replacement, cross-pin fixation to a custom implant, and allograft prosthetic composite reconstruction (APC. A series of patients with APC reconstruction were evaluated to determine functional and radiologic outcome and complication rates. Twelve patients were retrospectively identified who had a distal femoral APC reconstruction between 1994 and 2007 to salvage an extremity with a segment of remaining bone that was less than 20 centimeters in length. Seventeen APC reconstructions were performed in twelve patients. Eight were primary procedures and nine were revision procedures. Average f/u was 89 months. Twelve APC reconstructions (71% united and five (29% were persistent nonunions. At most recent followup 10 patients (83% had a healed APC which allowed WBAT. One pt (8% had an amputation and one pt (8% died prior to union. Average time to union was 19 months. Four pts (33% or five APC reconstructions (29% required further surgery to obtain a united reconstruction. Although Distal Femoral APC reconstruction has a high complication rate, a stable reconstruction was obtained in 83% of patients.

  11. Brain tissue segmentation using q-entropy in multiple sclerosis magnetic resonance images

    Energy Technology Data Exchange (ETDEWEB)

    Diniz, P.R.B.; Brum, D.G. [Universidade de Sao Paulo (USP), Ribeirao Preto, SP (Brazil). Faculdade de Medicina. Dept. de Neurociencias e Ciencias do Comportamento; Santos, A. C. [Universidade de Sao Paulo (USP), Ribeirao Preto, SP (Brazil). Faculdade de Medicina. Dept. de Clinica Medica; Murta-Junior, L.O.; Araujo, D.B. de, E-mail: murta@usp.b [Universidade de Sao Paulo (USP), Ribeirao Preto, SP (Brazil). Faculdade de Filosofia, Ciencias e Letras. Dept. de Fisica e Matematica

    2010-01-15

    The loss of brain volume has been used as a marker of tissue destruction and can be used as an index of the progression of neurodegenerative diseases, such as multiple sclerosis. In the present study, we tested a new method for tissue segmentation based on pixel intensity threshold using generalized Tsallis entropy to determine a statistical segmentation parameter for each single class of brain tissue. We compared the performance of this method using a range of different q parameters and found a different optimal q parameter for white matter, gray matter, and cerebrospinal fluid. Our results support the conclusion that the differences in structural correlations and scale invariant similarities present in each tissue class can be accessed by generalized Tsallis entropy, obtaining the intensity limits for these tissue class separations. In order to test this method, we used it for analysis of brain magnetic resonance images of 43 patients and 10 healthy controls matched for gender and age. The values found for the entropic q index were 0.2 for cerebrospinal fluid, 0.1 for white matter and 1.5 for gray matter. With this algorithm, we could detect an annual loss of 0.98% for the patients, in agreement with literature data. Thus, we can conclude that the entropy of Tsallis adds advantages to the process of automatic target segmentation of tissue classes, which had not been demonstrated previously. (author)

  12. Brain tissue segmentation using q-entropy in multiple sclerosis magnetic resonance images

    Directory of Open Access Journals (Sweden)

    P.R.B. Diniz

    2010-01-01

    Full Text Available The loss of brain volume has been used as a marker of tissue destruction and can be used as an index of the progression of neurodegenerative diseases, such as multiple sclerosis. In the present study, we tested a new method for tissue segmentation based on pixel intensity threshold using generalized Tsallis entropy to determine a statistical segmentation parameter for each single class of brain tissue. We compared the performance of this method using a range of different q parameters and found a different optimal q parameter for white matter, gray matter, and cerebrospinal fluid. Our results support the conclusion that the differences in structural correlations and scale invariant similarities present in each tissue class can be accessed by generalized Tsallis entropy, obtaining the intensity limits for these tissue class separations. In order to test this method, we used it for analysis of brain magnetic resonance images of 43 patients and 10 healthy controls matched for gender and age. The values found for the entropic q index were 0.2 for cerebrospinal fluid, 0.1 for white matter and 1.5 for gray matter. With this algorithm, we could detect an annual loss of 0.98% for the patients, in agreement with literature data. Thus, we can conclude that the entropy of Tsallis adds advantages to the process of automatic target segmentation of tissue classes, which had not been demonstrated previously.

  13. Brain MR image segmentation based on an improved active contour model.

    Directory of Open Access Journals (Sweden)

    Xiangrui Meng

    Full Text Available It is often a difficult task to accurately segment brain magnetic resonance (MR images with intensity in-homogeneity and noise. This paper introduces a novel level set method for simultaneous brain MR image segmentation and intensity inhomogeneity correction. To reduce the effect of noise, novel anisotropic spatial information, which can preserve more details of edges and corners, is proposed by incorporating the inner relationships among the neighbor pixels. Then the proposed energy function uses the multivariate Student's t-distribution to fit the distribution of the intensities of each tissue. Furthermore, the proposed model utilizes Hidden Markov random fields to model the spatial correlation between neigh-boring pixels/voxels. The means of the multivariate Student's t-distribution can be adaptively estimated by multiplying a bias field to reduce the effect of intensity inhomogeneity. In the end, we reconstructed the energy function to be convex and calculated it by using the Split Bregman method, which allows our framework for random initialization, thereby allowing fully automated applications. Our method can obtain the final result in less than 1 second for 2D image with size 256 × 256 and less than 300 seconds for 3D image with size 256 × 256 × 171. The proposed method was compared to other state-of-the-art segmentation methods using both synthetic and clinical brain MR images and increased the accuracies of the results more than 3%.

  14. Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation.

    Science.gov (United States)

    Kamnitsas, Konstantinos; Ledig, Christian; Newcombe, Virginia F J; Simpson, Joanna P; Kane, Andrew D; Menon, David K; Rueckert, Daniel; Glocker, Ben

    2017-02-01

    We propose a dual pathway, 11-layers deep, three-dimensional Convolutional Neural Network for the challenging task of brain lesion segmentation. The devised architecture is the result of an in-depth analysis of the limitations of current networks proposed for similar applications. To overcome the computational burden of processing 3D medical scans, we have devised an efficient and effective dense training scheme which joins the processing of adjacent image patches into one pass through the network while automatically adapting to the inherent class imbalance present in the data. Further, we analyze the development of deeper, thus more discriminative 3D CNNs. In order to incorporate both local and larger contextual information, we employ a dual pathway architecture that processes the input images at multiple scales simultaneously. For post-processing of the network's soft segmentation, we use a 3D fully connected Conditional Random Field which effectively removes false positives. Our pipeline is extensively evaluated on three challenging tasks of lesion segmentation in multi-channel MRI patient data with traumatic brain injuries, brain tumours, and ischemic stroke. We improve on the state-of-the-art for all three applications, with top ranking performance on the public benchmarks BRATS 2015 and ISLES 2015. Our method is computationally efficient, which allows its adoption in a variety of research and clinical settings. The source code of our implementation is made publicly available. Copyright © 2016 The Authors. Published by Elsevier B.V. All rights reserved.

  15. Study of Inter- and Intra-fraction Motion in Brain Tumor Patients Undergoing VMAT Treatment

    International Nuclear Information System (INIS)

    Ascencion Ybarra, Y.; Alfonso Laguardia, R.; Yartsev, S.

    2015-01-01

    Conforming dose to the tumor and sparing normal tissue can be challenging for brain tumors with complex shapes in close proximity to critical structures. The goal of this study was to evaluate the inter- and intra-fraction motion in brain tumor patients undergoing volumetric modulated arc therapy (VMAT). The image matching software was found to be very sensitive to the choice of the region of matching. It is recommended to use the same region of interest for comparing the image sets and perform the automatic matching based on bony landmarks in brain tumor cases. (Author)

  16. (18)F-Fluorodeoxyglucose PET/Computed Tomography for Primary Brain Tumors

    DEFF Research Database (Denmark)

    Antonsen Segtnan, Eivind; Hess, Søren; Grupe, Peter

    2015-01-01

    Structural imaging with computed tomography (CT) and MR imaging is the mainstay in primary diagnosis of primary brain tumors, but these modalities depend on morphologic appearance and an intact blood-brain barrier, and important aspects of tumor biology are not addressed. Such issues may...... describes some of the potential contemporary applications of FDG and PET in primary brain tumors....... be alleviated by (18)F-fluorodeoxyglucose (FDG)-PET and FDG-PET/CT imaging, which may provide clinically important information with regard to primary differentiation between tumor types, initial staging and risk stratification, therapy planning, response evaluation, and recurrence detection. This article...

  17. The use of cannabidiol for seizure management in patients with brain tumor-related epilepsy.

    Science.gov (United States)

    Warren, Paula Province; Bebin, E Martina; Nabors, L Burt; Szaflarski, Jerzy P

    Epilepsy, commonly encountered by patients with brain tumors, is often refractory to standard therapies. Our aim was to examine the safety and efficacy of pharmaceutical grade cannabidiol (CBD; Epidiolex; Greenwich Biosciences) in those patients with epilepsy with concomitant tumors enrolled in The University of Alabama at Birmingham CBD Program (NCT02700412 and NCT02695537). Of the three patients with refractory seizures and a history of a primary brain tumor, two had improvement in seizure frequency and all three had improvement in seizure severity. These pilot results suggest that CBD should be further studied for the treatment of brain tumor-related epilepsy.

  18. Transferrin receptor-1 and ferritin heavy and light chains in astrocytic brain tumors

    DEFF Research Database (Denmark)

    Rosager, Ann Mari; Sørensen, Mia D; Dahlrot, Rikke H

    2017-01-01

    Astrocytic brain tumors are the most frequent primary brain tumors. Treatment with radio- and chemotherapy has increased survival making prognostic biomarkers increasingly important. The aim of the present study was to investigate the expression and prognostic value of transferrin receptor-1 (TfR1......) as well as ferritin heavy (FTH) and light (FTL) chain in astrocytic brain tumors. A cohort of 111 astrocytic brain tumors (grade II-IV) was stained immunohistochemically with antibodies against TfR1, FTH, and FTL and scored semi-quantitatively. Double-immunofluorescence stainings were established...... in anaplastic astrocytomas, while high amounts of FTL-positive microglia/macrophages had a negative prognostic value. The results suggest that regulation of the iron metabolism in astrocytic brain tumors is complex involving both autocrine and paracrine signaling....

  19. 99mTc-MIBI-SPECT-studies in the evaluation of brain tumors

    International Nuclear Information System (INIS)

    Ambrus, E.; Pavics, L.; Gruenwald, F.; Barath, B.; Tiszlavicz, L.; Bender, H.; Menzel, C.; Almasi, L.; Lang, J.; Bodosi, M.; Biersack, H.J.; Csernay, L.

    1994-01-01

    Brain SPECT studies were performed 5 and 60 minutes after 99m Tc-MIBI administration in 41 patients with brain tumors confirmed by CT and surgical removal (13 meningeomas, 8 astrocytomas grades I-III, 10 glioblastomas, 10 metastases). 99m Tc-MIBI uptake was found in 32 out of 41 brain tumors. According to the semiquantitative SPECT analysis, the tumor/non tumor radios revealed a statistically significant difference in the early tracer uptake between meningeomas and astrocytomas (+4.73±2.91 vs -1.75±0.75, p 99m Tc-MIBI uptake and its changes with time. We concluded that the combination of an early and late 99m Tc-MIBI brain SPECT may be helpful in the non invasive histological classification of brain tumors and the determination of the grade of theirs malignancy. (orig.) [de

  20. Preclinical validation of electrochemotherapy as an effective treatment for brain tumors

    DEFF Research Database (Denmark)

    Agerholm-Larsen, Birgit; Iversen, Helle K; Ibsen, Per

    2011-01-01

    Electrochemotherapy represents a strategy to enhance chemotherapeutic drug uptake by delivering electrical pulses which exceed the dielectric strength of the cell membrane, causing transient formation of structures that enhance permeabilization. Here we show that brain tumors in a rat model can...... be eliminated by electrochemotherapy with a novel electrode device developed for use in the brain. By using this method, the cytotoxicity of bleomycin can be augmented more than 300-fold because of increased permeabilization and more direct passage of drug to the cytosol, enabling highly efficient local tumor...... to treat primary brain tumors and brain metastases....