Lung vessel segmentation in CT images using graph-cuts

Science.gov (United States)

Zhai, Zhiwei; Staring, Marius; Stoel, Berend C.

2016-03-01

Accurate lung vessel segmentation is an important operation for lung CT analysis. Filters that are based on analyzing the eigenvalues of the Hessian matrix are popular for pulmonary vessel enhancement. However, due to their low response at vessel bifurcations and vessel boundaries, extracting lung vessels by thresholding the vesselness is not sufficiently accurate. Some methods turn to graph-cuts for more accurate segmentation, as it incorporates neighbourhood information. In this work, we propose a new graph-cuts cost function combining appearance and shape, where CT intensity represents appearance and vesselness from a Hessian-based filter represents shape. Due to the amount of voxels in high resolution CT scans, the memory requirement and time consumption for building a graph structure is very high. In order to make the graph representation computationally tractable, those voxels that are considered clearly background are removed from the graph nodes, using a threshold on the vesselness map. The graph structure is then established based on the remaining voxel nodes, source/sink nodes and the neighbourhood relationship of the remaining voxels. Vessels are segmented by minimizing the energy cost function with the graph-cuts optimization framework. We optimized the parameters used in the graph-cuts cost function and evaluated the proposed method with two manually labeled sub-volumes. For independent evaluation, we used 20 CT scans of the VESSEL12 challenge. The evaluation results of the sub-volume data show that the proposed method produced a more accurate vessel segmentation compared to the previous methods, with F1 score 0.76 and 0.69. In the VESSEL12 data-set, our method obtained a competitive performance with an area under the ROC curve of 0.975, especially among the binary submissions.

Computer-aided segmentation system for 3D chest CT

International Nuclear Information System (INIS)

Iwasawa, Tae; Komagata, Takanobu; Ogura, Takashi; Iwao, Yuma; Goto, Toshiyuki; Asakura, Akira; Inoue, Tomio

2012-01-01

We will introduce the quantitative analysis of the chest CT images using computer-assisted segmentation system (Gaussian Histogram Normalized Correlation; GHNC). This system can divide the lung into several patterns, for example, normal, emphysema and fibrous lesion, and measure each lesion volume quantitatively. We analyzed 3D-CT images of 20 patients with lung cancer. GHNC could measure the volumes of emphysema and fibrosis lesions, respectively. GHNC analysis will be feasible for preoperative CT evaluation, especially in the patients with combined pulmonary fibrosis and emphysema. (author)

Transmission Line Resonator Segmented with Series Capacitors

DEFF Research Database (Denmark)

Zhurbenko, Vitaliy; Boer, Vincent; Petersen, Esben Thade

2016-01-01

Transmission line resonators are often used as coils in high field MRI. Due to distributed nature of such resonators, coils based on them produce inhomogeneous field. This work investigates application of series capacitors to improve field homogeneity along the resonator. The equations for optimal...... values of evenly distributed capacitors are presented. The performances of the segmented resonator and a regular transmission line resonator are compared....

Quantification of esophageal wall thickness in CT using atlas-based segmentation technique

Science.gov (United States)

Wang, Jiahui; Kang, Min Kyu; Kligerman, Seth; Lu, Wei

2015-03-01

Esophageal wall thickness is an important predictor of esophageal cancer response to therapy. In this study, we developed a computerized pipeline for quantification of esophageal wall thickness using computerized tomography (CT). We first segmented the esophagus using a multi-atlas-based segmentation scheme. The esophagus in each atlas CT was manually segmented to create a label map. Using image registration, all of the atlases were aligned to the imaging space of the target CT. The deformation field from the registration was applied to the label maps to warp them to the target space. A weighted majority-voting label fusion was employed to create the segmentation of esophagus. Finally, we excluded the lumen from the esophagus using a threshold of -600 HU and measured the esophageal wall thickness. The developed method was tested on a dataset of 30 CT scans, including 15 esophageal cancer patients and 15 normal controls. The mean Dice similarity coefficient (DSC) and mean absolute distance (MAD) between the segmented esophagus and the reference standard were employed to evaluate the segmentation results. Our method achieved a mean Dice coefficient of 65.55 ± 10.48% and mean MAD of 1.40 ± 1.31 mm for all the cases. The mean esophageal wall thickness of cancer patients and normal controls was 6.35 ± 1.19 mm and 6.03 ± 0.51 mm, respectively. We conclude that the proposed method can perform quantitative analysis of esophageal wall thickness and would be useful for tumor detection and tumor response evaluation of esophageal cancer.

Automatic segmentation of lumbar vertebrae in CT images

Science.gov (United States)

Kulkarni, Amruta; Raina, Akshita; Sharifi Sarabi, Mona; Ahn, Christine S.; Babayan, Diana; Gaonkar, Bilwaj; Macyszyn, Luke; Raghavendra, Cauligi

2017-03-01

Lower back pain is one of the most prevalent disorders in the developed/developing world. However, its etiology is poorly understood and treatment is often determined subjectively. In order to quantitatively study the emergence and evolution of back pain, it is necessary to develop consistently measurable markers for pathology. Imaging based measures offer one solution to this problem. The development of imaging based on quantitative biomarkers for the lower back necessitates automated techniques to acquire this data. While the problem of segmenting lumbar vertebrae has been addressed repeatedly in literature, the associated problem of computing relevant biomarkers on the basis of the segmentation has not been addressed thoroughly. In this paper, we propose a Random-Forest based approach that learns to segment vertebral bodies in CT images followed by a biomarker evaluation framework that extracts vertebral heights and widths from the segmentations obtained. Our dataset consists of 15 CT sagittal scans obtained from General Electric Healthcare. Our main approach is divided into three parts: the first stage is image pre-processing which is used to correct for variations in illumination across all the images followed by preparing the foreground and background objects from images; the next stage is Machine Learning using Random-Forests, which distinguishes the interest-point vectors between foreground or background; and the last step is image post-processing, which is crucial to refine the results of classifier. The Dice coefficient was used as a statistical validation metric to evaluate the performance of our segmentations with an average value of 0.725 for our dataset.

Design of wireless data transmission system for spiral CT

International Nuclear Information System (INIS)

Wang Jue; Wang Fuquan; Liu Huaili

2010-01-01

A new wireless data transmission scheme based on UWB was proposed after studying the structure and character of spiral CT transmission system, the system was designed and validated. Using UWB device as wireless module to realize wireless data transmission. Using FPGA as main controller to meet the requirement of timing control for system module. Using two pieces of SDRAM in pingpang operation to realize large capacity storage mechanism. Using USB 2.0 interface to realize high-speed connection with UWB module. The results show that the transmission speed of the system arrival at 16.87 M bit ps within 3 meters, and the precision is 100%, it can be used for line-array spiral CT. (authors)

Mixed raster content segmentation, compression, transmission

CERN Document Server

Pavlidis, George

2017-01-01

This book presents the main concepts in handling digital images of mixed content, traditionally referenced as mixed raster content (MRC), in two main parts. The first includes introductory chapters covering the scientific and technical background aspects, whereas the second presents a set of research and development approaches to tackle key issues in MRC segmentation, compression and transmission. The book starts with a review of color theory and the mechanism of color vision in humans. In turn, the second chapter reviews data coding and compression methods so as to set the background and demonstrate the complexity involved in dealing with MRC. Chapter three addresses the segmentation of images through an extensive literature review, which highlights the various approaches used to tackle MRC segmentation. The second part of the book focuses on the segmentation of color images for optimized compression, including multi-layered decomposition and representation of MRC and the processes that can be employed to op...

Segmentation algorithm of colon based on multi-slice CT colonography

Science.gov (United States)

Hu, Yizhong; Ahamed, Mohammed Shabbir; Takahashi, Eiji; Suzuki, Hidenobu; Kawata, Yoshiki; Niki, Noboru; Suzuki, Masahiro; Iinuma, Gen; Moriyama, Noriyuki

2012-02-01

CT colonography is a radiology test that looks at people's large intestines(colon). CT colonography can screen many options of colon cancer. This test is used to detect polyps or cancers of the colon. CT colonography is safe and reliable. It can be used if people are too sick to undergo other forms of colon cancer screening. In our research, we proposed a method for automatic segmentation of the colon from abdominal computed Tomography (CT) images. Our multistage detection method extracted colon and spited colon into different parts according to the colon anatomy information. We found that among the five segmented parts of the colon, sigmoid (20%) and rectum (50%) are more sensitive toward polyps and masses than the other three parts. Our research focused on detecting the colon by the individual diagnosis of sigmoid and rectum. We think it would make the rapid and easy diagnosis of colon in its earlier stage and help doctors for analysis of correct position of each part and detect the colon rectal cancer much easier.

Multi-phase simultaneous segmentation of tumor in lung 4D-CT data with context information.

Directory of Open Access Journals (Sweden)

Zhengwen Shen

Full Text Available Lung 4D computed tomography (4D-CT plays an important role in high-precision radiotherapy because it characterizes respiratory motion, which is crucial for accurate target definition. However, the manual segmentation of a lung tumor is a heavy workload for doctors because of the large number of lung 4D-CT data slices. Meanwhile, tumor segmentation is still a notoriously challenging problem in computer-aided diagnosis. In this paper, we propose a new method based on an improved graph cut algorithm with context information constraint to find a convenient and robust approach of lung 4D-CT tumor segmentation. We combine all phases of the lung 4D-CT into a global graph, and construct a global energy function accordingly. The sub-graph is first constructed for each phase. A context cost term is enforced to achieve segmentation results in every phase by adding a context constraint between neighboring phases. A global energy function is finally constructed by combining all cost terms. The optimization is achieved by solving a max-flow/min-cut problem, which leads to simultaneous and robust segmentation of the tumor in all the lung 4D-CT phases. The effectiveness of our approach is validated through experiments on 10 different lung 4D-CT cases. The comparison with the graph cut without context constraint, the level set method and the graph cut with star shape prior demonstrates that the proposed method obtains more accurate and robust segmentation results.

Automatic blood vessel based-liver segmentation using the portal phase abdominal CT

Science.gov (United States)

Maklad, Ahmed S.; Matsuhiro, Mikio; Suzuki, Hidenobu; Kawata, Yoshiki; Niki, Noboru; Shimada, Mitsuo; Iinuma, Gen

2018-02-01

Liver segmentation is the basis for computer-based planning of hepatic surgical interventions. In diagnosis and analysis of hepatic diseases and surgery planning, automatic segmentation of liver has high importance. Blood vessel (BV) has showed high performance at liver segmentation. In our previous work, we developed a semi-automatic method that segments the liver through the portal phase abdominal CT images in two stages. First stage was interactive segmentation of abdominal blood vessels (ABVs) and subsequent classification into hepatic (HBVs) and non-hepatic (non-HBVs). This stage had 5 interactions that include selective threshold for bone segmentation, selecting two seed points for kidneys segmentation, selection of inferior vena cava (IVC) entrance for starting ABVs segmentation, identification of the portal vein (PV) entrance to the liver and the IVC-exit for classifying HBVs from other ABVs (non-HBVs). Second stage is automatic segmentation of the liver based on segmented ABVs as described in [4]. For full automation of our method we developed a method [5] that segments ABVs automatically tackling the first three interactions. In this paper, we propose full automation of classifying ABVs into HBVs and non- HBVs and consequently full automation of liver segmentation that we proposed in [4]. Results illustrate that the method is effective at segmentation of the liver through the portal abdominal CT images.

CT evaluation of decrease in attenuation in the superior segment of the left lower lobe

International Nuclear Information System (INIS)

Inaoka, Tsutomu; Takahashi, Koji; Ono, Hidetoshi

2003-01-01

We occasionally see decrease in attenuation in the superior segment of the left lower lobe on normal chest CT and notice that this finding could be seen in elder population. Then, we assessed the frequency, age distribution and cause of decrease in attenuation in the superior segment of the left lower lobe. Chest CT scans of 246 patients without lung or cardiac disorders were retrospectively reviewed. Segmental low attenuation area in the superior segment of the left lower lobe was identified in 12 patients (4.9%), which were 65-92 years old with mean age of 77.2 years old. In all of them, chest CT demonstrated that the tortuous descending aorta compressed directly the superior segmental bronchus of the left lower lobe. It is concluded that the lateral tortuousity of the descending aorta could cause decrease in attenuation in the superior segment of the left lower lobe. (author)

Topological leakage detection and freeze-and-grow propagation for improved CT-based airway segmentation

Science.gov (United States)

Nadeem, Syed Ahmed; Hoffman, Eric A.; Sieren, Jered P.; Saha, Punam K.

2018-03-01

Numerous large multi-center studies are incorporating the use of computed tomography (CT)-based characterization of the lung parenchyma and bronchial tree to understand chronic obstructive pulmonary disease status and progression. To the best of our knowledge, there are no fully automated airway tree segmentation methods, free of the need for user review. A failure in even a fraction of segmentation results necessitates manual revision of all segmentation masks which is laborious considering the thousands of image data sets evaluated in large studies. In this paper, we present a novel CT-based airway tree segmentation algorithm using topological leakage detection and freeze-and-grow propagation. The method is fully automated requiring no manual inputs or post-segmentation editing. It uses simple intensity-based connectivity and a freeze-and-grow propagation algorithm to iteratively grow the airway tree starting from an initial seed inside the trachea. It begins with a conservative parameter and then, gradually shifts toward more generous parameter values. The method was applied on chest CT scans of fifteen subjects at total lung capacity. Airway segmentation results were qualitatively assessed and performed comparably to established airway segmentation method with no major visual leakages.

Lesion Detection in CT Images Using Deep Learning Semantic Segmentation Technique

Science.gov (United States)

Kalinovsky, A.; Liauchuk, V.; Tarasau, A.

2017-05-01

In this paper, the problem of automatic detection of tuberculosis lesion on 3D lung CT images is considered as a benchmark for testing out algorithms based on a modern concept of Deep Learning. For training and testing of the algorithms a domestic dataset of 338 3D CT scans of tuberculosis patients with manually labelled lesions was used. The algorithms which are based on using Deep Convolutional Networks were implemented and applied in three different ways including slice-wise lesion detection in 2D images using semantic segmentation, slice-wise lesion detection in 2D images using sliding window technique as well as straightforward detection of lesions via semantic segmentation in whole 3D CT scans. The algorithms demonstrate superior performance compared to algorithms based on conventional image analysis methods.

Automatic lung tumor segmentation on PET/CT images using fuzzy Markov random field model.

Science.gov (United States)

Guo, Yu; Feng, Yuanming; Sun, Jian; Zhang, Ning; Lin, Wang; Sa, Yu; Wang, Ping

2014-01-01

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.

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.

TU-H-CAMPUS-IeP3-01: Simultaneous PET Restoration and PET/CT Co-Segmentation Using a Variational Method

International Nuclear Information System (INIS)

Li, L; Tan, S; Lu, W

2016-01-01

Purpose: PET images are usually blurred due to the finite spatial resolution, while CT images suffer from low contrast. Segment a tumor from either a single PET or CT image is thus challenging. To make full use of the complementary information between PET and CT, we propose a novel variational method for simultaneous PET image restoration and PET/CT images co-segmentation. Methods: The proposed model was constructed based on the Γ-convergence approximation of Mumford-Shah (MS) segmentation model for PET/CT co-segmentation. Moreover, a PET de-blur process was integrated into the MS model to improve the segmentation accuracy. An interaction edge constraint term over the two modalities were specially designed to share the complementary information. The energy functional was iteratively optimized using an alternate minimization (AM) algorithm. The performance of the proposed method was validated on ten lung cancer cases and five esophageal cancer cases. The ground truth were manually delineated by an experienced radiation oncologist using the complementary visual features of PET and CT. The segmentation accuracy was evaluated by Dice similarity index (DSI) and volume error (VE). Results: The proposed method achieved an expected restoration result for PET image and satisfactory segmentation results for both PET and CT images. For lung cancer dataset, the average DSI (0.72) increased by 0.17 and 0.40 than single PET and CT segmentation. For esophageal cancer dataset, the average DSI (0.85) increased by 0.07 and 0.43 than single PET and CT segmentation. Conclusion: The proposed method took full advantage of the complementary information from PET and CT images. This work was supported in part by the National Cancer Institute Grants R01CA172638. Shan Tan and Laquan Li were supported in part by the National Natural Science Foundation of China, under Grant Nos. 60971112 and 61375018.

Performance evaluation of 2D and 3D deep learning approaches for automatic segmentation of multiple organs on CT images

Science.gov (United States)

Zhou, Xiangrong; Yamada, Kazuma; Kojima, Takuya; Takayama, Ryosuke; Wang, Song; Zhou, Xinxin; Hara, Takeshi; Fujita, Hiroshi

2018-02-01

The purpose of this study is to evaluate and compare the performance of modern deep learning techniques for automatically recognizing and segmenting multiple organ regions on 3D CT images. CT image segmentation is one of the important task in medical image analysis and is still very challenging. Deep learning approaches have demonstrated the capability of scene recognition and semantic segmentation on nature images and have been used to address segmentation problems of medical images. Although several works showed promising results of CT image segmentation by using deep learning approaches, there is no comprehensive evaluation of segmentation performance of the deep learning on segmenting multiple organs on different portions of CT scans. In this paper, we evaluated and compared the segmentation performance of two different deep learning approaches that used 2D- and 3D deep convolutional neural networks (CNN) without- and with a pre-processing step. A conventional approach that presents the state-of-the-art performance of CT image segmentation without deep learning was also used for comparison. A dataset that includes 240 CT images scanned on different portions of human bodies was used for performance evaluation. The maximum number of 17 types of organ regions in each CT scan were segmented automatically and compared to the human annotations by using ratio of intersection over union (IU) as the criterion. The experimental results demonstrated the IUs of the segmentation results had a mean value of 79% and 67% by averaging 17 types of organs that segmented by a 3D- and 2D deep CNN, respectively. All the results of the deep learning approaches showed a better accuracy and robustness than the conventional segmentation method that used probabilistic atlas and graph-cut methods. The effectiveness and the usefulness of deep learning approaches were demonstrated for solving multiple organs segmentation problem on 3D CT images.

Strategies for CT tissue segmentation for Monte Carlo calculations in nuclear medicine dosimetry

DEFF Research Database (Denmark)

Braad, P E N; Andersen, T; Hansen, Søren Baarsgaard

2016-01-01

in the ICRP/ICRU male phantom and in a patient PET/CT-scanned with 124I prior to radioiodine therapy. Results: CT number variations body CT examinations at effective CT doses ∼2 mSv. Monte Carlo calculated absorbed doses depended on both the number of media types and accurate......Purpose: CT images are used for patient specific Monte Carlo treatment planning in radionuclide therapy. The authors investigated the impact of tissue classification, CT image segmentation, and CT errors on Monte Carlo calculated absorbed dose estimates in nuclear medicine. Methods: CT errors...

Objective Ventricle Segmentation in Brain CT with Ischemic Stroke Based on Anatomical Knowledge

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Xiaohua Qian

2017-01-01

Full Text Available Ventricle segmentation is a challenging technique for the development of detection system of ischemic stroke in computed tomography (CT, as ischemic stroke regions are adjacent to the brain ventricle with similar intensity. To address this problem, we developed an objective segmentation system of brain ventricle in CT. The intensity distribution of the ventricle was estimated based on clustering technique, connectivity, and domain knowledge, and the initial ventricle segmentation results were then obtained. To exclude the stroke regions from initial segmentation, a combined segmentation strategy was proposed, which is composed of three different schemes: (1 the largest three-dimensional (3D connected component was considered as the ventricular region; (2 the big stroke areas were removed by the image difference methods based on searching optimal threshold values; (3 the small stroke regions were excluded by the adaptive template algorithm. The proposed method was evaluated on 50 cases of patients with ischemic stroke. The mean Dice, sensitivity, specificity, and root mean squared error were 0.9447, 0.969, 0.998, and 0.219 mm, respectively. This system can offer a desirable performance. Therefore, the proposed system is expected to bring insights into clinic research and the development of detection system of ischemic stroke in CT.

SeLeCT: a lexical cohesion based news story segmentation system

OpenAIRE

Stokes, Nicola; Carthy, Joe; Smeaton, Alan F.

2004-01-01

In this paper we compare the performance of three distinct approaches to lexical cohesion based text segmentation. Most work in this area has focused on the discovery of textual units that discuss subtopic structure within documents. In contrast our segmentation task requires the discovery of topical units of text i.e., distinct news stories from broadcast news programmes. Our approach to news story segmentation (the SeLeCT system) is based on an analysis of lexical cohesive strength between ...

CT examination of segmental liver transplants from living donors. Anatomy and pathological findings

International Nuclear Information System (INIS)

Krupski, G.; Maas, R.; Rogiers, X.; Burdelski, M.; Broelsch, C.E.

1994-01-01

A lack of suitable pediatric donors and significantly better results than conventional transplantation have contributed to the steady increase in the number of segmental liver transplants from living donors throughout the world. This article describes the diagnostic impact of axial CT scans following transplantation in a retrospective evaluation of 18 CT examinations of 10 children with an average age of two years. Both spiral and conventional CT scans permit precise visualization of the postoperative anatomy of the upper abdomen that is more distinct than the images provided by ultrasonic scans. Thus, CT scans better facilitate detection of pathological findings. In 60% of the patients (67% of the examinations), the CT scan permitted a definite diagnosis; in the remaining cases, no morphological correlate to the clinical and laboratory findings was detected. In addition to traditional ultrasonic scanning, computed tomography represents a further noninvasive imaging technique for postoperative diagnostics following segmental liver transplants from living donors. (orig.) [de

The use of CT for evaluate to healing of segmental replantation in rabbits' tibia

International Nuclear Information System (INIS)

Liu Yifan; Hong Tianlu

2000-01-01

Objective: To study the value of CT in the bone healing. Methods: The rabbit's tibia segments were resected and replanted X-ray and CT photograph were taken after operation at 2,4,8,12 week. Results: CT is more clear than X-ray. Conclusion: CT is superior to X-ray photography in observed bone healing

A web-based procedure for liver segmentation in CT images

Science.gov (United States)

Yuan, Rong; Luo, Ming; Wang, Luyao; Xie, Qingguo

2015-03-01

Liver segmentation in CT images has been acknowledged as a basic and indispensable part in systems of computer aided liver surgery for operation design and risk evaluation. In this paper, we will introduce and implement a web-based procedure for liver segmentation to help radiologists and surgeons get an accurate result efficiently and expediently. Several clinical datasets are used to evaluate the accessibility and the accuracy. This procedure seems a promising approach for extraction of liver volumetry of various shapes. Moreover, it is possible for user to access the segmentation wherever the Internet is available without any specific machine.

3D marker-controlled watershed for kidney segmentation in clinical CT exams.

Science.gov (United States)

Wieclawek, Wojciech

2018-02-27

Image segmentation is an essential and non trivial task in computer vision and medical image analysis. Computed tomography (CT) is one of the most accessible medical examination techniques to visualize the interior of a patient's body. Among different computer-aided diagnostic systems, the applications dedicated to kidney segmentation represent a relatively small group. In addition, literature solutions are verified on relatively small databases. The goal of this research is to develop a novel algorithm for fully automated kidney segmentation. This approach is designed for large database analysis including both physiological and pathological cases. This study presents a 3D marker-controlled watershed transform developed and employed for fully automated CT kidney segmentation. The original and the most complex step in the current proposition is an automatic generation of 3D marker images. The final kidney segmentation step is an analysis of the labelled image obtained from marker-controlled watershed transform. It consists of morphological operations and shape analysis. The implementation is conducted in a MATLAB environment, Version 2017a, using i.a. Image Processing Toolbox. 170 clinical CT abdominal studies have been subjected to the analysis. The dataset includes normal as well as various pathological cases (agenesis, renal cysts, tumors, renal cell carcinoma, kidney cirrhosis, partial or radical nephrectomy, hematoma and nephrolithiasis). Manual and semi-automated delineations have been used as a gold standard. Wieclawek Among 67 delineated medical cases, 62 cases are 'Very good', whereas only 5 are 'Good' according to Cohen's Kappa interpretation. The segmentation results show that mean values of Sensitivity, Specificity, Dice, Jaccard, Cohen's Kappa and Accuracy are 90.29, 99.96, 91.68, 85.04, 91.62 and 99.89% respectively. All 170 medical cases (with and without outlines) have been classified by three independent medical experts as 'Very good' in 143

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

Development of high speed and reliable data transmission system for industrial CT

International Nuclear Information System (INIS)

Gao Fuqiang; Dong Yanli; Liu Guohua

2010-01-01

In order to meet the requirements of large capacity,high speed and high reliability of data transmission for industrial CT, a data transmission system based on USB 2.0 was designed. In the process of data transmission, FPGA was the main controller, and USB 2.0 CY7C68013A worked in slave FIFO mode. The system sent the data got from data acquisition system to host computer for image reconstruction. The testing results show that the transmission rate can reach 33 MB/s and the precision is 100%. The system satisfies the requirements of data transmission for industrial CT. (authors)

Active contour modes Crisp: new technique for segmentation of the lungs in CT images

International Nuclear Information System (INIS)

Reboucas Filho, Pedro Pedrosa; Cortez, Paulo Cesar; Holanda, Marcelo Alcantara

2011-01-01

This paper proposes a new active contour model (ACM), called ACM Crisp, and evaluates the segmentation of lungs in computed tomography (CT) images. An ACM draws a curve around or within the object of interest. This curve changes its shape, when some energy acts on it and moves towards the edges of the object. This process is performed by successive iterations of minimization of a given energy, associated with the curve. The ACMs described in the literature have limitations when used for segmentations of CT lung images. The ACM Crisp model overcomes these limitations, since it proposes automatic initiation and new external energy based on rules and radiological pulmonary densities. The paper compares other ACMs with the proposed method, which is shown to be superior. In order to validate the algorithm a medical expert in the field of Pulmonology of the Walter Cantidio University Hospital from the Federal University of Ceara carried out a qualitative analysis. In these analyses 100 CT lung images were used. The segmentation efficiency was evaluated into 5 categories with the following results for the ACM Crisp: 73% excellent, without errors, 20% acceptable, with small errors, and 7% reasonable, with large errors, 0% poor, covering only a small part of the lung, and 0% very bad, making a totally incorrect segmentation. In conclusion the ACM Crisp is considered a useful algorithm to segment CT lung images, and with potential to integrate medical diagnosis systems. (author)

Correction of oral contrast artifacts in CT-based attenuation correction of PET images using an automated segmentation algorithm

International Nuclear Information System (INIS)

Ahmadian, Alireza; Ay, Mohammad R.; Sarkar, Saeed; Bidgoli, Javad H.; Zaidi, Habib

2008-01-01

Oral contrast is usually administered in most X-ray computed tomography (CT) examinations of the abdomen and the pelvis as it allows more accurate identification of the bowel and facilitates the interpretation of abdominal and pelvic CT studies. However, the misclassification of contrast medium with high-density bone in CT-based attenuation correction (CTAC) is known to generate artifacts in the attenuation map (μmap), thus resulting in overcorrection for attenuation of positron emission tomography (PET) images. In this study, we developed an automated algorithm for segmentation and classification of regions containing oral contrast medium to correct for artifacts in CT-attenuation-corrected PET images using the segmented contrast correction (SCC) algorithm. The proposed algorithm consists of two steps: first, high CT number object segmentation using combined region- and boundary-based segmentation and second, object classification to bone and contrast agent using a knowledge-based nonlinear fuzzy classifier. Thereafter, the CT numbers of pixels belonging to the region classified as contrast medium are substituted with their equivalent effective bone CT numbers using the SCC algorithm. The generated CT images are then down-sampled followed by Gaussian smoothing to match the resolution of PET images. A piecewise calibration curve was then used to convert CT pixel values to linear attenuation coefficients at 511 keV. The visual assessment of segmented regions performed by an experienced radiologist confirmed the accuracy of the segmentation and classification algorithms for delineation of contrast-enhanced regions in clinical CT images. The quantitative analysis of generated μmaps of 21 clinical CT colonoscopy datasets showed an overestimation ranging between 24.4% and 37.3% in the 3D-classified regions depending on their volume and the concentration of contrast medium. Two PET/CT studies known to be problematic demonstrated the applicability of the technique in

Early detection of lung cancer from CT images: nodule segmentation and classification using deep learning

Science.gov (United States)

Sharma, Manu; Bhatt, Jignesh S.; Joshi, Manjunath V.

2018-04-01

Lung cancer is one of the most abundant causes of the cancerous deaths worldwide. It has low survival rate mainly due to the late diagnosis. With the hardware advancements in computed tomography (CT) technology, it is now possible to capture the high resolution images of lung region. However, it needs to be augmented by efficient algorithms to detect the lung cancer in the earlier stages using the acquired CT images. To this end, we propose a two-step algorithm for early detection of lung cancer. Given the CT image, we first extract the patch from the center location of the nodule and segment the lung nodule region. We propose to use Otsu method followed by morphological operations for the segmentation. This step enables accurate segmentation due to the use of data-driven threshold. Unlike other methods, we perform the segmentation without using the complete contour information of the nodule. In the second step, a deep convolutional neural network (CNN) is used for the better classification (malignant or benign) of the nodule present in the segmented patch. Accurate segmentation of even a tiny nodule followed by better classification using deep CNN enables the early detection of lung cancer. Experiments have been conducted using 6306 CT images of LIDC-IDRI database. We achieved the test accuracy of 84.13%, with the sensitivity and specificity of 91.69% and 73.16%, respectively, clearly outperforming the state-of-the-art algorithms.

Joint Segmentation of Multiple Thoracic Organs in CT Images with Two Collaborative Deep Architectures.

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Trullo, Roger; Petitjean, Caroline; Nie, Dong; Shen, Dinggang; Ruan, Su

2017-09-01

Computed Tomography (CT) is the standard imaging technique for radiotherapy planning. The delineation of Organs at Risk (OAR) in thoracic CT images is a necessary step before radiotherapy, for preventing irradiation of healthy organs. However, due to low contrast, multi-organ segmentation is a challenge. In this paper, we focus on developing a novel framework for automatic delineation of OARs. Different from previous works in OAR segmentation where each organ is segmented separately, we propose two collaborative deep architectures to jointly segment all organs, including esophagus, heart, aorta and trachea. Since most of the organ borders are ill-defined, we believe spatial relationships must be taken into account to overcome the lack of contrast. The aim of combining two networks is to learn anatomical constraints with the first network, which will be used in the second network, when each OAR is segmented in turn. Specifically, we use the first deep architecture, a deep SharpMask architecture, for providing an effective combination of low-level representations with deep high-level features, and then take into account the spatial relationships between organs by the use of Conditional Random Fields (CRF). Next, the second deep architecture is employed to refine the segmentation of each organ by using the maps obtained on the first deep architecture to learn anatomical constraints for guiding and refining the segmentations. Experimental results show superior performance on 30 CT scans, comparing with other state-of-the-art methods.

Marker-controlled watershed for lymphoma segmentation in sequential CT images

International Nuclear Information System (INIS)

Yan Jiayong; Zhao Binsheng; Wang, Liang; Zelenetz, Andrew; Schwartz, Lawrence H.

2006-01-01

Segmentation of lymphoma containing lymph nodes is a difficult task because of multiple variables associated with the tumor's location, intensity distribution, and contrast to its surrounding tissues. In this paper, we present a reliable and practical marker-controlled watershed algorithm for semi-automated segmentation of lymphoma in sequential CT images. Robust determination of internal and external markers is the key to successful use of the marker-controlled watershed transform in the segmentation of lymphoma and is the focus of this work. The external marker in our algorithm is the circle enclosing the lymphoma in a single slice. The internal marker, however, is determined automatically by combining techniques including Canny edge detection, thresholding, morphological operation, and distance map estimation. To obtain tumor volume, the segmented lymphoma in the current slice needs to be propagated to the adjacent slice to help determine the external and internal markers for delineation of the lymphoma in that slice. The algorithm was applied to 29 lymphomas (size range, 9-53 mm in diameter; mean, 23 mm) in nine patients. A blinded radiologist manually delineated all lymphomas on all slices. The manual result served as the ''gold standard'' for comparison. Several quantitative methods were applied to objectively evaluate the performance of the segmentation algorithm. The algorithm received a mean overlap, overestimation, and underestimation ratios of 83.2%, 13.5%, and 5.5%, respectively. The mean average boundary distance and Hausdorff boundary distance were 0.7 and 3.7 mm. Preliminary results have shown the potential of this computer algorithm to allow reliable segmentation and quantification of lymphomas on sequential CT images

CT liver volumetry using geodesic active contour segmentation with a level-set algorithm

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Suzuki, Kenji; Epstein, Mark L.; Kohlbrenner, Ryan; Obajuluwa, Ademola; Xu, Jianwu; Hori, Masatoshi; Baron, Richard

2010-03-01

Automatic liver segmentation on CT images is challenging because the liver often abuts other organs of a similar density. Our purpose was to develop an accurate automated liver segmentation scheme for measuring liver volumes. We developed an automated volumetry scheme for the liver in CT based on a 5 step schema. First, an anisotropic smoothing filter was applied to portal-venous phase CT images to remove noise while preserving the liver structure, followed by an edge enhancer to enhance the liver boundary. By using the boundary-enhanced image as a speed function, a fastmarching algorithm generated an initial surface that roughly estimated the liver shape. A geodesic-active-contour segmentation algorithm coupled with level-set contour-evolution refined the initial surface so as to more precisely fit the liver boundary. The liver volume was calculated based on the refined liver surface. Hepatic CT scans of eighteen prospective liver donors were obtained under a liver transplant protocol with a multi-detector CT system. Automated liver volumes obtained were compared with those manually traced by a radiologist, used as "gold standard." The mean liver volume obtained with our scheme was 1,520 cc, whereas the mean manual volume was 1,486 cc, with the mean absolute difference of 104 cc (7.0%). CT liver volumetrics based on an automated scheme agreed excellently with "goldstandard" manual volumetrics (intra-class correlation coefficient was 0.95) with no statistically significant difference (p(F<=f)=0.32), and required substantially less completion time. Our automated scheme provides an efficient and accurate way of measuring liver volumes.

Deep convolutional networks for pancreas segmentation in CT imaging

Science.gov (United States)

Roth, Holger R.; Farag, Amal; Lu, Le; Turkbey, Evrim B.; Summers, Ronald M.

2015-03-01

Automatic organ segmentation is an important prerequisite for many computer-aided diagnosis systems. The high anatomical variability of organs in the abdomen, such as the pancreas, prevents many segmentation methods from achieving high accuracies when compared to state-of-the-art segmentation of organs like the liver, heart or kidneys. Recently, the availability of large annotated training sets and the accessibility of affordable parallel computing resources via GPUs have made it feasible for "deep learning" methods such as convolutional networks (ConvNets) to succeed in image classification tasks. These methods have the advantage that used classification features are trained directly from the imaging data. We present a fully-automated bottom-up method for pancreas segmentation in computed tomography (CT) images of the abdomen. The method is based on hierarchical coarse-to-fine classification of local image regions (superpixels). Superpixels are extracted from the abdominal region using Simple Linear Iterative Clustering (SLIC). An initial probability response map is generated, using patch-level confidences and a two-level cascade of random forest classifiers, from which superpixel regions with probabilities larger 0.5 are retained. These retained superpixels serve as a highly sensitive initial input of the pancreas and its surroundings to a ConvNet that samples a bounding box around each superpixel at different scales (and random non-rigid deformations at training time) in order to assign a more distinct probability of each superpixel region being pancreas or not. We evaluate our method on CT images of 82 patients (60 for training, 2 for validation, and 20 for testing). Using ConvNets we achieve maximum Dice scores of an average 68% +/- 10% (range, 43-80%) in testing. This shows promise for accurate pancreas segmentation, using a deep learning approach and compares favorably to state-of-the-art methods.

Multiatlas whole heart segmentation of CT data using conditional entropy for atlas ranking and selection.

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Zhuang, Xiahai; Bai, Wenjia; Song, Jingjing; Zhan, Songhua; Qian, Xiaohua; Shi, Wenzhe; Lian, Yanyun; Rueckert, Daniel

2015-07-01

Cardiac computed tomography (CT) is widely used in clinical diagnosis of cardiovascular diseases. Whole heart segmentation (WHS) plays a vital role in developing new clinical applications of cardiac CT. However, the shape and appearance of the heart can vary greatly across different scans, making the automatic segmentation particularly challenging. The objective of this work is to develop and evaluate a multiatlas segmentation (MAS) scheme using a new atlas ranking and selection algorithm for automatic WHS of CT data. Research on different MAS strategies and their influence on WHS performance are limited. This work provides a detailed comparison study evaluating the impacts of label fusion, atlas ranking, and sizes of the atlas database on the segmentation performance. Atlases in a database were registered to the target image using a hierarchical registration scheme specifically designed for cardiac images. A subset of the atlases were selected for label fusion, according to the authors' proposed atlas ranking criterion which evaluated the performance of each atlas by computing the conditional entropy of the target image given the propagated atlas labeling. Joint label fusion was used to combine multiple label estimates to obtain the final segmentation. The authors used 30 clinical cardiac CT angiography (CTA) images to evaluate the proposed MAS scheme and to investigate different segmentation strategies. The mean WHS Dice score of the proposed MAS method was 0.918 ± 0.021, and the mean runtime for one case was 13.2 min on a workstation. This MAS scheme using joint label fusion generated significantly better Dice scores than the other label fusion strategies, including majority voting (0.901 ± 0.276, p ranking study, the proposed criterion based on conditional entropy yielded a performance curve with higher WHS Dice scores compared to the conventional schemes (p ranking algorithm and joint label fusion, the MAS scheme is able to generate accurate segmentation

The relevance of MRI for patient modeling in head and neck hyperthermia treatment planning: A comparison of CT and CT-MRI based tissue segmentation on simulated temperature

International Nuclear Information System (INIS)

Verhaart, René F.; Paulides, Margarethus M.; Fortunati, Valerio; Walsum, Theo van; Veenland, Jifke F.; Verduijn, Gerda M.; Lugt, Aad van der

2014-01-01

Purpose: In current clinical practice, head and neck (H and N) hyperthermia treatment planning (HTP) is solely based on computed tomography (CT) images. Magnetic resonance imaging (MRI) provides superior soft-tissue contrast over CT. The purpose of the authors’ study is to investigate the relevance of using MRI in addition to CT for patient modeling in H and N HTP. Methods: CT and MRI scans were acquired for 11 patients in an immobilization mask. Three observers manually segmented on CT, MRI T1 weighted (MRI-T1w), and MRI T2 weighted (MRI-T2w) images the following thermo-sensitive tissues: cerebrum, cerebellum, brainstem, myelum, sclera, lens, vitreous humor, and the optical nerve. For these tissues that are used for patient modeling in H and N HTP, the interobserver variation of manual tissue segmentation in CT and MRI was quantified with the mean surface distance (MSD). Next, the authors compared the impact of CT and CT and MRI based patient models on the predicted temperatures. For each tissue, the modality was selected that led to the lowest observer variation and inserted this in the combined CT and MRI based patient model (CT and MRI), after a deformable image registration. In addition, a patient model with a detailed segmentation of brain tissues (including white matter, gray matter, and cerebrospinal fluid) was created (CT and MRI db ). To quantify the relevance of MRI based segmentation for H and N HTP, the authors compared the predicted maximum temperatures in the segmented tissues (T max ) and the corresponding specific absorption rate (SAR) of the patient models based on (1) CT, (2) CT and MRI, and (3) CT and MRI db . Results: In MRI, a similar or reduced interobserver variation was found compared to CT (maximum of median MSD in CT: 0.93 mm, MRI-T1w: 0.72 mm, MRI-T2w: 0.66 mm). Only for the optical nerve the interobserver variation is significantly lower in CT compared to MRI (median MSD in CT: 0.58 mm, MRI-T1w: 1.27 mm, MRI-T2w: 1.40 mm). Patient

The relevance of MRI for patient modeling in head and neck hyperthermia treatment planning: A comparison of CT and CT-MRI based tissue segmentation on simulated temperature

Energy Technology Data Exchange (ETDEWEB)

Verhaart, René F., E-mail: r.f.verhaart@erasmusmc.nl; Paulides, Margarethus M. [Hyperthermia Unit, Department of Radiation Oncology, Erasmus MC - Cancer Institute, Groene Hilledijk 301, Rotterdam 3008 AE (Netherlands); Fortunati, Valerio; Walsum, Theo van; Veenland, Jifke F. [Biomedical Imaging Group of Rotterdam, Department of Medical Informatics and Radiology, Erasmus MC, Dr. Molewaterplein 50/60, Rotterdam 3015 GE (Netherlands); Verduijn, Gerda M. [Department of Radiation Oncology, Erasmus MC - Cancer Institute, Groene Hilledijk 301, Rotterdam 3008 AE (Netherlands); Lugt, Aad van der [Department of Radiology, Erasmus MC, Dr. Molewaterplein 50/60, Rotterdam 3015 GE (Netherlands)

2014-12-15

Purpose: In current clinical practice, head and neck (H and N) hyperthermia treatment planning (HTP) is solely based on computed tomography (CT) images. Magnetic resonance imaging (MRI) provides superior soft-tissue contrast over CT. The purpose of the authors’ study is to investigate the relevance of using MRI in addition to CT for patient modeling in H and N HTP. Methods: CT and MRI scans were acquired for 11 patients in an immobilization mask. Three observers manually segmented on CT, MRI T1 weighted (MRI-T1w), and MRI T2 weighted (MRI-T2w) images the following thermo-sensitive tissues: cerebrum, cerebellum, brainstem, myelum, sclera, lens, vitreous humor, and the optical nerve. For these tissues that are used for patient modeling in H and N HTP, the interobserver variation of manual tissue segmentation in CT and MRI was quantified with the mean surface distance (MSD). Next, the authors compared the impact of CT and CT and MRI based patient models on the predicted temperatures. For each tissue, the modality was selected that led to the lowest observer variation and inserted this in the combined CT and MRI based patient model (CT and MRI), after a deformable image registration. In addition, a patient model with a detailed segmentation of brain tissues (including white matter, gray matter, and cerebrospinal fluid) was created (CT and MRI{sub db}). To quantify the relevance of MRI based segmentation for H and N HTP, the authors compared the predicted maximum temperatures in the segmented tissues (T{sub max}) and the corresponding specific absorption rate (SAR) of the patient models based on (1) CT, (2) CT and MRI, and (3) CT and MRI{sub db}. Results: In MRI, a similar or reduced interobserver variation was found compared to CT (maximum of median MSD in CT: 0.93 mm, MRI-T1w: 0.72 mm, MRI-T2w: 0.66 mm). Only for the optical nerve the interobserver variation is significantly lower in CT compared to MRI (median MSD in CT: 0.58 mm, MRI-T1w: 1.27 mm, MRI-T2w: 1.40 mm

The relevance of MRI for patient modeling in head and neck hyperthermia treatment planning: a comparison of CT and CT-MRI based tissue segmentation on simulated temperature.

Science.gov (United States)

Verhaart, René F; Fortunati, Valerio; Verduijn, Gerda M; van der Lugt, Aad; van Walsum, Theo; Veenland, Jifke F; Paulides, Margarethus M

2014-12-01

In current clinical practice, head and neck (H&N) hyperthermia treatment planning (HTP) is solely based on computed tomography (CT) images. Magnetic resonance imaging (MRI) provides superior soft-tissue contrast over CT. The purpose of the authors' study is to investigate the relevance of using MRI in addition to CT for patient modeling in H&N HTP. CT and MRI scans were acquired for 11 patients in an immobilization mask. Three observers manually segmented on CT, MRI T1 weighted (MRI-T1w), and MRI T2 weighted (MRI-T2w) images the following thermo-sensitive tissues: cerebrum, cerebellum, brainstem, myelum, sclera, lens, vitreous humor, and the optical nerve. For these tissues that are used for patient modeling in H&N HTP, the interobserver variation of manual tissue segmentation in CT and MRI was quantified with the mean surface distance (MSD). Next, the authors compared the impact of CT and CT and MRI based patient models on the predicted temperatures. For each tissue, the modality was selected that led to the lowest observer variation and inserted this in the combined CT and MRI based patient model (CT and MRI), after a deformable image registration. In addition, a patient model with a detailed segmentation of brain tissues (including white matter, gray matter, and cerebrospinal fluid) was created (CT and MRIdb). To quantify the relevance of MRI based segmentation for H&N HTP, the authors compared the predicted maximum temperatures in the segmented tissues (Tmax) and the corresponding specific absorption rate (SAR) of the patient models based on (1) CT, (2) CT and MRI, and (3) CT and MRIdb. In MRI, a similar or reduced interobserver variation was found compared to CT (maximum of median MSD in CT: 0.93 mm, MRI-T1w: 0.72 mm, MRI-T2w: 0.66 mm). Only for the optical nerve the interobserver variation is significantly lower in CT compared to MRI (median MSD in CT: 0.58 mm, MRI-T1w: 1.27 mm, MRI-T2w: 1.40 mm). Patient models based on CT (Tmax: 38.0 °C) and CT and MRI

Short linear shadows connecting pulmonary segmental arteries to oblique fissures in volumetric thin-section CT images: comparing CT, micro-CT and histopathology

International Nuclear Information System (INIS)

Guan, Chun-Shuang; Ma, Da-Qing; Chen, Jiang-Hong; Chen, Bu-Dong; Cui, Dun; Zhang, Yan-Song; Liu, Wei-Hua

2016-01-01

To retrospectively evaluate short linear shadows connecting pulmonary segmental arteries to oblique fissures in thin-section CT images and determine their anatomical basis. CT scanning was performed on 108 patients and 11 lung specimens with no lung diseases around the oblique fissures or hilar. Two radiologists evaluated the imaging. The parameters included length, thickness of short linear shadows, pulmonary segmental artery variations, and traction interlobar fissures, etc. The short linear shadows were not related to sex, age, or smoking history. The lengths of the short linear shadows were generally within 10 mm. The thicknesses of the short linear shadows ranged from 1 to 2 mm. Of the patients, 26.9 % showed pulmonary segmental artery variations; 66.7 % of short linear shadows pulled oblique fissures. In three-dimensional images, the short linear shadows appeared as arc planes, with one side edge connected to the oblique fissure, one side edge connected to a pulmonary segmental artery. On the tissue slices, the short linear shadow exhibited a band structure composed of connective tissues, small blood vessels, and small lymphatic vessels. Short linear shadows are a type of normal intrapulmonary membranes and can maintain the integrity of the oblique fissures and hilar structure. (orig.)

A semiautomatic CT-based ensemble segmentation of lung tumors: comparison with oncologists' delineations and with the surgical specimen.

Science.gov (United States)

Rios Velazquez, Emmanuel; Aerts, Hugo J W L; Gu, Yuhua; Goldgof, Dmitry B; De Ruysscher, Dirk; Dekker, Andre; Korn, René; Gillies, Robert J; Lambin, Philippe

2012-11-01

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). For 20 NSCLC patients (stages 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 are available on www.cancerdata.org. 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 cm(3), mean±SD) and manual delineations (81.9±94.1 cm(3); 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). Semiautomatic segmentation of the primary tumor on CT demonstrated high agreement with CT/PET manual delineations and strongly correlated with the macroscopic diameter considered as 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. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

Fully automatic algorithm for segmenting full human diaphragm in non-contrast CT Images

Science.gov (United States)

Karami, Elham; Gaede, Stewart; Lee, Ting-Yim; Samani, Abbas

2015-03-01

The diaphragm is a sheet of muscle which separates the thorax from the abdomen and it acts as the most important muscle of the respiratory system. As such, an accurate segmentation of the diaphragm, not only provides key information for functional analysis of the respiratory system, but also can be used for locating other abdominal organs such as the liver. However, diaphragm segmentation is extremely challenging in non-contrast CT images due to the diaphragm's similar appearance to other abdominal organs. In this paper, we present a fully automatic algorithm for diaphragm segmentation in non-contrast CT images. The method is mainly based on a priori knowledge about the human diaphragm anatomy. The diaphragm domes are in contact with the lungs and the heart while its circumference runs along the lumbar vertebrae of the spine as well as the inferior border of the ribs and sternum. As such, the diaphragm can be delineated by segmentation of these organs followed by connecting relevant parts of their outline properly. More specifically, the bottom surface of the lungs and heart, the spine borders and the ribs are delineated, leading to a set of scattered points which represent the diaphragm's geometry. Next, a B-spline filter is used to find the smoothest surface which pass through these points. This algorithm was tested on a noncontrast CT image of a lung cancer patient. The results indicate that there is an average Hausdorff distance of 2.96 mm between the automatic and manually segmented diaphragms which implies a favourable accuracy.

Concurrent segmentation of the prostate on MRI and CT via linked statistical shape models for radiotherapy planning

International Nuclear Information System (INIS)

Chowdhury, Najeeb; Toth, Robert; Chappelow, Jonathan; Kim, Sung; Motwani, Sabin; Punekar, Salman; Lin Haibo; Both, Stefan; Vapiwala, Neha; Hahn, Stephen; Madabhushi, Anant

2012-01-01

Purpose: Prostate gland segmentation is a critical step in prostate radiotherapy planning, where dose plans are typically formulated on CT. Pretreatment MRI is now beginning to be acquired at several medical centers. Delineation of the prostate on MRI is acknowledged as being significantly simpler to perform, compared to delineation on CT. In this work, the authors present a novel framework for building a linked statistical shape model (LSSM), a statistical shape model (SSM) that links the shape variation of a structure of interest (SOI) across multiple imaging modalities. This framework is particularly relevant in scenarios where accurate boundary delineations of the SOI on one of the modalities may not be readily available, or difficult to obtain, for training a SSM. In this work the authors apply the LSSM in the context of multimodal prostate segmentation for radiotherapy planning, where the prostate is concurrently segmented on MRI and CT. Methods: The framework comprises a number of logically connected steps. The first step utilizes multimodal registration of MRI and CT to map 2D boundary delineations of the prostate from MRI onto corresponding CT images, for a set of training studies. Hence, the scheme obviates the need for expert delineations of the gland on CT for explicitly constructing a SSM for prostate segmentation on CT. The delineations of the prostate gland on MRI and CT allows for 3D reconstruction of the prostate shape which facilitates the building of the LSSM. In order to perform concurrent prostate MRI and CT segmentation using the LSSM, the authors employ a region-based level set approach where the authors deform the evolving prostate boundary to simultaneously fit to MRI and CT images in which voxels are classified to be either part of the prostate or outside the prostate. The classification is facilitated by using a combination of MRI-CT probabilistic spatial atlases and a random forest classifier, driven by gradient and Haar features

Automated segmentation of murine lung tumors in x-ray micro-CT images

Science.gov (United States)

Swee, Joshua K. Y.; Sheridan, Clare; de Bruin, Elza; Downward, Julian; Lassailly, Francois; Pizarro, Luis

2014-03-01

Recent years have seen micro-CT emerge as a means of providing imaging analysis in pre-clinical study, with in-vivo micro-CT having been shown to be particularly applicable to the examination of murine lung tumors. Despite this, existing studies have involved substantial human intervention during the image analysis process, with the use of fully-automated aids found to be almost non-existent. We present a new approach to automate the segmentation of murine lung tumors designed specifically for in-vivo micro-CT-based pre-clinical lung cancer studies that addresses the specific requirements of such study, as well as the limitations human-centric segmentation approaches experience when applied to such micro-CT data. Our approach consists of three distinct stages, and begins by utilizing edge enhancing and vessel enhancing non-linear anisotropic diffusion filters to extract anatomy masks (lung/vessel structure) in a pre-processing stage. Initial candidate detection is then performed through ROI reduction utilizing obtained masks and a two-step automated segmentation approach that aims to extract all disconnected objects within the ROI, and consists of Otsu thresholding, mathematical morphology and marker-driven watershed. False positive reduction is finally performed on initial candidates through random-forest-driven classification using the shape, intensity, and spatial features of candidates. We provide validation of our approach using data from an associated lung cancer study, showing favorable results both in terms of detection (sensitivity=86%, specificity=89%) and structural recovery (Dice Similarity=0.88) when compared against manual specialist annotation.

[Automated detection and volumetric segmentation of the spleen in CT scans].

Science.gov (United States)

Hammon, M; Dankerl, P; Kramer, M; Seifert, S; Tsymbal, A; Costa, M J; Janka, R; Uder, M; Cavallaro, A

2012-08-01

To introduce automated detection and volumetric segmentation of the spleen in spiral CT scans with the THESEUS-MEDICO software. The consistency between automated volumetry (aV), estimated volume determination (eV) and manual volume segmentation (mV) was evaluated. Retrospective evaluation of the CAD system based on methods like "marginal space learning" and "boosting algorithms". 3 consecutive spiral CT scans (thoraco-abdominal; portal-venous contrast agent phase; 1 or 5 mm slice thickness) of 15 consecutive lymphoma patients were included. The eV: 30 cm³ + 0.58 (width × length × thickness of the spleen) and the mV as the reference standard were determined by an experienced radiologist. The aV could be performed in all CT scans within 15.2 (± 2.4) seconds. The average splenic volume measured by aV was 268.21 ± 114.67 cm³ compared to 281.58 ± 130.21 cm³ in mV and 268.93 ± 104.60 cm³ in eV. The correlation coefficient was 0.99 (coefficient of determination (R²) = 0.98) for aV and mV, 0.91 (R² = 0.83) for mV and eV and 0.91 (R² = 0.82) for aV and eV. There was an almost perfect correlation of the changes in splenic volume measured with the new aV and mV (0.92; R² = 0.84), mV and eV (0.95; R² = 0.91) and aV and eV (0.83; R² = 0.69) between two time points. The automated detection and volumetric segmentation software rapidly provides an accurate measurement of the splenic volume in CT scans. Knowledge about splenic volume and its change between two examinations provides valuable clinical information without effort for the radiologist. © Georg Thieme Verlag KG Stuttgart · New York.

Automated detection and volumetric segmentation of the spleen in CT scans

International Nuclear Information System (INIS)

Hammon, M.; Dankerl, P.; Janka, R.; Uder, M.; Cavallaro, A.; Kramer, M.; Seifert, S.; Tsymbal, A.; Costa, M.J.

2012-01-01

To introduce automated detection and volumetric segmentation of the spleen in spiral CT scans with the THESEUS-MEDICO software. The consistency between automated volumetry (aV), estimated volume determination (eV) and manual volume segmentation (mV) was evaluated. Retrospective evaluation of the CAD system based on methods like ''marginal space learning'' and ''boosting algorithms''. 3 consecutive spiral CT scans (thoraco-abdominal; portal-venous contrast agent phase; 1 or 5 mm slice thickness) of 15 consecutive lymphoma patients were included. The eV: 30 cm 3 + 0.58 (width x length x thickness of the spleen) and the mV as the reference standard were determined by an experienced radiologist. The aV could be performed in all CT scans within 15.2 (± 2.4) seconds. The average splenic volume measured by aV was 268.21 ± 114.67 cm 3 compared to 281.58 ± 130.21 cm 3 in mV and 268.93 ± 104.60 cm 3 in eV. The correlation coefficient was 0.99 (coefficient of determination (R 2 ) = 0.98) for aV and mV, 0.91 (R 2 = 0.83) for mV and eV and 0.91 (R 2 = 0.82) for aV and eV. There was an almost perfect correlation of the changes in splenic volume measured with the new aV and mV (0.92; R 2 = 0.84), mV and eV (0.95; R 2 = 0.91) and aV and eV (0.83; R 2 = 0.69) between two time points. The automated detection and volumetric segmentation software rapidly provides an accurate measurement of the splenic volume in CT scans. Knowledge about splenic volume and its change between two examinations provides valuable clinical information without effort for the radiologist. (orig.)

Segmentation of hepatic artery in multi-phase liver CT using directional dilation and connectivity analysis

Science.gov (United States)

Wang, Lei; Schnurr, Alena-Kathrin; Zidowitz, Stephan; Georgii, Joachim; Zhao, Yue; Razavi, Mohammad; Schwier, Michael; Hahn, Horst K.; Hansen, Christian

2016-03-01

Segmentation of hepatic arteries in multi-phase computed tomography (CT) images is indispensable in liver surgery planning. During image acquisition, the hepatic artery is enhanced by the injection of contrast agent. The enhanced signals are often not stably acquired due to non-optimal contrast timing. Other vascular structure, such as hepatic vein or portal vein, can be enhanced as well in the arterial phase, which can adversely affect the segmentation results. Furthermore, the arteries might suffer from partial volume effects due to their small diameter. To overcome these difficulties, we propose a framework for robust hepatic artery segmentation requiring a minimal amount of user interaction. First, an efficient multi-scale Hessian-based vesselness filter is applied on the artery phase CT image, aiming to enhance vessel structures with specified diameter range. Second, the vesselness response is processed using a Bayesian classifier to identify the most probable vessel structures. Considering the vesselness filter normally performs not ideally on the vessel bifurcations or the segments corrupted by noise, two vessel-reconnection techniques are proposed. The first technique uses a directional morphological operator to dilate vessel segments along their centerline directions, attempting to fill the gap between broken vascular segments. The second technique analyzes the connectivity of vessel segments and reconnects disconnected segments and branches. Finally, a 3D vessel tree is reconstructed. The algorithm has been evaluated using 18 CT images of the liver. To quantitatively measure the similarities between segmented and reference vessel trees, the skeleton coverage and mean symmetric distance are calculated to quantify the agreement between reference and segmented vessel skeletons, resulting in an average of 0:55+/-0:27 and 12:7+/-7:9 mm (mean standard deviation), respectively.

Automatic segmentation of phase-correlated CT scans through nonrigid image registration using geometrically regularized free-form deformation

International Nuclear Information System (INIS)

Shekhar, Raj; Lei, Peng; Castro-Pareja, Carlos R.; Plishker, William L.; D'Souza, Warren D.

2007-01-01

Conventional radiotherapy is planned using free-breathing computed tomography (CT), ignoring the motion and deformation of the anatomy from respiration. New breath-hold-synchronized, gated, and four-dimensional (4D) CT acquisition strategies are enabling radiotherapy planning utilizing a set of CT scans belonging to different phases of the breathing cycle. Such 4D treatment planning relies on the availability of tumor and organ contours in all phases. The current practice of manual segmentation is impractical for 4D CT, because it is time consuming and tedious. A viable solution is registration-based segmentation, through which contours provided by an expert for a particular phase are propagated to all other phases while accounting for phase-to-phase motion and anatomical deformation. Deformable image registration is central to this task, and a free-form deformation-based nonrigid image registration algorithm will be presented. Compared with the original algorithm, this version uses novel, computationally simpler geometric constraints to preserve the topology of the dense control-point grid used to represent free-form deformation and prevent tissue fold-over. Using mean squared difference as an image similarity criterion, the inhale phase is registered to the exhale phase of lung CT scans of five patients and of characteristically low-contrast abdominal CT scans of four patients. In addition, using expert contours for the inhale phase, the corresponding contours were automatically generated for the exhale phase. The accuracy of the segmentation (and hence deformable image registration) was judged by comparing automatically segmented contours with expert contours traced directly in the exhale phase scan using three metrics: volume overlap index, root mean square distance, and Hausdorff distance. The accuracy of the segmentation (in terms of radial distance mismatch) was approximately 2 mm in the thorax and 3 mm in the abdomen, which compares favorably to the

Deep learning of the sectional appearances of 3D CT images for anatomical structure segmentation based on an FCN voting method.

Science.gov (United States)

Zhou, Xiangrong; Takayama, Ryosuke; Wang, Song; Hara, Takeshi; Fujita, Hiroshi

2017-10-01

We propose a single network trained by pixel-to-label deep learning to address the general issue of automatic multiple organ segmentation in three-dimensional (3D) computed tomography (CT) images. Our method can be described as a voxel-wise multiple-class classification scheme for automatically assigning labels to each pixel/voxel in a 2D/3D CT image. We simplify the segmentation algorithms of anatomical structures (including multiple organs) in a CT image (generally in 3D) to a majority voting scheme over the semantic segmentation of multiple 2D slices drawn from different viewpoints with redundancy. The proposed method inherits the spirit of fully convolutional networks (FCNs) that consist of "convolution" and "deconvolution" layers for 2D semantic image segmentation, and expands the core structure with 3D-2D-3D transformations to adapt to 3D CT image segmentation. All parameters in the proposed network are trained pixel-to-label from a small number of CT cases with human annotations as the ground truth. The proposed network naturally fulfills the requirements of multiple organ segmentations in CT cases of different sizes that cover arbitrary scan regions without any adjustment. The proposed network was trained and validated using the simultaneous segmentation of 19 anatomical structures in the human torso, including 17 major organs and two special regions (lumen and content inside of stomach). Some of these structures have never been reported in previous research on CT segmentation. A database consisting of 240 (95% for training and 5% for testing) 3D CT scans, together with their manually annotated ground-truth segmentations, was used in our experiments. The results show that the 19 structures of interest were segmented with acceptable accuracy (88.1% and 87.9% voxels in the training and testing datasets, respectively, were labeled correctly) against the ground truth. We propose a single network based on pixel-to-label deep learning to address the challenging

Segmentation of human skull in MRI using statistical shape information from CT data.

Science.gov (United States)

Wang, Defeng; Shi, Lin; Chu, Winnie C W; Cheng, Jack C Y; Heng, Pheng Ann

2009-09-01

To automatically segment the skull from the MRI data using a model-based three-dimensional segmentation scheme. This study exploited the statistical anatomy extracted from the CT data of a group of subjects by means of constructing an active shape model of the skull surfaces. To construct a reliable shape model, a novel approach was proposed to optimize the automatic landmarking on the coupled surfaces (i.e., the skull vault) by minimizing the description length that incorporated local thickness information. This model was then used to locate the skull shape in MRI of a different group of patients. Compared with performing landmarking separately on the coupled surfaces, the proposed landmarking method constructed models that had better generalization ability and specificity. The segmentation accuracies were measured by the Dice coefficient and the set difference, and compared with the method based on mathematical morphology operations. The proposed approach using the active shape model based on the statistical skull anatomy presented in the head CT data contributes to more reliable segmentation of the skull from MRI data.

Patient size and x-ray transmission in body CT.

Science.gov (United States)

Ogden, Kent; Huda, Walter; Scalzetti, Ernest M; Roskopf, Marsha L

2004-04-01

Physical characteristics were obtained for 196 patients undergoing chest and abdomen computed tomography (CT) examinations. Computed tomography sections for these patients having no evident pathology were analyzed to determine patient dimensions (AP and lateral), together with the average attenuation coefficient. Patient weights ranged from approximately 3 kg to about 120 kg. For chest CT, the mean Hounsfield unit (HU) fell from about -120 HU for newborns to about -300 HU for adults. For abdominal CT, the mean HU for children and normal-sized adults was about 20 HU, but decreased to below -50 HU for adults weighing more than 100 kg. The effective photon energy and percent energy fluence transmitted through a given patient size and composition was calculated for representative x-ray spectra at 80, 100, 120, and 140 kV tube potentials. A 70-kg adult scanned at 120 kVp transmits 2.6% of the energy fluence for chest and 0.7% for abdomen CT examinations. Reducing the patient size to 10 kg increases transmission by an order of magnitude. For 70 kg patients, effective energies in body CT range from approximately 50 keV at 80 kVp to approximately 67 keV at 140 kVp; increasing patient size from 10 to 120 kg resulted in an increase in effective photon energy of approximately 4 keV. The x-ray transmission data and effective photon energy data can be used to determine CT image noise and image contrast, respectively, and information on patient size and composition can be used to determine patient doses.

Abdominal multi-organ segmentation from CT images using conditional shape-location and unsupervised intensity priors.

Science.gov (United States)

Okada, Toshiyuki; Linguraru, Marius George; Hori, Masatoshi; Summers, Ronald M; Tomiyama, Noriyuki; Sato, Yoshinobu

2015-12-01

This paper addresses the automated segmentation of multiple organs in upper abdominal computed tomography (CT) data. The aim of our study is to develop methods to effectively construct the conditional priors and use their prediction power for more accurate segmentation as well as easy adaptation to various imaging conditions in CT images, as observed in clinical practice. We propose a general framework of multi-organ segmentation which effectively incorporates interrelations among multiple organs and easily adapts to various imaging conditions without the need for supervised intensity information. The features of the framework are as follows: (1) A method for modeling conditional shape and location (shape-location) priors, which we call prediction-based priors, is developed to derive accurate priors specific to each subject, which enables the estimation of intensity priors without the need for supervised intensity information. (2) Organ correlation graph is introduced, which defines how the conditional priors are constructed and segmentation processes of multiple organs are executed. In our framework, predictor organs, whose segmentation is sufficiently accurate by using conventional single-organ segmentation methods, are pre-segmented, and the remaining organs are hierarchically segmented using conditional shape-location priors. The proposed framework was evaluated through the segmentation of eight abdominal organs (liver, spleen, left and right kidneys, pancreas, gallbladder, aorta, and inferior vena cava) from 134 CT data from 86 patients obtained under six imaging conditions at two hospitals. The experimental results show the effectiveness of the proposed prediction-based priors and the applicability to various imaging conditions without the need for supervised intensity information. Average Dice coefficients for the liver, spleen, and kidneys were more than 92%, and were around 73% and 67% for the pancreas and gallbladder, respectively. Copyright © 2015

Automatic lung segmentation in functional SPECT images using active shape models trained on reference lung shapes from CT.

Science.gov (United States)

Cheimariotis, Grigorios-Aris; Al-Mashat, Mariam; Haris, Kostas; Aletras, Anthony H; Jögi, Jonas; Bajc, Marika; Maglaveras, Nicolaos; Heiberg, Einar

2018-02-01

Image segmentation is an essential step in quantifying the extent of reduced or absent lung function. The aim of this study is to develop and validate a new tool for automatic segmentation of lungs in ventilation and perfusion SPECT images and compare automatic and manual SPECT lung segmentations with reference computed tomography (CT) volumes. A total of 77 subjects (69 patients with obstructive lung disease, and 8 subjects without apparent perfusion of ventilation loss) performed low-dose CT followed by ventilation/perfusion (V/P) SPECT examination in a hybrid gamma camera system. In the training phase, lung shapes from the 57 anatomical low-dose CT images were used to construct two active shape models (right lung and left lung) which were then used for image segmentation. The algorithm was validated in 20 patients, comparing its results to reference delineation of corresponding CT images, and by comparing automatic segmentation to manual delineations in SPECT images. The Dice coefficient between automatic SPECT delineations and manual SPECT delineations were 0.83 ± 0.04% for the right and 0.82 ± 0.05% for the left lung. There was statistically significant difference between reference volumes from CT and automatic delineations for the right (R = 0.53, p = 0.02) and left lung (R = 0.69, p automatic quantification of wide range of measurements.

Adaptive geodesic transform for segmentation of vertebrae on CT images

Science.gov (United States)

Gaonkar, Bilwaj; Shu, Liao; Hermosillo, Gerardo; Zhan, Yiqiang

2014-03-01

Vertebral segmentation is a critical first step in any quantitative evaluation of vertebral pathology using CT images. This is especially challenging because bone marrow tissue has the same intensity profile as the muscle surrounding the bone. Thus simple methods such as thresholding or adaptive k-means fail to accurately segment vertebrae. While several other algorithms such as level sets may be used for segmentation any algorithm that is clinically deployable has to work in under a few seconds. To address these dual challenges we present here, a new algorithm based on the geodesic distance transform that is capable of segmenting the spinal vertebrae in under one second. To achieve this we extend the theory of the geodesic distance transforms proposed in1 to incorporate high level anatomical knowledge through adaptive weighting of image gradients. Such knowledge may be provided by the user directly or may be automatically generated by another algorithm. We incorporate information 'learnt' using a previously published machine learning algorithm2 to segment the L1 to L5 vertebrae. While we present a particular application here, the adaptive geodesic transform is a generic concept which can be applied to segmentation of other organs as well.

Segmentation-free empirical beam hardening correction for CT

Energy Technology Data Exchange (ETDEWEB)

Schüller, Sören; Sawall, Stefan [German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, Heidelberg 69120 (Germany); Stannigel, Kai; Hülsbusch, Markus; Ulrici, Johannes; Hell, Erich [Sirona Dental Systems GmbH, Fabrikstraße 31, 64625 Bensheim (Germany); Kachelrieß, Marc, E-mail: marc.kachelriess@dkfz.de [German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120 Heidelberg (Germany)

2015-02-15

Purpose: The polychromatic nature of the x-ray beams and their effects on the reconstructed image are often disregarded during standard image reconstruction. This leads to cupping and beam hardening artifacts inside the reconstructed volume. To correct for a general cupping, methods like water precorrection exist. They correct the hardening of the spectrum during the penetration of the measured object only for the major tissue class. In contrast, more complex artifacts like streaks between dense objects need other techniques of correction. If using only the information of one single energy scan, there are two types of corrections. The first one is a physical approach. Thereby, artifacts can be reproduced and corrected within the original reconstruction by using assumptions in a polychromatic forward projector. These assumptions could be the used spectrum, the detector response, the physical attenuation and scatter properties of the intersected materials. A second method is an empirical approach, which does not rely on much prior knowledge. This so-called empirical beam hardening correction (EBHC) and the previously mentioned physical-based technique are both relying on a segmentation of the present tissues inside the patient. The difficulty thereby is that beam hardening by itself, scatter, and other effects, which diminish the image quality also disturb the correct tissue classification and thereby reduce the accuracy of the two known classes of correction techniques. The herein proposed method works similar to the empirical beam hardening correction but does not require a tissue segmentation and therefore shows improvements on image data, which are highly degraded by noise and artifacts. Furthermore, the new algorithm is designed in a way that no additional calibration or parameter fitting is needed. Methods: To overcome the segmentation of tissues, the authors propose a histogram deformation of their primary reconstructed CT image. This step is essential for the

Segmentation-free empirical beam hardening correction for CT.

Science.gov (United States)

Schüller, Sören; Sawall, Stefan; Stannigel, Kai; Hülsbusch, Markus; Ulrici, Johannes; Hell, Erich; Kachelrieß, Marc

2015-02-01

The polychromatic nature of the x-ray beams and their effects on the reconstructed image are often disregarded during standard image reconstruction. This leads to cupping and beam hardening artifacts inside the reconstructed volume. To correct for a general cupping, methods like water precorrection exist. They correct the hardening of the spectrum during the penetration of the measured object only for the major tissue class. In contrast, more complex artifacts like streaks between dense objects need other techniques of correction. If using only the information of one single energy scan, there are two types of corrections. The first one is a physical approach. Thereby, artifacts can be reproduced and corrected within the original reconstruction by using assumptions in a polychromatic forward projector. These assumptions could be the used spectrum, the detector response, the physical attenuation and scatter properties of the intersected materials. A second method is an empirical approach, which does not rely on much prior knowledge. This so-called empirical beam hardening correction (EBHC) and the previously mentioned physical-based technique are both relying on a segmentation of the present tissues inside the patient. The difficulty thereby is that beam hardening by itself, scatter, and other effects, which diminish the image quality also disturb the correct tissue classification and thereby reduce the accuracy of the two known classes of correction techniques. The herein proposed method works similar to the empirical beam hardening correction but does not require a tissue segmentation and therefore shows improvements on image data, which are highly degraded by noise and artifacts. Furthermore, the new algorithm is designed in a way that no additional calibration or parameter fitting is needed. To overcome the segmentation of tissues, the authors propose a histogram deformation of their primary reconstructed CT image. This step is essential for the proposed

WE-G-207-05: Relationship Between CT Image Quality, Segmentation Performance, and Quantitative Image Feature Analysis

Energy Technology Data Exchange (ETDEWEB)

Lee, J; Nishikawa, R [University of Pittsburgh, Pittsburgh, PA (United States); Reiser, I [The University of Chicago, Chicago, IL (United States); Boone, J [UC Davis Medical Center, Sacramento, CA (United States)

2015-06-15

Purpose: Segmentation quality can affect quantitative image feature analysis. The objective of this study is to examine the relationship between computed tomography (CT) image quality, segmentation performance, and quantitative image feature analysis. Methods: A total of 90 pathology proven breast lesions in 87 dedicated breast CT images were considered. An iterative image reconstruction (IIR) algorithm was used to obtain CT images with different quality. With different combinations of 4 variables in the algorithm, this study obtained a total of 28 different qualities of CT images. Two imaging tasks/objectives were considered: 1) segmentation and 2) classification of the lesion as benign or malignant. Twenty-three image features were extracted after segmentation using a semi-automated algorithm and 5 of them were selected via a feature selection technique. Logistic regression was trained and tested using leave-one-out-cross-validation and its area under the ROC curve (AUC) was recorded. The standard deviation of a homogeneous portion and the gradient of a parenchymal portion of an example breast were used as an estimate of image noise and sharpness. The DICE coefficient was computed using a radiologist’s drawing on the lesion. Mean DICE and AUC were used as performance metrics for each of the 28 reconstructions. The relationship between segmentation and classification performance under different reconstructions were compared. Distributions (median, 95% confidence interval) of DICE and AUC for each reconstruction were also compared. Results: Moderate correlation (Pearson’s rho = 0.43, p-value = 0.02) between DICE and AUC values was found. However, the variation between DICE and AUC values for each reconstruction increased as the image sharpness increased. There was a combination of IIR parameters that resulted in the best segmentation with the worst classification performance. Conclusion: There are certain images that yield better segmentation or classification

Radiation exposure during transmission measurements: comparison between CT- and germanium-based techniques with a current PET scanner

International Nuclear Information System (INIS)

Wu, Tung-Hsin; Huang, Yung-Hui; Lee, Jason J.S.; Wang, Shih-Yuan; Wang, Su-Cheng; Su, Cheng-Tau; Chen, Liang-Kung; Chu, Tieh-Chi

2004-01-01

In positron emission tomographic (PET) scanning, transmission measurements for attenuation correction are commonly performed by using external germanium-68 rod sources. Recently, combined PET and computed tomographic (CT) scanners have been developed in which the CT data can be used for both anatomical-metabolic image formation and attenuation correction of the PET data. The purpose of this study was to evaluate the difference between germanium- and CT-based transmission scanning in terms of their radiation doses by using the same measurement technique and to compare the doses that patients receive during brain, cardiac and whole-body scans. Measurement of absorbed doses to organs was conducted by using a Rando Alderson phantom with thermoluminescent dosimeters. Effective doses were calculated according to the guidelines in the International Commission on Radiation Protection Publication Number 60. Compared with radionuclide doses used in routine 2-[fluorine-18]-fluoro-2-deoxy-d-glucose PET imaging, doses absorbed during germanium-based transmission scans were almost negligible. On the other hand, absorbed doses from CT-based transmission scans were significantly higher, particularly with a whole-body scanning protocol. Effective doses were 8.81 mSv in the high-speed mode and 18.97 mSv in the high-quality mode for whole-body CT-based transmission scans. These measurements revealed that the doses received by a patient during CT-based transmission scanning are more than those received in a typical PET examination. Therefore, the radiation doses represent a limitation to the generalised use of CT-based transmission measurements with current PET/CT scanner systems. (orig.)

3D segmentation of liver, kidneys and spleen from CT images

International Nuclear Information System (INIS)

Bekes, G.; Fidrich, M.; Nyul, L.G.; Mate, E.; Kuba, A.

2007-01-01

The clinicians often need to segment the abdominal organs for radiotherapy planning. Manual segmentation of these organs is very time-consuming, therefore automated methods are desired. We developed a semi-automatic segmentation method to outline liver, spleen and kidneys. It works on CT images without contrast intake that are acquired with a routine clinical protocol. From an initial surface around a user defined seed point, the segmentation of the organ is obtained by an active surface algorithm. Pre- and post-processing steps are used to adapt the general method for specific organs. The evaluation results show that the accuracy of our method is about 90%, which can be further improved with little manual editing, and that the precision is slightly higher than that of manual contouring. Our method is accurate, precise and fast enough to use in the clinical practice. (orig.)

Prostate CT segmentation method based on nonrigid registration in ultrasound-guided CT-based HDR prostate brachytherapy

Energy Technology Data Exchange (ETDEWEB)

Yang, Xiaofeng, E-mail: xyang43@emory.edu; Rossi, Peter; Ogunleye, Tomi; Marcus, David M.; Jani, Ashesh B.; Curran, Walter J.; Liu, Tian [Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, Georgia 30322 (United States); Mao, Hui [Department of Radiology and Imaging Sciences, Emory University, Atlanta, Georgia 30322 (United States)

2014-11-01

Purpose: The technological advances in real-time ultrasound image guidance for high-dose-rate (HDR) prostate brachytherapy have placed this treatment modality at the forefront of innovation in cancer radiotherapy. Prostate HDR treatment often involves placing the HDR catheters (needles) into the prostate gland under the transrectal ultrasound (TRUS) guidance, then generating a radiation treatment plan based on CT prostate images, and subsequently delivering high dose of radiation through these catheters. The main challenge for this HDR procedure is to accurately segment the prostate volume in the CT images for the radiation treatment planning. In this study, the authors propose a novel approach that integrates the prostate volume from 3D TRUS images into the treatment planning CT images to provide an accurate prostate delineation for prostate HDR treatment. Methods: The authors’ approach requires acquisition of 3D TRUS prostate images in the operating room right after the HDR catheters are inserted, which takes 1–3 min. These TRUS images are used to create prostate contours. The HDR catheters are reconstructed from the intraoperative TRUS and postoperative CT images, and subsequently used as landmarks for the TRUS–CT image fusion. After TRUS–CT fusion, the TRUS-based prostate volume is deformed to the CT images for treatment planning. This method was first validated with a prostate-phantom study. In addition, a pilot study of ten patients undergoing HDR prostate brachytherapy was conducted to test its clinical feasibility. The accuracy of their approach was assessed through the locations of three implanted fiducial (gold) markers, as well as T2-weighted MR prostate images of patients. Results: For the phantom study, the target registration error (TRE) of gold-markers was 0.41 ± 0.11 mm. For the ten patients, the TRE of gold markers was 1.18 ± 0.26 mm; the prostate volume difference between the authors’ approach and the MRI-based volume was 7.28% ± 0

Prostate CT segmentation method based on nonrigid registration in ultrasound-guided CT-based HDR prostate brachytherapy

Science.gov (United States)

Yang, Xiaofeng; Rossi, Peter; Ogunleye, Tomi; Marcus, David M.; Jani, Ashesh B.; Mao, Hui; Curran, Walter J.; Liu, Tian

2014-01-01

Purpose: The technological advances in real-time ultrasound image guidance for high-dose-rate (HDR) prostate brachytherapy have placed this treatment modality at the forefront of innovation in cancer radiotherapy. Prostate HDR treatment often involves placing the HDR catheters (needles) into the prostate gland under the transrectal ultrasound (TRUS) guidance, then generating a radiation treatment plan based on CT prostate images, and subsequently delivering high dose of radiation through these catheters. The main challenge for this HDR procedure is to accurately segment the prostate volume in the CT images for the radiation treatment planning. In this study, the authors propose a novel approach that integrates the prostate volume from 3D TRUS images into the treatment planning CT images to provide an accurate prostate delineation for prostate HDR treatment. Methods: The authors’ approach requires acquisition of 3D TRUS prostate images in the operating room right after the HDR catheters are inserted, which takes 1–3 min. These TRUS images are used to create prostate contours. The HDR catheters are reconstructed from the intraoperative TRUS and postoperative CT images, and subsequently used as landmarks for the TRUS–CT image fusion. After TRUS–CT fusion, the TRUS-based prostate volume is deformed to the CT images for treatment planning. This method was first validated with a prostate-phantom study. In addition, a pilot study of ten patients undergoing HDR prostate brachytherapy was conducted to test its clinical feasibility. The accuracy of their approach was assessed through the locations of three implanted fiducial (gold) markers, as well as T2-weighted MR prostate images of patients. Results: For the phantom study, the target registration error (TRE) of gold-markers was 0.41 ± 0.11 mm. For the ten patients, the TRE of gold markers was 1.18 ± 0.26 mm; the prostate volume difference between the authors’ approach and the MRI-based volume was 7.28% ± 0

Fully automatic segmentation of femurs with medullary canal definition in high and in low resolution CT scans.

Science.gov (United States)

Almeida, Diogo F; Ruben, Rui B; Folgado, João; Fernandes, Paulo R; Audenaert, Emmanuel; Verhegghe, Benedict; De Beule, Matthieu

2016-12-01

Femur segmentation can be an important tool in orthopedic surgical planning. However, in order to overcome the need of an experienced user with extensive knowledge on the techniques, segmentation should be fully automatic. In this paper a new fully automatic femur segmentation method for CT images is presented. This method is also able to define automatically the medullary canal and performs well even in low resolution CT scans. Fully automatic femoral segmentation was performed adapting a template mesh of the femoral volume to medical images. In order to achieve this, an adaptation of the active shape model (ASM) technique based on the statistical shape model (SSM) and local appearance model (LAM) of the femur with a novel initialization method was used, to drive the template mesh deformation in order to fit the in-image femoral shape in a time effective approach. With the proposed method a 98% convergence rate was achieved. For high resolution CT images group the average error is less than 1mm. For the low resolution image group the results are also accurate and the average error is less than 1.5mm. The proposed segmentation pipeline is accurate, robust and completely user free. The method is robust to patient orientation, image artifacts and poorly defined edges. The results excelled even in CT images with a significant slice thickness, i.e., above 5mm. Medullary canal segmentation increases the geometric information that can be used in orthopedic surgical planning or in finite element analysis. Copyright © 2016 IPEM. Published by Elsevier Ltd. All rights reserved.

Multiatlas whole heart segmentation of CT data using conditional entropy for atlas ranking and selection

Energy Technology Data Exchange (ETDEWEB)

Zhuang, Xiahai, E-mail: zhuangxiahai@sjtu.edu.cn; Qian, Xiaohua [SJTU-CU International Cooperative Research Center, Department of Engineering Mechanics, School of Naval Architecture Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai 200240 (China); Bai, Wenjia; Shi, Wenzhe; Rueckert, Daniel [Biomedical Image Analysis Group, Department of Computing, Imperial College London, 180 Queens Gate, London SW7 2AZ (United Kingdom); Song, Jingjing; Zhan, Songhua [Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai 201203 (China); Lian, Yanyun [Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai 201210 (China)

2015-07-15

Purpose: Cardiac computed tomography (CT) is widely used in clinical diagnosis of cardiovascular diseases. Whole heart segmentation (WHS) plays a vital role in developing new clinical applications of cardiac CT. However, the shape and appearance of the heart can vary greatly across different scans, making the automatic segmentation particularly challenging. The objective of this work is to develop and evaluate a multiatlas segmentation (MAS) scheme using a new atlas ranking and selection algorithm for automatic WHS of CT data. Research on different MAS strategies and their influence on WHS performance are limited. This work provides a detailed comparison study evaluating the impacts of label fusion, atlas ranking, and sizes of the atlas database on the segmentation performance. Methods: Atlases in a database were registered to the target image using a hierarchical registration scheme specifically designed for cardiac images. A subset of the atlases were selected for label fusion, according to the authors’ proposed atlas ranking criterion which evaluated the performance of each atlas by computing the conditional entropy of the target image given the propagated atlas labeling. Joint label fusion was used to combine multiple label estimates to obtain the final segmentation. The authors used 30 clinical cardiac CT angiography (CTA) images to evaluate the proposed MAS scheme and to investigate different segmentation strategies. Results: The mean WHS Dice score of the proposed MAS method was 0.918 ± 0.021, and the mean runtime for one case was 13.2 min on a workstation. This MAS scheme using joint label fusion generated significantly better Dice scores than the other label fusion strategies, including majority voting (0.901 ± 0.276, p < 0.01), locally weighted voting (0.905 ± 0.0247, p < 0.01), and probabilistic patch-based fusion (0.909 ± 0.0249, p < 0.01). In the atlas ranking study, the proposed criterion based on conditional entropy yielded a performance curve

Bias atlases for segmentation-based PET attenuation correction using PET-CT and MR.

Science.gov (United States)

Ouyang, Jinsong; Chun, Se Young; Petibon, Yoann; Bonab, Ali A; Alpert, Nathaniel; Fakhri, Georges El

2013-10-01

This study was to obtain voxel-wise PET accuracy and precision using tissue-segmentation for attenuation correction. We applied multiple thresholds to the CTs of 23 patients to classify tissues. For six of the 23 patients, MR images were also acquired. The MR fat/in-phase ratio images were used for fat segmentation. Segmented tissue classes were used to create attenuation maps, which were used for attenuation correction in PET reconstruction. PET bias images were then computed using the PET reconstructed with the original CT as the reference. We registered the CTs for all the patients and transformed the corresponding bias images accordingly. We then obtained the mean and standard deviation bias atlas using all the registered bias images. Our CT-based study shows that four-class segmentation (air, lungs, fat, other tissues), which is available on most PET-MR scanners, yields 15.1%, 4.1%, 6.6%, and 12.9% RMSE bias in lungs, fat, non-fat soft-tissues, and bones, respectively. An accurate fat identification is achievable using fat/in-phase MR images. Furthermore, we have found that three-class segmentation (air, lungs, other tissues) yields less than 5% standard deviation of bias within the heart, liver, and kidneys. This implies that three-class segmentation can be sufficient to achieve small variation of bias for imaging these three organs. Finally, we have found that inter- and intra-patient lung density variations contribute almost equally to the overall standard deviation of bias within the lungs.

Dense volumetric detection and segmentation of mediastinal lymph nodes in chest CT images

Science.gov (United States)

Oda, Hirohisa; Roth, Holger R.; Bhatia, Kanwal K.; Oda, Masahiro; Kitasaka, Takayuki; Iwano, Shingo; Homma, Hirotoshi; Takabatake, Hirotsugu; Mori, Masaki; Natori, Hiroshi; Schnabel, Julia A.; Mori, Kensaku

2018-02-01

We propose a novel mediastinal lymph node detection and segmentation method from chest CT volumes based on fully convolutional networks (FCNs). Most lymph node detection methods are based on filters for blob-like structures, which are not specific for lymph nodes. The 3D U-Net is a recent example of the state-of-the-art 3D FCNs. The 3D U-Net can be trained to learn appearances of lymph nodes in order to output lymph node likelihood maps on input CT volumes. However, it is prone to oversegmentation of each lymph node due to the strong data imbalance between lymph nodes and the remaining part of the CT volumes. To moderate the balance of sizes between the target classes, we train the 3D U-Net using not only lymph node annotations but also other anatomical structures (lungs, airways, aortic arches, and pulmonary arteries) that can be extracted robustly in an automated fashion. We applied the proposed method to 45 cases of contrast-enhanced chest CT volumes. Experimental results showed that 95.5% of lymph nodes were detected with 16.3 false positives per CT volume. The segmentation results showed that the proposed method can prevent oversegmentation, achieving an average Dice score of 52.3 +/- 23.1%, compared to the baseline method with 49.2 +/- 23.8%, respectively.

Abdominal multi-organ CT segmentation using organ correlation graph and prediction-based shape and location priors.

Science.gov (United States)

Okada, Toshiyuki; Linguraru, Marius George; Hori, Masatoshi; Summers, Ronald M; Tomiyama, Noriyuki; Sato, Yoshinobu

2013-01-01

The paper addresses the automated segmentation of multiple organs in upper abdominal CT data. We propose a framework of multi-organ segmentation which is adaptable to any imaging conditions without using intensity information in manually traced training data. The features of the framework are as follows: (1) the organ correlation graph (OCG) is introduced, which encodes the spatial correlations among organs inherent in human anatomy; (2) the patient-specific organ shape and location priors obtained using OCG enable the estimation of intensity priors from only target data and optionally a number of untraced CT data of the same imaging condition as the target data. The proposed methods were evaluated through segmentation of eight abdominal organs (liver, spleen, left and right kidney, pancreas, gallbladder, aorta, and inferior vena cava) from 86 CT data obtained by four imaging conditions at two hospitals. The performance was comparable to the state-of-the-art method using intensity priors constructed from manually traced data.

Body Composition Assessment in Axial CT Images Using FEM-Based Automatic Segmentation of Skeletal Muscle.

Science.gov (United States)

Popuri, Karteek; Cobzas, Dana; Esfandiari, Nina; Baracos, Vickie; Jägersand, Martin

2016-02-01

The proportions of muscle and fat tissues in the human body, referred to as body composition is a vital measurement for cancer patients. Body composition has been recently linked to patient survival and the onset/recurrence of several types of cancers in numerous cancer research studies. This paper introduces a fully automatic framework for the segmentation of muscle and fat tissues from CT images to estimate body composition. We developed a novel finite element method (FEM) deformable model that incorporates a priori shape information via a statistical deformation model (SDM) within the template-based segmentation framework. The proposed method was validated on 1000 abdominal and 530 thoracic CT images and we obtained very good segmentation results with Jaccard scores in excess of 90% for both the muscle and fat regions.

Construction of Korean male tomographic model segmented from PET-CT data

International Nuclear Information System (INIS)

Lee, Choon Sik; Park, Sang Kyun; Lee, Jai Ki

2004-01-01

Tomographic human models provide currently the most realistic representation of human anatomy for radiation dosimetry calculation. Most of the models have been constructed by using computed tomographic (CT) or magnetic resonance (MR) images obtained from a single individual. Each scan has its inherent advantages and disadvantages. CT scan gives a considerable radiation dose to a subject, and MR scan takes too long time to get clear images of an immobile subject. An emerging source of medical images for the construction of tomographic models is PET-CT, which is performed when looking for cancer. In this study, a tomographic model of Korean adult male was developed by processing whole-body CT images of a PET-CT-scanned healthy volunteer. The 343 slices of the CT images were semi-automatically segmented layer by layer using a graphic software and screen digitizer. The 3rd Korean tomographic model, named KRMAN-2, consisting of 300x150x344 voxels of a size of 2x2x5mm 3 , was constructed. Examples of application to Monte Carlo radiation dosimetry calculation in idealized whole-body irradiations were given and discussed

3D-SIFT-Flow for atlas-based CT liver image segmentation.

Science.gov (United States)

Xu, Yan; Xu, Chenchao; Kuang, Xiao; Wang, Hongkai; Chang, Eric I-Chao; Huang, Weimin; Fan, Yubo

2016-05-01

In this paper, the authors proposed a new 3D registration algorithm, 3D-scale invariant feature transform (SIFT)-Flow, for multiatlas-based liver segmentation in computed tomography (CT) images. In the registration work, the authors developed a new registration method that takes advantage of dense correspondence using the informative and robust SIFT feature. The authors computed the dense SIFT features for the source image and the target image and designed an objective function to obtain the correspondence between these two images. Labeling of the source image was then mapped to the target image according to the former correspondence, resulting in accurate segmentation. In the fusion work, the 2D-based nonparametric label transfer method was extended to 3D for fusing the registered 3D atlases. Compared with existing registration algorithms, 3D-SIFT-Flow has its particular advantage in matching anatomical structures (such as the liver) that observe large variation/deformation. The authors observed consistent improvement over widely adopted state-of-the-art registration methods such as ELASTIX, ANTS, and multiatlas fusion methods such as joint label fusion. Experimental results of liver segmentation on the MICCAI 2007 Grand Challenge are encouraging, e.g., Dice overlap ratio 96.27% ± 0.96% by our method compared with the previous state-of-the-art result of 94.90% ± 2.86%. Experimental results show that 3D-SIFT-Flow is robust for segmenting the liver from CT images, which has large tissue deformation and blurry boundary, and 3D label transfer is effective and efficient for improving the registration accuracy.

Inter-slice bidirectional registration-based segmentation of the prostate gland in MR and CT image sequences

Energy Technology Data Exchange (ETDEWEB)

Khalvati, Farzad, E-mail: farzad.khalvati@uwaterloo.ca; Tizhoosh, Hamid R. [Department of Systems Design Engineering, University of Waterloo, Waterloo, Ontario N2L 3G1 (Canada); Salmanpour, Aryan; Rahnamayan, Shahryar [Department of Engineering and Applied Science, University of Ontario Institute of Technology, Oshawa, Ontario L1H 7K4 (Canada); Rodrigues, George [Department of Radiation Oncology, London Regional Cancer Program, London, Ontario N6C 2R6, Canada and Department of Epidemiology/Biostatistics, University of Western Ontario, London, Ontario N6A 3K7 (Canada)

2013-12-15

Purpose: Accurate segmentation and volume estimation of the prostate gland in magnetic resonance (MR) and computed tomography (CT) images are necessary steps in diagnosis, treatment, and monitoring of prostate cancer. This paper presents an algorithm for the prostate gland volume estimation based on the semiautomated segmentation of individual slices in T2-weighted MR and CT image sequences. Methods: The proposedInter-Slice Bidirectional Registration-based Segmentation (iBRS) algorithm relies on interslice image registration of volume data to segment the prostate gland without the use of an anatomical atlas. It requires the user to mark only three slices in a given volume dataset, i.e., the first, middle, and last slices. Next, the proposed algorithm uses a registration algorithm to autosegment the remaining slices. We conducted comprehensive experiments to measure the performance of the proposed algorithm using three registration methods (i.e., rigid, affine, and nonrigid techniques). Results: The results with the proposed technique were compared with manual marking using prostate MR and CT images from 117 patients. Manual marking was performed by an expert user for all 117 patients. The median accuracies for individual slices measured using the Dice similarity coefficient (DSC) were 92% and 91% for MR and CT images, respectively. The iBRS algorithm was also evaluated regarding user variability, which confirmed that the algorithm was robust to interuser variability when marking the prostate gland. Conclusions: The proposed algorithm exploits the interslice data redundancy of the images in a volume dataset of MR and CT images and eliminates the need for an atlas, minimizing the computational cost while producing highly accurate results which are robust to interuser variability.

Inter-slice bidirectional registration-based segmentation of the prostate gland in MR and CT image sequences

International Nuclear Information System (INIS)

Khalvati, Farzad; Tizhoosh, Hamid R.; Salmanpour, Aryan; Rahnamayan, Shahryar; Rodrigues, George

2013-01-01

Purpose: Accurate segmentation and volume estimation of the prostate gland in magnetic resonance (MR) and computed tomography (CT) images are necessary steps in diagnosis, treatment, and monitoring of prostate cancer. This paper presents an algorithm for the prostate gland volume estimation based on the semiautomated segmentation of individual slices in T2-weighted MR and CT image sequences. Methods: The proposedInter-Slice Bidirectional Registration-based Segmentation (iBRS) algorithm relies on interslice image registration of volume data to segment the prostate gland without the use of an anatomical atlas. It requires the user to mark only three slices in a given volume dataset, i.e., the first, middle, and last slices. Next, the proposed algorithm uses a registration algorithm to autosegment the remaining slices. We conducted comprehensive experiments to measure the performance of the proposed algorithm using three registration methods (i.e., rigid, affine, and nonrigid techniques). Results: The results with the proposed technique were compared with manual marking using prostate MR and CT images from 117 patients. Manual marking was performed by an expert user for all 117 patients. The median accuracies for individual slices measured using the Dice similarity coefficient (DSC) were 92% and 91% for MR and CT images, respectively. The iBRS algorithm was also evaluated regarding user variability, which confirmed that the algorithm was robust to interuser variability when marking the prostate gland. Conclusions: The proposed algorithm exploits the interslice data redundancy of the images in a volume dataset of MR and CT images and eliminates the need for an atlas, minimizing the computational cost while producing highly accurate results which are robust to interuser variability

Comparison of human and automatic segmentations of kidneys from CT images

International Nuclear Information System (INIS)

Rao, Manjori; Stough, Joshua; Chi, Y.-Y.; Muller, Keith; Tracton, Gregg; Pizer, Stephen M.; Chaney, Edward L.

2005-01-01

Purpose: A controlled observer study was conducted to compare a method for automatic image segmentation with conventional user-guided segmentation of right and left kidneys from planning computerized tomographic (CT) images. Methods and materials: Deformable shape models called m-reps were used to automatically segment right and left kidneys from 12 target CT images, and the results were compared with careful manual segmentations performed by two human experts. M-rep models were trained based on manual segmentations from a collection of images that did not include the targets. Segmentation using m-reps began with interactive initialization to position the kidney model over the target kidney in the image data. Fully automatic segmentation proceeded through two stages at successively smaller spatial scales. At the first stage, a global similarity transformation of the kidney model was computed to position the model closer to the target kidney. The similarity transformation was followed by large-scale deformations based on principal geodesic analysis (PGA). During the second stage, the medial atoms comprising the m-rep model were deformed one by one. This procedure was iterated until no changes were observed. The transformations and deformations at both stages were driven by optimizing an objective function with two terms. One term penalized the currently deformed m-rep by an amount proportional to its deviation from the mean m-rep derived from PGA of the training segmentations. The second term computed a model-to-image match term based on the goodness of match of the trained intensity template for the currently deformed m-rep with the corresponding intensity data in the target image. Human and m-rep segmentations were compared using quantitative metrics provided in a toolset called Valmet. Metrics reported in this article include (1) percent volume overlap; (2) mean surface distance between two segmentations; and (3) maximum surface separation (Hausdorff distance

Intra-patient semi-automated segmentation of the cervix-uterus in CT-images for adaptive radiotherapy of cervical cancer

Science.gov (United States)

Luiza Bondar, M.; Hoogeman, Mischa; Schillemans, Wilco; Heijmen, Ben

2013-08-01

For online adaptive radiotherapy of cervical cancer, fast and accurate image segmentation is required to facilitate daily treatment adaptation. Our aim was twofold: (1) to test and compare three intra-patient automated segmentation methods for the cervix-uterus structure in CT-images and (2) to improve the segmentation accuracy by including prior knowledge on the daily bladder volume or on the daily coordinates of implanted fiducial markers. The tested methods were: shape deformation (SD) and atlas-based segmentation (ABAS) using two non-rigid registration methods: demons and a hierarchical algorithm. Tests on 102 CT-scans of 13 patients demonstrated that the segmentation accuracy significantly increased by including the bladder volume predicted with a simple 1D model based on a manually defined bladder top. Moreover, manually identified implanted fiducial markers significantly improved the accuracy of the SD method. For patients with large cervix-uterus volume regression, the use of CT-data acquired toward the end of the treatment was required to improve segmentation accuracy. Including prior knowledge, the segmentation results of SD (Dice similarity coefficient 85 ± 6%, error margin 2.2 ± 2.3 mm, average time around 1 min) and of ABAS using hierarchical non-rigid registration (Dice 82 ± 10%, error margin 3.1 ± 2.3 mm, average time around 30 s) support their use for image guided online adaptive radiotherapy of cervical cancer.

Segmental Adenomyomatosis of Gallbladder: CT Assessment of the Patterns of Cholecystolithiasis

International Nuclear Information System (INIS)

Yoo, Yeon Hwa; Yu, Jeong Sik; Chung, Jae Joon; Kim, Joo Hee; Cho, Eun Suk; Kim, Dae Jung; Ahn, Jhii Hyun; Kim, Ki Whang

2011-01-01

To clarify the relationship between the pattern of cholecystolithiasis and the gross features of segmental adenomyomatosis of the gallbladder. Fifty-five consecutive patients with segmental adenomyomatosis with calcified gallbladder stones defined on CT were retrospectively analyzed in terms of (i) stone location (fundal vs. neck compartment) and (ii) size of the largest stone as a function of the extent of segmental mural thickening (type A, limited at the narrow segment; type B, partially extended in the fundal direction; type C, involving the entire fundal compartment). The extent of segmental mural thickening in patients with cholecystolithiasis was compared with a control group (n = 48) lacking stones. Stones were found more frequently in the fundal compartment in 48 patients compared to the neck compartment in 12 patients (p<0.001). The mean size of the largest stone in type C (5.4 ± 4.9 mm) was larger than in type A (2.3 ± 2.2 mm) (p=0.033). In patients with cholecystolithiasis, type C segmental thickening was predominant (69%) compared to the control group (42%) (p=0.012). In addition to a higher prevalence of stones, a wide extent of mural thickening combined with large stone size in the fundal compartment suggests the contribution of segmental adenomyomatosis to stone formation and chronic inflammation.

Photosynthesis-related infrared light transmission changes in spinach leaf segments

International Nuclear Information System (INIS)

Akimoto, T.

1985-01-01

The time courses of infrared light transmission changes and fluorescence induced by light in spinach leaf segments were measured. The illumination by red light exhibited a complex wave pattern. The transmission approached the baseline after repeating decreases and increases. Illumination by far-red light decreased the transmission. One of the differences between the two responses was the difference between the two amplitudes of the first increasing component. The component in the red light response was larger than the component in the far-red light response. The transmission decrease by far-red light is supposed to correspond to ''red drop.'' The transmission decrease by far-red light was suppressed by red light. This is due to an activation of a transmission-increasing component. This probably corresponds to ''enhancement.'' A proportional correlation existed between the intensity of far-red light and the minimum intensity of red light that suppressed the transmission decrease induced by far-red light. The component which made Peak D in the time course of fluorescence yield and the first increasing component in the transmission changes were suppressed by intense light

Segmenting CT prostate images using population and patient-specific statistics for radiotherapy

International Nuclear Information System (INIS)

Feng, Qianjin; Foskey, Mark; Chen Wufan; Shen Dinggang

2010-01-01

Purpose: In the segmentation of sequential treatment-time CT prostate images acquired in image-guided radiotherapy, accurately capturing the intrapatient variation of the patient under therapy is more important than capturing interpatient variation. However, using the traditional deformable-model-based segmentation methods, it is difficult to capture intrapatient variation when the number of samples from the same patient is limited. This article presents a new deformable model, designed specifically for segmenting sequential CT images of the prostate, which leverages both population and patient-specific statistics to accurately capture the intrapatient variation of the patient under therapy. Methods: The novelty of the proposed method is twofold: First, a weighted combination of gradient and probability distribution function (PDF) features is used to build the appearance model to guide model deformation. The strengths of each feature type are emphasized by dynamically adjusting the weight between the profile-based gradient features and the local-region-based PDF features during the optimization process. An additional novel aspect of the gradient-based features is that, to alleviate the effect of feature inconsistency in the regions of gas and bone adjacent to the prostate, the optimal profile length at each landmark is calculated by statistically investigating the intensity profile in the training set. The resulting gradient-PDF combined feature produces more accurate and robust segmentations than general gradient features. Second, an online learning mechanism is used to build shape and appearance statistics for accurately capturing intrapatient variation. Results: The performance of the proposed method was evaluated on 306 images of the 24 patients. Compared to traditional gradient features, the proposed gradient-PDF combination features brought 5.2% increment in the success ratio of segmentation (from 94.1% to 99.3%). To evaluate the effectiveness of online

Segmenting CT prostate images using population and patient-specific statistics for radiotherapy

Energy Technology Data Exchange (ETDEWEB)

Feng, Qianjin; Foskey, Mark; Chen Wufan; Shen Dinggang [Biomedical Engineering College, South Medical University, Guangzhou (China) and Department of Radiology, University of North Carolina, Chapel Hill, North Carolina 27510 (United States); Department of Radiation Oncology, University of North Carolina, Chapel Hill, North Carolina 27599 (United States); Biomedical Engineering College, South Medical University, Guangzhou 510510 (China); Department of Radiology, University of North Carolina, Chapel Hill, North Carolina 27510 (United States)

2010-08-15

Purpose: In the segmentation of sequential treatment-time CT prostate images acquired in image-guided radiotherapy, accurately capturing the intrapatient variation of the patient under therapy is more important than capturing interpatient variation. However, using the traditional deformable-model-based segmentation methods, it is difficult to capture intrapatient variation when the number of samples from the same patient is limited. This article presents a new deformable model, designed specifically for segmenting sequential CT images of the prostate, which leverages both population and patient-specific statistics to accurately capture the intrapatient variation of the patient under therapy. Methods: The novelty of the proposed method is twofold: First, a weighted combination of gradient and probability distribution function (PDF) features is used to build the appearance model to guide model deformation. The strengths of each feature type are emphasized by dynamically adjusting the weight between the profile-based gradient features and the local-region-based PDF features during the optimization process. An additional novel aspect of the gradient-based features is that, to alleviate the effect of feature inconsistency in the regions of gas and bone adjacent to the prostate, the optimal profile length at each landmark is calculated by statistically investigating the intensity profile in the training set. The resulting gradient-PDF combined feature produces more accurate and robust segmentations than general gradient features. Second, an online learning mechanism is used to build shape and appearance statistics for accurately capturing intrapatient variation. Results: The performance of the proposed method was evaluated on 306 images of the 24 patients. Compared to traditional gradient features, the proposed gradient-PDF combination features brought 5.2% increment in the success ratio of segmentation (from 94.1% to 99.3%). To evaluate the effectiveness of online

Intrathoracic Airway Tree Segmentation from CT Images Using a Fuzzy Connectivity Method

Directory of Open Access Journals (Sweden)

Fereshteh Yousefi Rizi

2009-03-01

Full Text Available Introduction: Virtual bronchoscopy is a reliable and efficient diagnostic method for primary symptoms of lung cancer. The segmentation of airways from CT images is a critical step for numerous virtual bronchoscopy applications. Materials and Methods: To overcome the limitations of the fuzzy connectedness method, the proposed technique, called fuzzy connectivity - fuzzy C-mean (FC-FCM, utilized the FCM algorithm. Then, hanging-togetherness of pixels was handled by employing a spatial membership function. Another problem in airway segmentation that had to be overcome was the leakage into the extra-luminal regions due to the thinness of the airway walls during the process of segmentation. Results:   The result shows an accuracy of 92.92% obtained for segmentation of the airway tree up to the fourth generation. Conclusion:  We have presented a new segmentation method that is not only robust regarding the leakage problem but also functions more efficiently than the traditional FC method.

Segmentation of teeth in CT volumetric dataset by panoramic projection and variational level set

Energy Technology Data Exchange (ETDEWEB)

Hosntalab, Mohammad [Islamic Azad University, Faculty of Engineering, Science and Research Branch, Tehran (Iran); Aghaeizadeh Zoroofi, Reza [University of Tehran, Control and Intelligent Processing Center of Excellence, School of Electrical and Computer Engineering, College of Engineering, Tehran (Iran); Abbaspour Tehrani-Fard, Ali [Islamic Azad University, Faculty of Engineering, Science and Research Branch, Tehran (Iran); Sharif University of Technology, Department of Electrical Engineering, Tehran (Iran); Shirani, Gholamreza [Faculty of Dentistry Medical Science of Tehran University, Oral and Maxillofacial Surgery Department, Tehran (Iran)

2008-09-15

Quantification of teeth is of clinical importance for various computer assisted procedures such as dental implant, orthodontic planning, face, jaw and cosmetic surgeries. In this regard, segmentation is a major step. In this paper, we propose a method for segmentation of teeth in volumetric computed tomography (CT) data using panoramic re-sampling of the dataset in the coronal view and variational level set. The proposed method consists of five steps as follows: first, we extract a mask in a CT images using Otsu thresholding. Second, the teeth are segmented from other bony tissues by utilizing anatomical knowledge of teeth in the jaws. Third, the proposed method is followed by estimating the arc of the upper and lower jaws and panoramic re-sampling of the dataset. Separation of upper and lower jaws and initial segmentation of teeth are performed by employing the horizontal and vertical projections of the panoramic dataset, respectively. Based the above mentioned procedures an initial mask for each tooth is obtained. Finally, we utilize the initial mask of teeth and apply a Variational level set to refine initial teeth boundaries to final contours. The proposed algorithm was evaluated in the presence of 30 multi-slice CT datasets including 3,600 images. Experimental results reveal the effectiveness of the proposed method. In the proposed algorithm, the variational level set technique was utilized to trace the contour of the teeth. In view of the fact that, this technique is based on the characteristic of the overall region of the teeth image, it is possible to extract a very smooth and accurate tooth contour using this technique. In the presence of the available datasets, the proposed technique was successful in teeth segmentation compared to previous techniques. (orig.)

Segmentation of teeth in CT volumetric dataset by panoramic projection and variational level set

International Nuclear Information System (INIS)

Hosntalab, Mohammad; Aghaeizadeh Zoroofi, Reza; Abbaspour Tehrani-Fard, Ali; Shirani, Gholamreza

2008-01-01

Quantification of teeth is of clinical importance for various computer assisted procedures such as dental implant, orthodontic planning, face, jaw and cosmetic surgeries. In this regard, segmentation is a major step. In this paper, we propose a method for segmentation of teeth in volumetric computed tomography (CT) data using panoramic re-sampling of the dataset in the coronal view and variational level set. The proposed method consists of five steps as follows: first, we extract a mask in a CT images using Otsu thresholding. Second, the teeth are segmented from other bony tissues by utilizing anatomical knowledge of teeth in the jaws. Third, the proposed method is followed by estimating the arc of the upper and lower jaws and panoramic re-sampling of the dataset. Separation of upper and lower jaws and initial segmentation of teeth are performed by employing the horizontal and vertical projections of the panoramic dataset, respectively. Based the above mentioned procedures an initial mask for each tooth is obtained. Finally, we utilize the initial mask of teeth and apply a Variational level set to refine initial teeth boundaries to final contours. The proposed algorithm was evaluated in the presence of 30 multi-slice CT datasets including 3,600 images. Experimental results reveal the effectiveness of the proposed method. In the proposed algorithm, the variational level set technique was utilized to trace the contour of the teeth. In view of the fact that, this technique is based on the characteristic of the overall region of the teeth image, it is possible to extract a very smooth and accurate tooth contour using this technique. In the presence of the available datasets, the proposed technique was successful in teeth segmentation compared to previous techniques. (orig.)

Interleaved 3D-CNNs for joint segmentation of small-volume structures in head and neck CT images.

Science.gov (United States)

Ren, Xuhua; Xiang, Lei; Nie, Dong; Shao, Yeqin; Zhang, Huan; Shen, Dinggang; Wang, Qian

2018-02-26

Accurate 3D image segmentation is a crucial step in radiation therapy planning of head and neck tumors. These segmentation results are currently obtained by manual outlining of tissues, which is a tedious and time-consuming procedure. Automatic segmentation provides an alternative solution, which, however, is often difficult for small tissues (i.e., chiasm and optic nerves in head and neck CT images) because of their small volumes and highly diverse appearance/shape information. In this work, we propose to interleave multiple 3D Convolutional Neural Networks (3D-CNNs) to attain automatic segmentation of small tissues in head and neck CT images. A 3D-CNN was designed to segment each structure of interest. To make full use of the image appearance information, multiscale patches are extracted to describe the center voxel under consideration and then input to the CNN architecture. Next, as neighboring tissues are often highly related in the physiological and anatomical perspectives, we interleave the CNNs designated for the individual tissues. In this way, the tentative segmentation result of a specific tissue can contribute to refine the segmentations of other neighboring tissues. Finally, as more CNNs are interleaved and cascaded, a complex network of CNNs can be derived, such that all tissues can be jointly segmented and iteratively refined. Our method was validated on a set of 48 CT images, obtained from the Medical Image Computing and Computer Assisted Intervention (MICCAI) Challenge 2015. The Dice coefficient (DC) and the 95% Hausdorff Distance (95HD) are computed to measure the accuracy of the segmentation results. The proposed method achieves higher segmentation accuracy (with the average DC: 0.58 ± 0.17 for optic chiasm, and 0.71 ± 0.08 for optic nerve; 95HD: 2.81 ± 1.56 mm for optic chiasm, and 2.23 ± 0.90 mm for optic nerve) than the MICCAI challenge winner (with the average DC: 0.38 for optic chiasm, and 0.68 for optic nerve; 95HD: 3.48 for

3D-SIFT-Flow for atlas-based CT liver image segmentation

Energy Technology Data Exchange (ETDEWEB)

Xu, Yan, E-mail: xuyan04@gmail.com [State Key Laboratory of Software Development Environment and Key Laboratory of Biomechanics and Mechanobiology of Ministry of Education, Beihang University, Beijing 100191, China and Research Institute of Beihang University in Shenzhen and Microsoft Research, Beijing 100080 (China); Xu, Chenchao, E-mail: chenchaoxu33@gmail.com; Kuang, Xiao, E-mail: kuangxiao.ace@gmail.com [School of Biological Science and Medical Engineering, Beihang University, Beijing 100191 (China); Wang, Hongkai, E-mail: wang.hongkai@gmail.com [Department of Biomedical Engineering, Dalian University of Technology, Dalian 116024 (China); Chang, Eric I-Chao, E-mail: eric.chang@microsoft.com [Microsoft Research, Beijing 100080 (China); Huang, Weimin, E-mail: wmhuang@i2r.a-star.edu.sg [Institute for Infocomm Research (I2R), Singapore 138632 (Singapore); Fan, Yubo, E-mail: yubofan@buaa.edu.cn [Key Laboratory of Biomechanics and Mechanobiology of Ministry of Education, Beihang University, Beijing 100191 (China)

2016-05-15

Purpose: In this paper, the authors proposed a new 3D registration algorithm, 3D-scale invariant feature transform (SIFT)-Flow, for multiatlas-based liver segmentation in computed tomography (CT) images. Methods: In the registration work, the authors developed a new registration method that takes advantage of dense correspondence using the informative and robust SIFT feature. The authors computed the dense SIFT features for the source image and the target image and designed an objective function to obtain the correspondence between these two images. Labeling of the source image was then mapped to the target image according to the former correspondence, resulting in accurate segmentation. In the fusion work, the 2D-based nonparametric label transfer method was extended to 3D for fusing the registered 3D atlases. Results: Compared with existing registration algorithms, 3D-SIFT-Flow has its particular advantage in matching anatomical structures (such as the liver) that observe large variation/deformation. The authors observed consistent improvement over widely adopted state-of-the-art registration methods such as ELASTIX, ANTS, and multiatlas fusion methods such as joint label fusion. Experimental results of liver segmentation on the MICCAI 2007 Grand Challenge are encouraging, e.g., Dice overlap ratio 96.27% ± 0.96% by our method compared with the previous state-of-the-art result of 94.90% ± 2.86%. Conclusions: Experimental results show that 3D-SIFT-Flow is robust for segmenting the liver from CT images, which has large tissue deformation and blurry boundary, and 3D label transfer is effective and efficient for improving the registration accuracy.

3D-SIFT-Flow for atlas-based CT liver image segmentation

International Nuclear Information System (INIS)

Xu, Yan; Xu, Chenchao; Kuang, Xiao; Wang, Hongkai; Chang, Eric I-Chao; Huang, Weimin; Fan, Yubo

2016-01-01

Purpose: In this paper, the authors proposed a new 3D registration algorithm, 3D-scale invariant feature transform (SIFT)-Flow, for multiatlas-based liver segmentation in computed tomography (CT) images. Methods: In the registration work, the authors developed a new registration method that takes advantage of dense correspondence using the informative and robust SIFT feature. The authors computed the dense SIFT features for the source image and the target image and designed an objective function to obtain the correspondence between these two images. Labeling of the source image was then mapped to the target image according to the former correspondence, resulting in accurate segmentation. In the fusion work, the 2D-based nonparametric label transfer method was extended to 3D for fusing the registered 3D atlases. Results: Compared with existing registration algorithms, 3D-SIFT-Flow has its particular advantage in matching anatomical structures (such as the liver) that observe large variation/deformation. The authors observed consistent improvement over widely adopted state-of-the-art registration methods such as ELASTIX, ANTS, and multiatlas fusion methods such as joint label fusion. Experimental results of liver segmentation on the MICCAI 2007 Grand Challenge are encouraging, e.g., Dice overlap ratio 96.27% ± 0.96% by our method compared with the previous state-of-the-art result of 94.90% ± 2.86%. Conclusions: Experimental results show that 3D-SIFT-Flow is robust for segmenting the liver from CT images, which has large tissue deformation and blurry boundary, and 3D label transfer is effective and efficient for improving the registration accuracy.

Tracking fuzzy borders using geodesic curves with application to liver segmentation on planning CT

International Nuclear Information System (INIS)

Yuan, Yading; Chao, Ming; Sheu, Ren-Dih; Rosenzweig, Kenneth; Lo, Yeh-Chi

2015-01-01

Purpose: This work aims to develop a robust and efficient method to track the fuzzy borders between liver and the abutted organs where automatic liver segmentation usually suffers, and to investigate its applications in automatic liver segmentation on noncontrast-enhanced planning computed tomography (CT) images. Methods: In order to track the fuzzy liver–chestwall and liver–heart borders where oversegmentation is often found, a starting point and an ending point were first identified on the coronal view images; the fuzzy border was then determined as a geodesic curve constructed by minimizing the gradient-weighted path length between these two points near the fuzzy border. The minimization of path length was numerically solved by fast-marching method. The resultant fuzzy borders were incorporated into the authors’ automatic segmentation scheme, in which the liver was initially estimated by a patient-specific adaptive thresholding and then refined by a geodesic active contour model. By using planning CT images of 15 liver patients treated with stereotactic body radiation therapy, the liver contours extracted by the proposed computerized scheme were compared with those manually delineated by a radiation oncologist. Results: The proposed automatic liver segmentation method yielded an average Dice similarity coefficient of 0.930 ± 0.015, whereas it was 0.912 ± 0.020 if the fuzzy border tracking was not used. The application of fuzzy border tracking was found to significantly improve the segmentation performance. The mean liver volume obtained by the proposed method was 1727 cm 3 , whereas it was 1719 cm 3 for manual-outlined volumes. The computer-generated liver volumes achieved excellent agreement with manual-outlined volumes with correlation coefficient of 0.98. Conclusions: The proposed method was shown to provide accurate segmentation for liver in the planning CT images where contrast agent is not applied. The authors’ results also clearly demonstrated

Segmentation of organs at risk in CT volumes of head, thorax, abdomen, and pelvis

Science.gov (United States)

Han, Miaofei; Ma, Jinfeng; Li, Yan; Li, Meiling; Song, Yanli; Li, Qiang

2015-03-01

Accurate segmentation of organs at risk (OARs) is a key step in treatment planning system (TPS) of image guided radiation therapy. We are developing three classes of methods to segment 17 organs at risk throughout the whole body, including brain, brain stem, eyes, mandible, temporomandibular joints, parotid glands, spinal cord, lungs, trachea, heart, livers, kidneys, spleen, prostate, rectum, femoral heads, and skin. The three classes of segmentation methods include (1) threshold-based methods for organs of large contrast with adjacent structures such as lungs, trachea, and skin; (2) context-driven Generalized Hough Transform-based methods combined with graph cut algorithm for robust localization and segmentation of liver, kidneys and spleen; and (3) atlas and registration-based methods for segmentation of heart and all organs in CT volumes of head and pelvis. The segmentation accuracy for the seventeen organs was subjectively evaluated by two medical experts in three levels of score: 0, poor (unusable in clinical practice); 1, acceptable (minor revision needed); and 2, good (nearly no revision needed). A database was collected from Ruijin Hospital, Huashan Hospital, and Xuhui Central Hospital in Shanghai, China, including 127 head scans, 203 thoracic scans, 154 abdominal scans, and 73 pelvic scans. The percentages of "good" segmentation results were 97.6%, 92.9%, 81.1%, 87.4%, 85.0%, 78.7%, 94.1%, 91.1%, 81.3%, 86.7%, 82.5%, 86.4%, 79.9%, 72.6%, 68.5%, 93.2%, 96.9% for brain, brain stem, eyes, mandible, temporomandibular joints, parotid glands, spinal cord, lungs, trachea, heart, livers, kidneys, spleen, prostate, rectum, femoral heads, and skin, respectively. Various organs at risk can be reliably segmented from CT scans by use of the three classes of segmentation methods.

Random walks with statistical shape prior for cochlea and inner ear segmentation in micro-CT images

DEFF Research Database (Denmark)

Ruiz Pujadas, Esmeralda; Piella, Gemma; Kjer, Hans Martin

2017-01-01

A cochlear implant is an electronic device which can restore sound to completely or partially deaf patients. For surgical planning, a patient-specific model of the inner ear must be built using high-resolution images accurately segmented. We propose a new framework for segmentation of micro-CT...

Automated 3D closed surface segmentation: application to vertebral body segmentation in CT images.

Science.gov (United States)

Liu, Shuang; Xie, Yiting; Reeves, Anthony P

2016-05-01

A fully automated segmentation algorithm, progressive surface resolution (PSR), is presented in this paper to determine the closed surface of approximately convex blob-like structures that are common in biomedical imaging. The PSR algorithm was applied to the cortical surface segmentation of 460 vertebral bodies on 46 low-dose chest CT images, which can be potentially used for automated bone mineral density measurement and compression fracture detection. The target surface is realized by a closed triangular mesh, which thereby guarantees the enclosure. The surface vertices of the triangular mesh representation are constrained along radial trajectories that are uniformly distributed in 3D angle space. The segmentation is accomplished by determining for each radial trajectory the location of its intersection with the target surface. The surface is first initialized based on an input high confidence boundary image and then resolved progressively based on a dynamic attraction map in an order of decreasing degree of evidence regarding the target surface location. For the visual evaluation, the algorithm achieved acceptable segmentation for 99.35 % vertebral bodies. Quantitative evaluation was performed on 46 vertebral bodies and achieved overall mean Dice coefficient of 0.939 (with max [Formula: see text] 0.957, min [Formula: see text] 0.906 and standard deviation [Formula: see text] 0.011) using manual annotations as the ground truth. Both visual and quantitative evaluations demonstrate encouraging performance of the PSR algorithm. This novel surface resolution strategy provides uniform angular resolution for the segmented surface with computation complexity and runtime that are linearly constrained by the total number of vertices of the triangular mesh representation.

Random walks with shape prior for cochlea segmentation in ex vivo μCT

DEFF Research Database (Denmark)

Ruiz Pujadas, Esmeralda; Kjer, Hans Martin; Piella, Gemma

2016-01-01

Purpose Cochlear implantation is a safe and effective surgical procedure to restore hearing in deaf patients. However, the level of restoration achieved may vary due to differences in anatomy, implant type and surgical access. In order to reduce the variability of the surgical outcomes, we...... propose a new framework for cochlea segmentation in ex vivo μCT images using random walks where a distance-based shape prior is combined with a region term estimated by a Gaussian mixture model. The prior is also weighted by a confidence map to adjust its influence according to the strength of the image...... contour. Random walks is performed iteratively, and the prior mask is aligned in every iteration. Results We tested the proposed approach in ten μCT data sets and compared it with other random walks-based segmentation techniques such as guided random walks (Eslami et al. in Med Image Anal 17...

Automated segmentation and recognition of abdominal wall muscles in X-ray torso CT images and its application in abdominal CAD

International Nuclear Information System (INIS)

Zhou, X.; Kamiya, N.; Hara, T.; Fujita, H.; Chen, H.; Yokoyama, R.; Hoshi, H.

2007-01-01

The information of abdominal wall is very important for the planning of surgical operation and abdominal organ recognition. In research fields of computer assisted radiology and surgery and computer-aided diagnosis, the segmentation and recognition of the abdominal wall muscles in CT images is a necessary pre-processing step. Due to the complexity of the abdominal wall structure and indistinctive in CT images, the automated segmentation of abdominal wall muscles is a difficult issue and has not been solved completely. We propose an approach to segment the abdominal wall muscles and divide it into three categories (front abdominal muscles including rectus abdominis; left and right side abdominal muscles including external oblique, internal oblique and transversus abdominis muscles) automatically. The approach, first, makes an initial classification of bone, fat, and muscles and organs based on the CT number. Then a layer structure is generated to describe the 3-D anatomical structures of human torso by stretching the torso region onto a thin-plate for easy recognition. The abdominal wall muscles are recognized on the layer structures using the spatial relations to the skeletal structure and CT numbers. Finally, the recognized regions are mapped back to the 3-D CT images using an inverse transformation of the stretching process. This method is applied to 20 cases of torso CT images and evaluations are based on visual comparison of the recognition results and the original CT images by an expert in anatomy. The results show that our approach can segment and recognize abdominal wall muscle regions effectively. (orig.)

Comparison of automatic and visual methods used for image segmentation in Endodontics: a microCT study.

Science.gov (United States)

Queiroz, Polyane Mazucatto; Rovaris, Karla; Santaella, Gustavo Machado; Haiter-Neto, Francisco; Freitas, Deborah Queiroz

2017-01-01

To calculate root canal volume and surface area in microCT images, an image segmentation by selecting threshold values is required, which can be determined by visual or automatic methods. Visual determination is influenced by the operator's visual acuity, while the automatic method is done entirely by computer algorithms. To compare between visual and automatic segmentation, and to determine the influence of the operator's visual acuity on the reproducibility of root canal volume and area measurements. Images from 31 extracted human anterior teeth were scanned with a μCT scanner. Three experienced examiners performed visual image segmentation, and threshold values were recorded. Automatic segmentation was done using the "Automatic Threshold Tool" available in the dedicated software provided by the scanner's manufacturer. Volume and area measurements were performed using the threshold values determined both visually and automatically. The paired Student's t-test showed no significant difference between visual and automatic segmentation methods regarding root canal volume measurements (p=0.93) and root canal surface (p=0.79). Although visual and automatic segmentation methods can be used to determine the threshold and calculate root canal volume and surface, the automatic method may be the most suitable for ensuring the reproducibility of threshold determination.

Pulmonary parenchyma segmentation in thin CT image sequences with spectral clustering and geodesic active contour model based on similarity

Science.gov (United States)

He, Nana; Zhang, Xiaolong; Zhao, Juanjuan; Zhao, Huilan; Qiang, Yan

2017-07-01

While the popular thin layer scanning technology of spiral CT has helped to improve diagnoses of lung diseases, the large volumes of scanning images produced by the technology also dramatically increase the load of physicians in lesion detection. Computer-aided diagnosis techniques like lesions segmentation in thin CT sequences have been developed to address this issue, but it remains a challenge to achieve high segmentation efficiency and accuracy without much involvement of human manual intervention. In this paper, we present our research on automated segmentation of lung parenchyma with an improved geodesic active contour model that is geodesic active contour model based on similarity (GACBS). Combining spectral clustering algorithm based on Nystrom (SCN) with GACBS, this algorithm first extracts key image slices, then uses these slices to generate an initial contour of pulmonary parenchyma of un-segmented slices with an interpolation algorithm, and finally segments lung parenchyma of un-segmented slices. Experimental results show that the segmentation results generated by our method are close to what manual segmentation can produce, with an average volume overlap ratio of 91.48%.

Automated CT-based segmentation and quantification of total intracranial volume

Energy Technology Data Exchange (ETDEWEB)

Aguilar, Carlos; Wahlund, Lars-Olof; Westman, Eric [Karolinska Institute, Department of Neurobiology, Care Sciences and Society (NVS), Division of Clinical Geriatrics, Stockholm (Sweden); Edholm, Kaijsa; Cavallin, Lena; Muller, Susanne; Axelsson, Rimma [Karolinska Institute, Department of Clinical Science, Intervention and Technology, Division of Medical Imaging and Technology, Stockholm (Sweden); Karolinska University Hospital in Huddinge, Department of Radiology, Stockholm (Sweden); Simmons, Andrew [King' s College London, Institute of Psychiatry, London (United Kingdom); NIHR Biomedical Research Centre for Mental Health and Biomedical Research Unit for Dementia, London (United Kingdom); Skoog, Ingmar [Gothenburg University, Department of Psychiatry and Neurochemistry, The Sahlgrenska Academy, Gothenburg (Sweden); Larsson, Elna-Marie [Uppsala University, Department of Surgical Sciences, Radiology, Akademiska Sjukhuset, Uppsala (Sweden)

2015-11-15

To develop an algorithm to segment and obtain an estimate of total intracranial volume (tICV) from computed tomography (CT) images. Thirty-six CT examinations from 18 patients were included. Ten patients were examined twice the same day and eight patients twice six months apart (these patients also underwent MRI). The algorithm combines morphological operations, intensity thresholding and mixture modelling. The method was validated against manual delineation and its robustness assessed from repeated imaging examinations. Using automated MRI software, the comparability with MRI was investigated. Volumes were compared based on average relative volume differences and their magnitudes; agreement was shown by a Bland-Altman analysis graph. We observed good agreement between our algorithm and manual delineation of a trained radiologist: the Pearson's correlation coefficient was r = 0.94, tICVml[manual] = 1.05 x tICVml[automated] - 33.78 (R{sup 2} = 0.88). Bland-Altman analysis showed a bias of 31 mL and a standard deviation of 30 mL over a range of 1265 to 1526 mL. tICV measurements derived from CT using our proposed algorithm have shown to be reliable and consistent compared to manual delineation. However, it appears difficult to directly compare tICV measures between CT and MRI. (orig.)

Automated CT-based segmentation and quantification of total intracranial volume

International Nuclear Information System (INIS)

Aguilar, Carlos; Wahlund, Lars-Olof; Westman, Eric; Edholm, Kaijsa; Cavallin, Lena; Muller, Susanne; Axelsson, Rimma; Simmons, Andrew; Skoog, Ingmar; Larsson, Elna-Marie

2015-01-01

To develop an algorithm to segment and obtain an estimate of total intracranial volume (tICV) from computed tomography (CT) images. Thirty-six CT examinations from 18 patients were included. Ten patients were examined twice the same day and eight patients twice six months apart (these patients also underwent MRI). The algorithm combines morphological operations, intensity thresholding and mixture modelling. The method was validated against manual delineation and its robustness assessed from repeated imaging examinations. Using automated MRI software, the comparability with MRI was investigated. Volumes were compared based on average relative volume differences and their magnitudes; agreement was shown by a Bland-Altman analysis graph. We observed good agreement between our algorithm and manual delineation of a trained radiologist: the Pearson's correlation coefficient was r = 0.94, tICVml[manual] = 1.05 x tICVml[automated] - 33.78 (R 2 = 0.88). Bland-Altman analysis showed a bias of 31 mL and a standard deviation of 30 mL over a range of 1265 to 1526 mL. tICV measurements derived from CT using our proposed algorithm have shown to be reliable and consistent compared to manual delineation. However, it appears difficult to directly compare tICV measures between CT and MRI. (orig.)

Liver segmentation in contrast enhanced CT data using graph cuts and interactive 3D segmentation refinement methods

Energy Technology Data Exchange (ETDEWEB)

Beichel, Reinhard; Bornik, Alexander; Bauer, Christian; Sorantin, Erich [Departments of Electrical and Computer Engineering and Internal Medicine, Iowa Institute for Biomedical Imaging, University of Iowa, Iowa City, Iowa 52242 (United States); Institute for Computer Graphics and Vision, Graz University of Technology, Inffeldgasse 16, A-8010 Graz (Austria); Department of Electrical and Computer Engineering, Iowa Institute for Biomedical Imaging, University of Iowa, Iowa City, Iowa 52242 (United States); Department of Radiology, Medical University Graz, Auenbruggerplatz 34, A-8010 Graz (Austria)

2012-03-15

Purpose: Liver segmentation is an important prerequisite for the assessment of liver cancer treatment options like tumor resection, image-guided radiation therapy (IGRT), radiofrequency ablation, etc. The purpose of this work was to evaluate a new approach for liver segmentation. Methods: A graph cuts segmentation method was combined with a three-dimensional virtual reality based segmentation refinement approach. The developed interactive segmentation system allowed the user to manipulate volume chunks and/or surfaces instead of 2D contours in cross-sectional images (i.e, slice-by-slice). The method was evaluated on twenty routinely acquired portal-phase contrast enhanced multislice computed tomography (CT) data sets. An independent reference was generated by utilizing a currently clinically utilized slice-by-slice segmentation method. After 1 h of introduction to the developed segmentation system, three experts were asked to segment all twenty data sets with the proposed method. Results: Compared to the independent standard, the relative volumetric segmentation overlap error averaged over all three experts and all twenty data sets was 3.74%. Liver segmentation required on average 16 min of user interaction per case. The calculated relative volumetric overlap errors were not found to be significantly different [analysis of variance (ANOVA) test, p = 0.82] between experts who utilized the proposed 3D system. In contrast, the time required by each expert for segmentation was found to be significantly different (ANOVA test, p = 0.0009). Major differences between generated segmentations and independent references were observed in areas were vessels enter or leave the liver and no accepted criteria for defining liver boundaries exist. In comparison, slice-by-slice based generation of the independent standard utilizing a live wire tool took 70.1 min on average. A standard 2D segmentation refinement approach applied to all twenty data sets required on average 38.2 min of

Liver segmentation in contrast enhanced CT data using graph cuts and interactive 3D segmentation refinement methods

International Nuclear Information System (INIS)

Beichel, Reinhard; Bornik, Alexander; Bauer, Christian; Sorantin, Erich

2012-01-01

Purpose: Liver segmentation is an important prerequisite for the assessment of liver cancer treatment options like tumor resection, image-guided radiation therapy (IGRT), radiofrequency ablation, etc. The purpose of this work was to evaluate a new approach for liver segmentation. Methods: A graph cuts segmentation method was combined with a three-dimensional virtual reality based segmentation refinement approach. The developed interactive segmentation system allowed the user to manipulate volume chunks and/or surfaces instead of 2D contours in cross-sectional images (i.e, slice-by-slice). The method was evaluated on twenty routinely acquired portal-phase contrast enhanced multislice computed tomography (CT) data sets. An independent reference was generated by utilizing a currently clinically utilized slice-by-slice segmentation method. After 1 h of introduction to the developed segmentation system, three experts were asked to segment all twenty data sets with the proposed method. Results: Compared to the independent standard, the relative volumetric segmentation overlap error averaged over all three experts and all twenty data sets was 3.74%. Liver segmentation required on average 16 min of user interaction per case. The calculated relative volumetric overlap errors were not found to be significantly different [analysis of variance (ANOVA) test, p = 0.82] between experts who utilized the proposed 3D system. In contrast, the time required by each expert for segmentation was found to be significantly different (ANOVA test, p = 0.0009). Major differences between generated segmentations and independent references were observed in areas were vessels enter or leave the liver and no accepted criteria for defining liver boundaries exist. In comparison, slice-by-slice based generation of the independent standard utilizing a live wire tool took 70.1 min on average. A standard 2D segmentation refinement approach applied to all twenty data sets required on average 38.2 min of

Liver segmentation in contrast enhanced CT data using graph cuts and interactive 3D segmentation refinement methods.

Science.gov (United States)

Beichel, Reinhard; Bornik, Alexander; Bauer, Christian; Sorantin, Erich

2012-03-01

Liver segmentation is an important prerequisite for the assessment of liver cancer treatment options like tumor resection, image-guided radiation therapy (IGRT), radiofrequency ablation, etc. The purpose of this work was to evaluate a new approach for liver segmentation. A graph cuts segmentation method was combined with a three-dimensional virtual reality based segmentation refinement approach. The developed interactive segmentation system allowed the user to manipulate volume chunks and∕or surfaces instead of 2D contours in cross-sectional images (i.e, slice-by-slice). The method was evaluated on twenty routinely acquired portal-phase contrast enhanced multislice computed tomography (CT) data sets. An independent reference was generated by utilizing a currently clinically utilized slice-by-slice segmentation method. After 1 h of introduction to the developed segmentation system, three experts were asked to segment all twenty data sets with the proposed method. Compared to the independent standard, the relative volumetric segmentation overlap error averaged over all three experts and all twenty data sets was 3.74%. Liver segmentation required on average 16 min of user interaction per case. The calculated relative volumetric overlap errors were not found to be significantly different [analysis of variance (ANOVA) test, p = 0.82] between experts who utilized the proposed 3D system. In contrast, the time required by each expert for segmentation was found to be significantly different (ANOVA test, p = 0.0009). Major differences between generated segmentations and independent references were observed in areas were vessels enter or leave the liver and no accepted criteria for defining liver boundaries exist. In comparison, slice-by-slice based generation of the independent standard utilizing a live wire tool took 70.1 min on average. A standard 2D segmentation refinement approach applied to all twenty data sets required on average 38.2 min of user interaction

Automatic segmentation of thoracic and pelvic CT images for radiotherapy planning using implicit anatomic knowledge and organ-specific segmentation strategies

International Nuclear Information System (INIS)

Haas, B; Coradi, T; Scholz, M; Kunz, P; Huber, M; Oppitz, U; Andre, L; Lengkeek, V; Huyskens, D; Esch, A van; Reddick, R

2008-01-01

Automatic segmentation of anatomical structures in medical images is a valuable tool for efficient computer-aided radiotherapy and surgery planning and an enabling technology for dynamic adaptive radiotherapy. This paper presents the design, algorithms and validation of new software for the automatic segmentation of CT images used for radiotherapy treatment planning. A coarse to fine approach is followed that consists of presegmentation, anatomic orientation and structure segmentation. No user input or a priori information about the image content is required. In presegmentation, the body outline, the bones and lung equivalent tissue are detected. Anatomic orientation recognizes the patient's position, orientation and gender and creates an elastic mapping of the slice positions to a reference scale. Structure segmentation is divided into localization, outlining and refinement, performed by procedures with implicit anatomic knowledge using standard image processing operations. The presented version of algorithms automatically segments the body outline and bones in any gender and patient position, the prostate, bladder and femoral heads for male pelvis in supine position, and the spinal canal, lungs, heart and trachea in supine position. The software was developed and tested on a collection of over 600 clinical radiotherapy planning CT stacks. In a qualitative validation on this test collection, anatomic orientation correctly detected gender, patient position and body region in 98% of the cases, a correct mapping was produced for 89% of thorax and 94% of pelvis cases. The average processing time for the entire segmentation of a CT stack was less than 1 min on a standard personal computer. Two independent retrospective studies were carried out for clinical validation. Study I was performed on 66 cases (30 pelvis, 36 thorax) with dosimetrists, study II on 52 cases (39 pelvis, 13 thorax) with radio-oncologists as experts. The experts rated the automatically produced

Image registration/fusion software for PET and CT/MRI by using simultaneous emission and transmission scans

International Nuclear Information System (INIS)

Kitamura, Keishi; Amano, Masaharu; Sato, Tomohiko; Okumura, Takeshi; Konishi, Norihiro; Komatsu, Masahiko

2003-01-01

When PET (positron emission tomography) is used for oncology studies, it is important to register and over-lay PET images with the images of other anatomical modalities, such as those obtained by CT (computed tomography) or MRI (magnetic resonance imaging), in order for the lesions to be anatomically located with high accuracy. The Shimadzu SET-2000W Series PET scanners provide simultaneous acquisition of emission and transmission data, which is capable of complete spatial alignment of both functional and attenuation images. This report describes our newly developed image registration/fusion software, which reformats PET emission images to the CT/MRI grid by using the transform matrix obtained by matching PET transmission images with CT/MRI images. Transmission images are registered and fused either automatically or manually, through 3-dimensional rotation and translation, with the transaxial, sagittal, and coronal fused images being monitored on the screen. This new method permits sufficiently accurate registration and efficient data processing with promoting effective use of CT/MRI images of the DICOM format, without using markers in data acquisition or any special equipment, such as a combined PET/CT scanner. (author)

Study on Two-segment Electric-mechanical Composite Braking Strategy of Tracked Vehicle Hybrid Transmission System

OpenAIRE

Ma, Tian; Gai, Jiangtao; Ma, Xiaofeng

2010-01-01

In order to lighten abrasion of braking system of hybrid electric tracked vehicle, according to characteristic of hybrid electric transmission, electric-mechanical composite braking method was proposed. By means of analyzing performance of electric braking and mechanical braking and three-segment composite braking strategy, two-segment electric-mechanical composite braking strategy was put forward in this paper. Simulation results of Matlab/Simulink indicated that the two-segment electric-mec...

Computer-assisted segmentation of CT images by statistical region merging for the production of voxel models of anatomy for CT dosimetry

Czech Academy of Sciences Publication Activity Database

Caon, M.; Sedlář, Jiří; Bajger, M.; Lee, G.

2014-01-01

Roč. 37, č. 2 (2014), s. 393-403 ISSN 0158-9938 Institutional support: RVO:67985556 Keywords : Voxel model * Image segmentation * Statistical region merging * CT dosimetry Subject RIV: JD - Computer Applications, Robotics Impact factor: 0.882, year: 2014 http://library.utia.cas.cz/separaty/2014/ZOI/sedlar-0428537.pdf

Automated detection and volumetric segmentation of the spleen in CT scans; Automatische Detektion und volumetrische Segmentierung der Milz in CT-Untersuchungen

Energy Technology Data Exchange (ETDEWEB)

Hammon, M.; Dankerl, P.; Janka, R.; Uder, M.; Cavallaro, A. [Universitaetsklinikum Erlangen (Germany). Radiologisches Inst.; Kramer, M.; Seifert, S.; Tsymbal, A.; Costa, M.J. [Siemens AG, Erlangen (Germany). Corporate Technology

2012-08-15

To introduce automated detection and volumetric segmentation of the spleen in spiral CT scans with the THESEUS-MEDICO software. The consistency between automated volumetry (aV), estimated volume determination (eV) and manual volume segmentation (mV) was evaluated. Retrospective evaluation of the CAD system based on methods like ''marginal space learning'' and ''boosting algorithms''. 3 consecutive spiral CT scans (thoraco-abdominal; portal-venous contrast agent phase; 1 or 5 mm slice thickness) of 15 consecutive lymphoma patients were included. The eV: 30 cm{sup 3} + 0.58 (width x length x thickness of the spleen) and the mV as the reference standard were determined by an experienced radiologist. The aV could be performed in all CT scans within 15.2 ({+-} 2.4) seconds. The average splenic volume measured by aV was 268.21 {+-} 114.67 cm{sup 3} compared to 281.58 {+-} 130.21 cm{sup 3} in mV and 268.93 {+-} 104.60 cm{sup 3} in eV. The correlation coefficient was 0.99 (coefficient of determination (R{sup 2}) = 0.98) for aV and mV, 0.91 (R{sup 2} = 0.83) for mV and eV and 0.91 (R{sup 2} = 0.82) for aV and eV. There was an almost perfect correlation of the changes in splenic volume measured with the new aV and mV (0.92; R{sup 2} = 0.84), mV and eV (0.95; R{sup 2} = 0.91) and aV and eV (0.83; R{sup 2} = 0.69) between two time points. The automated detection and volumetric segmentation software rapidly provides an accurate measurement of the splenic volume in CT scans. Knowledge about splenic volume and its change between two examinations provides valuable clinical information without effort for the radiologist. (orig.)

Integrated image presentation of transmission and fluorescent X-ray CT using synchrotron radiation

Energy Technology Data Exchange (ETDEWEB)

Zeniya, T.; Takeda, T. E-mail: ttakeda@md.tsukuba.ac.jp; Yu, Q.; Hasegawa, Y.; Hyodo, K.; Yuasa, T.; Hiranaka, Y.; Itai, Y.; Akatsuka, T

2001-07-21

We have developed a computed tomography (CT) system with synchrotron radiation (SR) to detect fluorescent X-rays and transmitted X-rays simultaneously. Both SR transmission X-ray CT (SR-TXCT) and SR fluorescent X-ray CT (SR-FXCT) can describe cross-sectional images with high spatial and contrast resolutions as compared to conventional CT. TXCT gives morphological information and FXCT gives functional information of organs. So, superposed display system for SR-FXCT and SR-TXCT images has been developed for clinical diagnosis with higher reliability. Preliminary experiment with brain phantom was carried out and the superposition of both images was performed. The superposed SR-CT image gave us both functional and morphological information easily with high reliability, thus demonstrating the usefulness of this system.

Integrated image presentation of transmission and fluorescent X-ray CT using synchrotron radiation

Science.gov (United States)

Zeniya, T.; Takeda, T.; Yu, Q.; Hasegawa, Y.; Hyodo, K.; Yuasa, T.; Hiranaka, Y.; Itai, Y.; Akatsuka, T.

2001-07-01

We have developed a computed tomography (CT) system with synchrotron radiation (SR) to detect fluorescent X-rays and transmitted X-rays simultaneously. Both SR transmission X-ray CT (SR-TXCT) and SR fluorescent X-ray CT (SR-FXCT) can describe cross-sectional images with high spatial and contrast resolutions as compared to conventional CT. TXCT gives morphological information and FXCT gives functional information of organs. So, superposed display system for SR-FXCT and SR-TXCT images has been developed for clinical diagnosis with higher reliability. Preliminary experiment with brain phantom was carried out and the superposition of both images was performed. The superposed SR-CT image gave us both functional and morphological information easily with high reliability, thus demonstrating the usefulness of this system.

Vessel Enhancement and Segmentation of 4D CT Lung Image Using Stick Tensor Voting

Science.gov (United States)

Cong, Tan; Hao, Yang; Jingli, Shi; Xuan, Yang

2016-12-01

Vessel enhancement and segmentation plays a significant role in medical image analysis. This paper proposes a novel vessel enhancement and segmentation method for 4D CT lung image using stick tensor voting algorithm, which focuses on addressing the vessel distortion issue of vessel enhancement diffusion (VED) method. Furthermore, the enhanced results are easily segmented using level-set segmentation. In our method, firstly, vessels are filtered using Frangi's filter to reduce intrapulmonary noises and extract rough blood vessels. Secondly, stick tensor voting algorithm is employed to estimate the correct direction along the vessel. Then the estimated direction along the vessel is used as the anisotropic diffusion direction of vessel in VED algorithm, which makes the intensity diffusion of points locating at the vessel wall be consistent with the directions of vessels and enhance the tubular features of vessels. Finally, vessels can be extracted from the enhanced image by applying level-set segmentation method. A number of experiments results show that our method outperforms traditional VED method in vessel enhancement and results in satisfied segmented vessels.

Reproducibility of Lobar Perfusion and Ventilation Quantification Using SPECT/CT Segmentation Software in Lung Cancer Patients.

Science.gov (United States)

Provost, Karine; Leblond, Antoine; Gauthier-Lemire, Annie; Filion, Édith; Bahig, Houda; Lord, Martin

2017-09-01

Planar perfusion scintigraphy with 99m Tc-labeled macroaggregated albumin is often used for pretherapy quantification of regional lung perfusion in lung cancer patients, particularly those with poor respiratory function. However, subdividing lung parenchyma into rectangular regions of interest, as done on planar images, is a poor reflection of true lobar anatomy. New tridimensional methods using SPECT and SPECT/CT have been introduced, including semiautomatic lung segmentation software. The present study evaluated inter- and intraobserver agreement on quantification using SPECT/CT software and compared the results for regional lung contribution obtained with SPECT/CT and planar scintigraphy. Methods: Thirty lung cancer patients underwent ventilation-perfusion scintigraphy with 99m Tc-macroaggregated albumin and 99m Tc-Technegas. The regional lung contribution to perfusion and ventilation was measured on both planar scintigraphy and SPECT/CT using semiautomatic lung segmentation software by 2 observers. Interobserver and intraobserver agreement for the SPECT/CT software was assessed using the intraclass correlation coefficient, Bland-Altman plots, and absolute differences in measurements. Measurements from planar and tridimensional methods were compared using the paired-sample t test and mean absolute differences. Results: Intraclass correlation coefficients were in the excellent range (above 0.9) for both interobserver and intraobserver agreement using the SPECT/CT software. Bland-Altman analyses showed very narrow limits of agreement. Absolute differences were below 2.0% in 96% of both interobserver and intraobserver measurements. There was a statistically significant difference between planar and SPECT/CT methods ( P software is highly reproducible. This tridimensional method yields statistically significant differences in measurements for right lung lobes when compared with planar scintigraphy. We recommend that SPECT/CT-based quantification be used for all lung

A deformable-model approach to semi-automatic segmentation of CT images demonstrated by application to the spinal canal

International Nuclear Information System (INIS)

Burnett, Stuart S.C.; Starkschall, George; Stevens, Craig W.; Liao Zhongxing

2004-01-01

Because of the importance of accurately defining the target in radiation treatment planning, we have developed a deformable-template algorithm for the semi-automatic delineation of normal tissue structures on computed tomography (CT) images. We illustrate the method by applying it to the spinal canal. Segmentation is performed in three steps: (a) partial delineation of the anatomic structure is obtained by wavelet-based edge detection; (b) a deformable-model template is fitted to the edge set by chamfer matching; and (c) the template is relaxed away from its original shape into its final position. Appropriately chosen ranges for the model parameters limit the deformations of the template, accounting for interpatient variability. Our approach differs from those used in other deformable models in that it does not inherently require the modeling of forces. Instead, the spinal canal was modeled using Fourier descriptors derived from four sets of manually drawn contours. Segmentation was carried out, without manual intervention, on five CT data sets and the algorithm's performance was judged subjectively by two radiation oncologists. Two assessments were considered: in the first, segmentation on a random selection of 100 axial CT images was compared with the corresponding contours drawn manually by one of six dosimetrists, also chosen randomly; in the second assessment, the segmentation of each image in the five evaluable CT sets (a total of 557 axial images) was rated as either successful, unsuccessful, or requiring further editing. Contours generated by the algorithm were more likely than manually drawn contours to be considered acceptable by the oncologists. The mean proportions of acceptable contours were 93% (automatic) and 69% (manual). Automatic delineation of the spinal canal was deemed to be successful on 91% of the images, unsuccessful on 2% of the images, and requiring further editing on 7% of the images. Our deformable template algorithm thus gives a robust

Towards dense volumetric pancreas segmentation in CT using 3D fully convolutional networks

Science.gov (United States)

Roth, Holger; Oda, Masahiro; Shimizu, Natsuki; Oda, Hirohisa; Hayashi, Yuichiro; Kitasaka, Takayuki; Fujiwara, Michitaka; Misawa, Kazunari; Mori, Kensaku

2018-03-01

Pancreas segmentation in computed tomography imaging has been historically difficult for automated methods because of the large shape and size variations between patients. In this work, we describe a custom-build 3D fully convolutional network (FCN) that can process a 3D image including the whole pancreas and produce an automatic segmentation. We investigate two variations of the 3D FCN architecture; one with concatenation and one with summation skip connections to the decoder part of the network. We evaluate our methods on a dataset from a clinical trial with gastric cancer patients, including 147 contrast enhanced abdominal CT scans acquired in the portal venous phase. Using the summation architecture, we achieve an average Dice score of 89.7 +/- 3.8 (range [79.8, 94.8])% in testing, achieving the new state-of-the-art performance in pancreas segmentation on this dataset.

Pancreas segmentation from 3D abdominal CT images using patient-specific weighted subspatial probabilistic atlases

Science.gov (United States)

Karasawa, Kenichi; Oda, Masahiro; Hayashi, Yuichiro; Nimura, Yukitaka; Kitasaka, Takayuki; Misawa, Kazunari; Fujiwara, Michitaka; Rueckert, Daniel; Mori, Kensaku

2015-03-01

Abdominal organ segmentations from CT volumes are now widely used in the computer-aided diagnosis and surgery assistance systems. Among abdominal organs, the pancreas is especially difficult to segment because of its large individual differences of the shape and position. In this paper, we propose a new pancreas segmentation method from 3D abdominal CT volumes using patient-specific weighted-subspatial probabilistic atlases. First of all, we perform normalization of organ shapes in training volumes and an input volume. We extract the Volume Of Interest (VOI) of the pancreas from the training volumes and an input volume. We divide each training VOI and input VOI into some cubic regions. We use a nonrigid registration method to register these cubic regions of the training VOI to corresponding regions of the input VOI. Based on the registration results, we calculate similarities between each cubic region of the training VOI and corresponding region of the input VOI. We select cubic regions of training volumes having the top N similarities in each cubic region. We subspatially construct probabilistic atlases weighted by the similarities in each cubic region. After integrating these probabilistic atlases in cubic regions into one, we perform a rough-to-precise segmentation of the pancreas using the atlas. The results of the experiments showed that utilization of the training volumes having the top N similarities in each cubic region led good results of the pancreas segmentation. The Jaccard Index and the average surface distance of the result were 58.9% and 2.04mm on average, respectively.

Automated medical image segmentation techniques

Directory of Open Access Journals (Sweden)

Sharma Neeraj

2010-01-01

Full Text Available Accurate segmentation of medical images is a key step in contouring during radiotherapy planning. Computed topography (CT and Magnetic resonance (MR imaging are the most widely used radiographic techniques in diagnosis, clinical studies and treatment planning. This review provides details of automated segmentation methods, specifically discussed in the context of CT and MR images. The motive is to discuss the problems encountered in segmentation of CT and MR images, and the relative merits and limitations of methods currently available for segmentation of medical images.

Parametric modelling and segmentation of vertebral bodies in 3D CT and MR spine images

International Nuclear Information System (INIS)

Štern, Darko; Likar, Boštjan; Pernuš, Franjo; Vrtovec, Tomaž

2011-01-01

Accurate and objective evaluation of vertebral deformations is of significant importance in clinical diagnostics and therapy of pathological conditions affecting the spine. Although modern clinical practice is focused on three-dimensional (3D) computed tomography (CT) and magnetic resonance (MR) imaging techniques, the established methods for evaluation of vertebral deformations are limited to measuring deformations in two-dimensional (2D) x-ray images. In this paper, we propose a method for quantitative description of vertebral body deformations by efficient modelling and segmentation of vertebral bodies in 3D. The deformations are evaluated from the parameters of a 3D superquadric model, which is initialized as an elliptical cylinder and then gradually deformed by introducing transformations that yield a more detailed representation of the vertebral body shape. After modelling the vertebral body shape with 25 clinically meaningful parameters and the vertebral body pose with six rigid body parameters, the 3D model is aligned to the observed vertebral body in the 3D image. The performance of the method was evaluated on 75 vertebrae from CT and 75 vertebrae from T 2 -weighted MR spine images, extracted from the thoracolumbar part of normal and pathological spines. The results show that the proposed method can be used for 3D segmentation of vertebral bodies in CT and MR images, as the proposed 3D model is able to describe both normal and pathological vertebral body deformations. The method may therefore be used for initialization of whole vertebra segmentation or for quantitative measurement of vertebral body deformations.

Effect of CT scanning parameters on volumetric measurements of pulmonary nodules by 3D active contour segmentation: a phantom study

International Nuclear Information System (INIS)

Way, Ted W; Chan, H-P; Goodsitt, Mitchell M; Sahiner, Berkman; Hadjiiski, Lubomir M; Zhou Chuan; Chughtai, Aamer

2008-01-01

The purpose of this study is to investigate the effects of CT scanning and reconstruction parameters on automated segmentation and volumetric measurements of nodules in CT images. Phantom nodules of known sizes were used so that segmentation accuracy could be quantified in comparison to ground-truth volumes. Spherical nodules having 4.8, 9.5 and 16 mm diameters and 50 and 100 mg cc -1 calcium contents were embedded in lung-tissue-simulating foam which was inserted in the thoracic cavity of a chest section phantom. CT scans of the phantom were acquired with a 16-slice scanner at various tube currents, pitches, fields-of-view and slice thicknesses. Scans were also taken using identical techniques either within the same day or five months apart for study of reproducibility. The phantom nodules were segmented with a three-dimensional active contour (3DAC) model that we previously developed for use on patient nodules. The percentage volume errors relative to the ground-truth volumes were estimated under the various imaging conditions. There was no statistically significant difference in volume error for repeated CT scans or scans taken with techniques where only pitch, field of view, or tube current (mA) were changed. However, the slice thickness significantly (p < 0.05) affected the volume error. Therefore, to evaluate nodule growth, consistent imaging conditions and high resolution should be used for acquisition of the serial CT scans, especially for smaller nodules. Understanding the effects of scanning and reconstruction parameters on volume measurements by 3DAC allows better interpretation of data and assessment of growth. Tracking nodule growth with computerized segmentation methods would reduce inter- and intraobserver variabilities

SU-D-206-03: Segmentation Assisted Fast Iterative Reconstruction Method for Cone-Beam CT

International Nuclear Information System (INIS)

Wu, P; Mao, T; Gong, S; Wang, J; Niu, T; Sheng, K; Xie, Y

2016-01-01

Purpose: Total Variation (TV) based iterative reconstruction (IR) methods enable accurate CT image reconstruction from low-dose measurements with sparse projection acquisition, due to the sparsifiable feature of most CT images using gradient operator. However, conventional solutions require large amount of iterations to generate a decent reconstructed image. One major reason is that the expected piecewise constant property is not taken into consideration at the optimization starting point. In this work, we propose an iterative reconstruction method for cone-beam CT (CBCT) using image segmentation to guide the optimization path more efficiently on the regularization term at the beginning of the optimization trajectory. Methods: Our method applies general knowledge that one tissue component in the CT image contains relatively uniform distribution of CT number. This general knowledge is incorporated into the proposed reconstruction using image segmentation technique to generate the piecewise constant template on the first-pass low-quality CT image reconstructed using analytical algorithm. The template image is applied as an initial value into the optimization process. Results: The proposed method is evaluated on the Shepp-Logan phantom of low and high noise levels, and a head patient. The number of iterations is reduced by overall 40%. Moreover, our proposed method tends to generate a smoother reconstructed image with the same TV value. Conclusion: We propose a computationally efficient iterative reconstruction method for CBCT imaging. Our method achieves a better optimization trajectory and a faster convergence behavior. It does not rely on prior information and can be readily incorporated into existing iterative reconstruction framework. Our method is thus practical and attractive as a general solution to CBCT iterative reconstruction. This work is supported by the Zhejiang Provincial Natural Science Foundation of China (Grant No. LR16F010001), National High-tech R

SU-D-206-03: Segmentation Assisted Fast Iterative Reconstruction Method for Cone-Beam CT

Energy Technology Data Exchange (ETDEWEB)

Wu, P; Mao, T; Gong, S; Wang, J; Niu, T [Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Institute of Translational Medicine, Zhejiang University, Hangzhou, Zhejiang (China); Sheng, K [Department of Radiation Oncology, University of California, Los Angeles, Los Angeles, CA (United States); Xie, Y [Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong (China)

2016-06-15

Purpose: Total Variation (TV) based iterative reconstruction (IR) methods enable accurate CT image reconstruction from low-dose measurements with sparse projection acquisition, due to the sparsifiable feature of most CT images using gradient operator. However, conventional solutions require large amount of iterations to generate a decent reconstructed image. One major reason is that the expected piecewise constant property is not taken into consideration at the optimization starting point. In this work, we propose an iterative reconstruction method for cone-beam CT (CBCT) using image segmentation to guide the optimization path more efficiently on the regularization term at the beginning of the optimization trajectory. Methods: Our method applies general knowledge that one tissue component in the CT image contains relatively uniform distribution of CT number. This general knowledge is incorporated into the proposed reconstruction using image segmentation technique to generate the piecewise constant template on the first-pass low-quality CT image reconstructed using analytical algorithm. The template image is applied as an initial value into the optimization process. Results: The proposed method is evaluated on the Shepp-Logan phantom of low and high noise levels, and a head patient. The number of iterations is reduced by overall 40%. Moreover, our proposed method tends to generate a smoother reconstructed image with the same TV value. Conclusion: We propose a computationally efficient iterative reconstruction method for CBCT imaging. Our method achieves a better optimization trajectory and a faster convergence behavior. It does not rely on prior information and can be readily incorporated into existing iterative reconstruction framework. Our method is thus practical and attractive as a general solution to CBCT iterative reconstruction. This work is supported by the Zhejiang Provincial Natural Science Foundation of China (Grant No. LR16F010001), National High-tech R

A hybrid method for airway segmentation and automated measurement of bronchial wall thickness on CT.

Science.gov (United States)

Xu, Ziyue; Bagci, Ulas; Foster, Brent; Mansoor, Awais; Udupa, Jayaram K; Mollura, Daniel J

2015-08-01

Inflammatory and infectious lung diseases commonly involve bronchial airway structures and morphology, and these abnormalities are often analyzed non-invasively through high resolution computed tomography (CT) scans. Assessing airway wall surfaces and the lumen are of great importance for diagnosing pulmonary diseases. However, obtaining high accuracy from a complete 3-D airway tree structure can be quite challenging. The airway tree structure has spiculated shapes with multiple branches and bifurcation points as opposed to solid single organ or tumor segmentation tasks in other applications, hence, it is complex for manual segmentation as compared with other tasks. For computerized methods, a fundamental challenge in airway tree segmentation is the highly variable intensity levels in the lumen area, which often causes a segmentation method to leak into adjacent lung parenchyma through blurred airway walls or soft boundaries. Moreover, outer wall definition can be difficult due to similar intensities of the airway walls and nearby structures such as vessels. In this paper, we propose a computational framework to accurately quantify airways through (i) a novel hybrid approach for precise segmentation of the lumen, and (ii) two novel methods (a spatially constrained Markov random walk method (pseudo 3-D) and a relative fuzzy connectedness method (3-D)) to estimate the airway wall thickness. We evaluate the performance of our proposed methods in comparison with mostly used algorithms using human chest CT images. Our results demonstrate that, on publicly available data sets and using standard evaluation criteria, the proposed airway segmentation method is accurate and efficient as compared with the state-of-the-art methods, and the airway wall estimation algorithms identified the inner and outer airway surfaces more accurately than the most widely applied methods, namely full width at half maximum and phase congruency. Copyright © 2015. Published by Elsevier B.V.

Esophagus segmentation in CT via 3D fully convolutional neural network and random walk.

Science.gov (United States)

Fechter, Tobias; Adebahr, Sonja; Baltas, Dimos; Ben Ayed, Ismail; Desrosiers, Christian; Dolz, Jose

2017-12-01

Precise delineation of organs at risk is a crucial task in radiotherapy treatment planning for delivering high doses to the tumor while sparing healthy tissues. In recent years, automated segmentation methods have shown an increasingly high performance for the delineation of various anatomical structures. However, this task remains challenging for organs like the esophagus, which have a versatile shape and poor contrast to neighboring tissues. For human experts, segmenting the esophagus from CT images is a time-consuming and error-prone process. To tackle these issues, we propose a random walker approach driven by a 3D fully convolutional neural network (CNN) to automatically segment the esophagus from CT images. First, a soft probability map is generated by the CNN. Then, an active contour model (ACM) is fitted to the CNN soft probability map to get a first estimation of the esophagus location. The outputs of the CNN and ACM are then used in conjunction with a probability model based on CT Hounsfield (HU) values to drive the random walker. Training and evaluation were done on 50 CTs from two different datasets, with clinically used peer-reviewed esophagus contours. Results were assessed regarding spatial overlap and shape similarity. The esophagus contours generated by the proposed algorithm showed a mean Dice coefficient of 0.76 ± 0.11, an average symmetric square distance of 1.36 ± 0.90 mm, and an average Hausdorff distance of 11.68 ± 6.80, compared to the reference contours. These results translate to a very good agreement with reference contours and an increase in accuracy compared to existing methods. Furthermore, when considering the results reported in the literature for the publicly available Synapse dataset, our method outperformed all existing approaches, which suggests that the proposed method represents the current state-of-the-art for automatic esophagus segmentation. We show that a CNN can yield accurate estimations of esophagus location, and that

An automatic system for segmentation, matching, anatomical labeling and measurement of airways from CT images

DEFF Research Database (Denmark)

Petersen, Jens; Feragen, Aasa; Owen, Megan

segmental branches, and longitudinal matching of airway branches in repeated scans of the same subject. Methods and Materials: The segmentation process begins from an automatically detected seed point in the trachea. The airway centerline tree is then constructed by iteratively adding locally optimal paths...... differences. Results: The segmentation method has been used on 9711 low dose CT images from the Danish Lung Cancer Screening Trial (DLCST). Manual inspection of thumbnail images revealed gross errors in a total of 44 images. 29 were missing branches at the lobar level and only 15 had obvious false positives...... measurements to segments matched in multiple images of the same subject using image registration was observed to increase their reproducibility. The anatomical branch labeling tool was validated on a subset of 20 subjects, 5 of each category: asymptomatic, mild, moderate and severe COPD. The average inter...

Consistent interactive segmentation of pulmonary ground glass nodules identified in CT studies

Science.gov (United States)

Zhang, Li; Fang, Ming; Naidich, David P.; Novak, Carol L.

2004-05-01

Ground glass nodules (GGNs) have proved especially problematic in lung cancer diagnosis, as despite frequently being malignant they characteristically have extremely slow rates of growth. This problem is further magnified by the small size of many of these lesions now being routinely detected following the introduction of multislice CT scanners capable of acquiring contiguous high resolution 1 to 1.25 mm sections throughout the thorax in a single breathhold period. Although segmentation of solid nodules can be used clinically to determine volume doubling times quantitatively, reliable methods for segmentation of pure ground glass nodules have yet to be introduced. Our purpose is to evaluate a newly developed computer-based segmentation method for rapid and reproducible measurements of pure ground glass nodules. 23 pure or mixed ground glass nodules were identified in a total of 8 patients by a radiologist and subsequently segmented by our computer-based method using Markov random field and shape analysis. The computer-based segmentation was initialized by a click point. Methodological consistency was assessed using the overlap ratio between 3 segmentations initialized by 3 different click points for each nodule. The 95% confidence interval on the mean of the overlap ratios proved to be [0.984, 0.998]. The computer-based method failed on two nodules that were difficult to segment even manually either due to especially low contrast or markedly irregular margins. While achieving consistent manual segmentation of ground glass nodules has proven problematic most often due to indistinct boundaries and interobserver variability, our proposed method introduces a powerful new tool for obtaining reproducible quantitative measurements of these lesions. It is our intention to further document the value of this approach with a still larger set of ground glass nodules.

Truncation artifact suppression in cone-beam radionuclide transmission CT using maximum likelihood techniques: evaluation with human subjects

International Nuclear Information System (INIS)

Manglos, S.H.

1992-01-01

Transverse image truncation can be a serious problem for human imaging using cone-beam transmission CT (CB-CT) implemented on a conventional rotating gamma camera. This paper presents a reconstruction method to reduce or eliminate the artifacts resulting from the truncation. The method uses a previously published transmission maximum likelihood EM algorithm, adapted to the cone-beam geometry. The reconstruction method is evaluated qualitatively using three human subjects of various dimensions and various degrees of truncation. (author)

Multi-organ segmentation from multi-phase abdominal CT via 4D graphs using enhancement, shape and location optimization.

Science.gov (United States)

Linguraru, Marius George; Pura, John A; Chowdhury, Ananda S; Summers, Ronald M

2010-01-01

The interpretation of medical images benefits from anatomical and physiological priors to optimize computer-aided diagnosis (CAD) applications. Diagnosis also relies on the comprehensive analysis of multiple organs and quantitative measures of soft tissue. An automated method optimized for medical image data is presented for the simultaneous segmentation of four abdominal organs from 4D CT data using graph cuts. Contrast-enhanced CT scans were obtained at two phases: non-contrast and portal venous. Intra-patient data were spatially normalized by non-linear registration. Then 4D erosion using population historic information of contrast-enhanced liver, spleen, and kidneys was applied to multi-phase data to initialize the 4D graph and adapt to patient specific data. CT enhancement information and constraints on shape, from Parzen windows, and location, from a probabilistic atlas, were input into a new formulation of a 4D graph. Comparative results demonstrate the effects of appearance and enhancement, and shape and location on organ segmentation.

Proposal of a novel ensemble learning based segmentation with a shape prior and its application to spleen segmentation from a 3D abdominal CT volume

International Nuclear Information System (INIS)

Shindo, Kiyo; Shimizu, Akinobu; Kobatake, Hidefumi; Nawano, Shigeru; Shinozaki, Kenji

2010-01-01

An organ segmentation learned by a conventional ensemble learning algorithm suffers from unnatural errors because each voxel is classified independently in the segmentation process. This paper proposes a novel ensemble learning algorithm that can take into account global shape and location of organs. It estimates the shape and location of an organ from a given image by combining an intermediate segmentation result with a statistical shape model. Once an ensemble learning algorithm could not improve the segmentation performance in the iterative learning process, it estimates the shape and location by finding an optimal model parameter set with maximum degree of correspondence between a statistical shape model and the intermediate segmentation result. Novel weak classifiers are generated based on a signed distance from a boundary of the estimated shape and a distance from a barycenter of the intermediate segmentation result. Subsequently it continues the learning process with the novel weak classifiers. This paper presents experimental results where the proposed ensemble learning algorithm generates a segmentation process that can extract a spleen from a 3D CT image more precisely than a conventional one. (author)

Liver Segmentation Based on Snakes Model and Improved GrowCut Algorithm in Abdominal CT Image

Directory of Open Access Journals (Sweden)

Huiyan Jiang

2013-01-01

Full Text Available A novel method based on Snakes Model and GrowCut algorithm is proposed to segment liver region in abdominal CT images. First, according to the traditional GrowCut method, a pretreatment process using K-means algorithm is conducted to reduce the running time. Then, the segmentation result of our improved GrowCut approach is used as an initial contour for the future precise segmentation based on Snakes model. At last, several experiments are carried out to demonstrate the performance of our proposed approach and some comparisons are conducted between the traditional GrowCut algorithm. Experimental results show that the improved approach not only has a better robustness and precision but also is more efficient than the traditional GrowCut method.

Adaptation of the Maracas algorithm for carotid artery segmentation and stenosis quantification on CT images

International Nuclear Information System (INIS)

Maria A Zuluaga; Maciej Orkisz; Edgar J F Delgado; Vincent Dore; Alfredo Morales Pinzon; Marcela Hernandez Hoyos

2010-01-01

This paper describes the adaptations of Maracas algorithm to the segmentation and quantification of vascular structures in CTA images of the carotid artery. The maracas algorithm, which is based on an elastic model and on a multi-scale Eigen-analysis of the inertia matrix, was originally designed to segment a single artery in MRA images. The modifications are primarily aimed at addressing the specificities of CT images and the bifurcations. The algorithms implemented in this new version are classified into two levels. 1. The low-level processing (filtering of noise and directional artifacts, enhancement and pre-segmentation) to improve the quality of the image and to pre-segment it. These techniques are based on a priori information about noise, artifacts and typical gray levels ranges of lumen, background and calcifications. 2. The high-level processing to extract the centerline of the artery, to segment the lumen and to quantify the stenosis. At this level, we apply a priori knowledge of shape and anatomy of vascular structures. The method was evaluated on 31 datasets from the carotid lumen segmentation and stenosis grading grand challenge 2009. The segmentation results obtained an average of 80:4% dice similarity score, compared to reference segmentation, and the mean stenosis quantification error was 14.4%.

A segmentation and point-matching enhanced efficient deformable image registration method for dose accumulation between HDR CT images

International Nuclear Information System (INIS)

Zhen, Xin; Chen, Haibin; Zhou, Linghong; Yan, Hao; Jiang, Steve; Jia, Xun; Gu, Xuejun; Mell, Loren K; Yashar, Catheryn M; Cervino, Laura

2015-01-01

Deformable image registration (DIR) of fractional high-dose-rate (HDR) CT images is challenging due to the presence of applicators in the brachytherapy image. Point-to-point correspondence fails because of the undesired deformation vector fields (DVF) propagated from the applicator region (AR) to the surrounding tissues, which can potentially introduce significant DIR errors in dose mapping. This paper proposes a novel segmentation and point-matching enhanced efficient DIR (named SPEED) scheme to facilitate dose accumulation among HDR treatment fractions. In SPEED, a semi-automatic seed point generation approach is developed to obtain the incremented fore/background point sets to feed the random walks algorithm, which is used to segment and remove the AR, leaving empty AR cavities in the HDR CT images. A feature-based ‘thin-plate-spline robust point matching’ algorithm is then employed for AR cavity surface points matching. With the resulting mapping, a DVF defining on each voxel is estimated by B-spline approximation, which serves as the initial DVF for the subsequent Demons-based DIR between the AR-free HDR CT images. The calculated DVF via Demons combined with the initial one serve as the final DVF to map doses between HDR fractions. The segmentation and registration accuracy are quantitatively assessed by nine clinical HDR cases from three gynecological cancer patients. The quantitative analysis and visual inspection of the DIR results indicate that SPEED can suppress the impact of applicator on DIR, and accurately register HDR CT images as well as deform and add interfractional HDR doses. (paper)

A segmentation and point-matching enhanced efficient deformable image registration method for dose accumulation between HDR CT images

Science.gov (United States)

Zhen, Xin; Chen, Haibin; Yan, Hao; Zhou, Linghong; Mell, Loren K.; Yashar, Catheryn M.; Jiang, Steve; Jia, Xun; Gu, Xuejun; Cervino, Laura

2015-04-01

Deformable image registration (DIR) of fractional high-dose-rate (HDR) CT images is challenging due to the presence of applicators in the brachytherapy image. Point-to-point correspondence fails because of the undesired deformation vector fields (DVF) propagated from the applicator region (AR) to the surrounding tissues, which can potentially introduce significant DIR errors in dose mapping. This paper proposes a novel segmentation and point-matching enhanced efficient DIR (named SPEED) scheme to facilitate dose accumulation among HDR treatment fractions. In SPEED, a semi-automatic seed point generation approach is developed to obtain the incremented fore/background point sets to feed the random walks algorithm, which is used to segment and remove the AR, leaving empty AR cavities in the HDR CT images. A feature-based ‘thin-plate-spline robust point matching’ algorithm is then employed for AR cavity surface points matching. With the resulting mapping, a DVF defining on each voxel is estimated by B-spline approximation, which serves as the initial DVF for the subsequent Demons-based DIR between the AR-free HDR CT images. The calculated DVF via Demons combined with the initial one serve as the final DVF to map doses between HDR fractions. The segmentation and registration accuracy are quantitatively assessed by nine clinical HDR cases from three gynecological cancer patients. The quantitative analysis and visual inspection of the DIR results indicate that SPEED can suppress the impact of applicator on DIR, and accurately register HDR CT images as well as deform and add interfractional HDR doses.

Comparison of two different segmentation methods on planar lung perfusion scan with reference to quantitative value on SPECT/CT

Energy Technology Data Exchange (ETDEWEB)

Suh, Min Seok; Kang, Yeon Koo; Ha, Seung Gyun [Dept. of Nuclear Medicine, Seoul National University Hospital, Seoul (Korea, Republic of); and others

2017-06-15

Until now, there was no single standardized regional segmentation method of planar lung perfusion scan. We compared planar scan based two segmentation methods, which are frequently used in the Society of Nuclear Medicine, with reference to the lung perfusion single photon emission computed tomography (SPECT)/computed tomography (CT) derived values in lung cancer patients. Fifty-five lung cancer patients (male:female, 37:18; age, 67.8 ± 10.7 years) were evaluated. The patients underwent planar scan and SPECT/CT after injection of technetium-99 m macroaggregated albumin (Tc-99 m-MAA). The % uptake and predicted postoperative percentage forced expiratory volume in 1 s (ppoFEV1%) derived from both posterior oblique (PO) and anterior posterior (AP) methods were compared with SPECT/CT derived parameters. Concordance analysis, paired comparison, reproducibility analysis and spearman correlation analysis were conducted. The % uptake derived from PO method showed higher concordance with SPECT/CT derived % uptake in every lobe compared to AP method. Both methods showed significantly different lobar distribution of % uptake compared to SPECT/CT. For the target region, ppoFEV1% measured from PO method showed higher concordance with SPECT/CT, but lower reproducibility compared to AP method. Preliminary data revealed that every method significantly correlated with actual postoperative FEV1%, with SPECT/CT showing the best correlation. The PO method derived values showed better concordance with SPECT/CT compared to the AP method. Both PO and AP methods showed significantly different lobar distribution compared to SPECT/CT. In clinical practice such difference according to different methods and lobes should be considered for more accurate postoperative lung function prediction.

Generic method for automatic bladder segmentation on cone beam CT using a patient-specific bladder shape model

International Nuclear Information System (INIS)

Schoot, A. J. A. J. van de; Schooneveldt, G.; Wognum, S.; Stalpers, L. J. A.; Rasch, C. R. N.; Bel, A.; Hoogeman, M. S.; Chai, X.

2014-01-01

Purpose: The aim of this study is to develop and validate a generic method for automatic bladder segmentation on cone beam computed tomography (CBCT), independent of gender and treatment position (prone or supine), using only pretreatment imaging data. Methods: Data of 20 patients, treated for tumors in the pelvic region with the entire bladder visible on CT and CBCT, were divided into four equally sized groups based on gender and treatment position. The full and empty bladder contour, that can be acquired with pretreatment CT imaging, were used to generate a patient-specific bladder shape model. This model was used to guide the segmentation process on CBCT. To obtain the bladder segmentation, the reference bladder contour was deformed iteratively by maximizing the cross-correlation between directional grey value gradients over the reference and CBCT bladder edge. To overcome incorrect segmentations caused by CBCT image artifacts, automatic adaptations were implemented. Moreover, locally incorrect segmentations could be adapted manually. After each adapted segmentation, the bladder shape model was expanded and new shape patterns were calculated for following segmentations. All available CBCTs were used to validate the segmentation algorithm. The bladder segmentations were validated by comparison with the manual delineations and the segmentation performance was quantified using the Dice similarity coefficient (DSC), surface distance error (SDE) and SD of contour-to-contour distances. Also, bladder volumes obtained by manual delineations and segmentations were compared using a Bland-Altman error analysis. Results: The mean DSC, mean SDE, and mean SD of contour-to-contour distances between segmentations and manual delineations were 0.87, 0.27 cm and 0.22 cm (female, prone), 0.85, 0.28 cm and 0.22 cm (female, supine), 0.89, 0.21 cm and 0.17 cm (male, supine) and 0.88, 0.23 cm and 0.17 cm (male, prone), respectively. Manual local adaptations improved the segmentation

Clinical Application of colored three-dimensional CT (3D-CT) for brain tumors using helical scanning CT (HES-CT)

International Nuclear Information System (INIS)

Ogura, Yuko; Katada, Kazuhiro; Fujisawa, Kazuhisa; Imai, Fumihiro; Kawase, Tsukasa; Kamei, Yoshifumi; Kanno, Tetsuo; Takeshita, Gen; Koga, Sukehiko

1995-01-01

We applied colored three-dimensional CT (colored 3D-CT) images to distinguish brain tumors from the surrounding vascular and bony structures using a work station system and helical scanning CT (HES-CT). CT scanners with a slip-ring system were employed (TCT-900S and X vigor). A slice thickness of 2 mm and bed speed of 2 mm/s were used. The volume of contrast medium injected was 60 to 70 ml. Four to 8 colors were used for the tissue segmentation on the workstation system (xtension) using the data transferred from HES-CT. Tissue segmentation succeeded on the colored 3D-CT images in all 13 cases. The relationship between the tumors and the surrounding structures were easily recognized. The technique was useful to simulate operative fields, because deep structures could be visualized by cutting and drilling the colored 3D-CT volumetric data. On the basis of our findings, we suggest that colored 3D-CT images should be used as a supplementary aid for preoperative simulation. (author)

CT-based patient modeling for head and neck hyperthermia treatment planning: manual versus automatic normal-tissue-segmentation.

Science.gov (United States)

Verhaart, René F; Fortunati, Valerio; Verduijn, Gerda M; van Walsum, Theo; Veenland, Jifke F; Paulides, Margarethus M

2014-04-01

Clinical trials have shown that hyperthermia, as adjuvant to radiotherapy and/or chemotherapy, improves treatment of patients with locally advanced or recurrent head and neck (H&N) carcinoma. Hyperthermia treatment planning (HTP) guided H&N hyperthermia is being investigated, which requires patient specific 3D patient models derived from Computed Tomography (CT)-images. To decide whether a recently developed automatic-segmentation algorithm can be introduced in the clinic, we compared the impact of manual- and automatic normal-tissue-segmentation variations on HTP quality. CT images of seven patients were segmented automatically and manually by four observers, to study inter-observer and intra-observer geometrical variation. To determine the impact of this variation on HTP quality, HTP was performed using the automatic and manual segmentation of each observer, for each patient. This impact was compared to other sources of patient model uncertainties, i.e. varying gridsizes and dielectric tissue properties. Despite geometrical variations, manual and automatic generated 3D patient models resulted in an equal, i.e. 1%, variation in HTP quality. This variation was minor with respect to the total of other sources of patient model uncertainties, i.e. 11.7%. Automatically generated 3D patient models can be introduced in the clinic for H&N HTP. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

CT-based patient modeling for head and neck hyperthermia treatment planning: Manual versus automatic normal-tissue-segmentation

International Nuclear Information System (INIS)

Verhaart, René F.; Fortunati, Valerio; Verduijn, Gerda M.; Walsum, Theo van; Veenland, Jifke F.; Paulides, Margarethus M.

2014-01-01

Background and purpose: Clinical trials have shown that hyperthermia, as adjuvant to radiotherapy and/or chemotherapy, improves treatment of patients with locally advanced or recurrent head and neck (H and N) carcinoma. Hyperthermia treatment planning (HTP) guided H and N hyperthermia is being investigated, which requires patient specific 3D patient models derived from Computed Tomography (CT)-images. To decide whether a recently developed automatic-segmentation algorithm can be introduced in the clinic, we compared the impact of manual- and automatic normal-tissue-segmentation variations on HTP quality. Material and methods: CT images of seven patients were segmented automatically and manually by four observers, to study inter-observer and intra-observer geometrical variation. To determine the impact of this variation on HTP quality, HTP was performed using the automatic and manual segmentation of each observer, for each patient. This impact was compared to other sources of patient model uncertainties, i.e. varying gridsizes and dielectric tissue properties. Results: Despite geometrical variations, manual and automatic generated 3D patient models resulted in an equal, i.e. 1%, variation in HTP quality. This variation was minor with respect to the total of other sources of patient model uncertainties, i.e. 11.7%. Conclusions: Automatically generated 3D patient models can be introduced in the clinic for H and N HTP

MO-F-CAMPUS-J-05: Toward MRI-Only Radiotherapy: Novel Tissue Segmentation and Pseudo-CT Generation Techniques Based On T1 MRI Sequences

Energy Technology Data Exchange (ETDEWEB)

Aouadi, S; McGarry, M; Hammoud, R; Torfeh, T; Perkins, G; Al-Hammadi, N [Hamad Medical Corporation, NCCCR, Doha (Qatar)

2015-06-15

Purpose: To develop and validate a 4 class tissue segmentation approach (air cavities, background, bone and soft-tissue) on T1 -weighted brain MRI and to create a pseudo-CT for MRI-only radiation therapy verification. Methods: Contrast-enhanced T1-weighted fast-spin-echo sequences (TR = 756ms, TE= 7.152ms), acquired on a 1.5T GE MRI-Simulator, are used.MRIs are firstly pre-processed to correct for non uniformity using the non parametric, non uniformity intensity normalization algorithm. Subsequently, a logarithmic inverse scaling log(1/image) is applied, prior to segmentation, to better differentiate bone and air from soft-tissues. Finally, the following method is enrolled to classify intensities into air cavities, background, bone and soft-tissue:Thresholded region growing with seed points in image corners is applied to get a mask of Air+Bone+Background. The background is, afterward, separated by the scan-line filling algorithm. The air mask is extracted by morphological opening followed by a post-processing based on knowledge about air regions geometry. The remaining rough bone pre-segmentation is refined by applying 3D geodesic active contours; bone segmentation evolves by the sum of internal forces from contour geometry and external force derived from image gradient magnitude.Pseudo-CT is obtained by assigning −1000HU to air and background voxels, performing linear mapping of soft-tissue MR intensities in [-400HU, 200HU] and inverse linear mapping of bone MR intensities in [200HU, 1000HU]. Results: Three brain patients having registered MRI and CT are used for validation. CT intensities classification into 4 classes is performed by thresholding. Dice and misclassification errors are quantified. Correct classifications for soft-tissue, bone, and air are respectively 89.67%, 77.8%, and 64.5%. Dice indices are acceptable for bone (0.74) and soft-tissue (0.91) but low for air regions (0.48). Pseudo-CT produces DRRs with acceptable clinical visual agreement to CT

Automatic Segmentation and Online virtualCT in Head-and-Neck Adaptive Radiation Therapy

Energy Technology Data Exchange (ETDEWEB)

Peroni, Marta, E-mail: marta.peroni@mail.polimi.it [Department of Bioengineering, Politecnico di Milano, Milano (Italy); Ciardo, Delia [Advanced Radiotherapy Center, European Institute of Oncology, Milano (Italy); Spadea, Maria Francesca [Department of Experimental and Clinical Medicine, Universita degli Studi Magna Graecia, Catanzaro (Italy); Riboldi, Marco [Department of Bioengineering, Politecnico di Milano, Milano (Italy); Bioengineering Unit, Centro Nazionale di Adroterapia Oncologica, Pavia (Italy); Comi, Stefania; Alterio, Daniela [Advanced Radiotherapy Center, European Institute of Oncology, Milano (Italy); Baroni, Guido [Department of Bioengineering, Politecnico di Milano, Milano (Italy); Bioengineering Unit, Centro Nazionale di Adroterapia Oncologica, Pavia (Italy); Orecchia, Roberto [Advanced Radiotherapy Center, European Institute of Oncology, Milano (Italy); Universita degli Studi di Milano, Milano (Italy); Medical Department, Centro Nazionale di Adroterapia Oncologica, Pavia (Italy)

2012-11-01

Purpose: The purpose of this work was to develop and validate an efficient and automatic strategy to generate online virtual computed tomography (CT) scans for adaptive radiation therapy (ART) in head-and-neck (HN) cancer treatment. Method: We retrospectively analyzed 20 patients, treated with intensity modulated radiation therapy (IMRT), for an HN malignancy. Different anatomical structures were considered: mandible, parotid glands, and nodal gross tumor volume (nGTV). We generated 28 virtualCT scans by means of nonrigid registration of simulation computed tomography (CTsim) and cone beam CT images (CBCTs), acquired for patient setup. We validated our approach by considering the real replanning CT (CTrepl) as ground truth. We computed the Dice coefficient (DSC), center of mass (COM) distance, and root mean square error (RMSE) between correspondent points located on the automatically segmented structures on CBCT and virtualCT. Results: Residual deformation between CTrepl and CBCT was below one voxel. Median DSC was around 0.8 for mandible and parotid glands, but only 0.55 for nGTV, because of the fairly homogeneous surrounding soft tissues and of its small volume. Median COM distance and RMSE were comparable with image resolution. No significant correlation between RMSE and initial or final deformation was found. Conclusion: The analysis provides evidence that deformable image registration may contribute significantly in reducing the need of full CT-based replanning in HN radiation therapy by supporting swift and objective decision-making in clinical practice. Further work is needed to strengthen algorithm potential in nGTV localization.

Automatic segmentation and online virtualCT in head-and-neck adaptive radiation therapy.

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Peroni, Marta; Ciardo, Delia; Spadea, Maria Francesca; Riboldi, Marco; Comi, Stefania; Alterio, Daniela; Baroni, Guido; Orecchia, Roberto

2012-11-01

The purpose of this work was to develop and validate an efficient and automatic strategy to generate online virtual computed tomography (CT) scans for adaptive radiation therapy (ART) in head-and-neck (HN) cancer treatment. We retrospectively analyzed 20 patients, treated with intensity modulated radiation therapy (IMRT), for an HN malignancy. Different anatomical structures were considered: mandible, parotid glands, and nodal gross tumor volume (nGTV). We generated 28 virtualCT scans by means of nonrigid registration of simulation computed tomography (CTsim) and cone beam CT images (CBCTs), acquired for patient setup. We validated our approach by considering the real replanning CT (CTrepl) as ground truth. We computed the Dice coefficient (DSC), center of mass (COM) distance, and root mean square error (RMSE) between correspondent points located on the automatically segmented structures on CBCT and virtualCT. Residual deformation between CTrepl and CBCT was below one voxel. Median DSC was around 0.8 for mandible and parotid glands, but only 0.55 for nGTV, because of the fairly homogeneous surrounding soft tissues and of its small volume. Median COM distance and RMSE were comparable with image resolution. No significant correlation between RMSE and initial or final deformation was found. The analysis provides evidence that deformable image registration may contribute significantly in reducing the need of full CT-based replanning in HN radiation therapy by supporting swift and objective decision-making in clinical practice. Further work is needed to strengthen algorithm potential in nGTV localization. Copyright © 2012 Elsevier Inc. All rights reserved.

A volumetric pulmonary CT segmentation method with applications in emphysema assessment

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Silva, José Silvestre; Silva, Augusto; Santos, Beatriz S.

2006-03-01

A segmentation method is a mandatory pre-processing step in many automated or semi-automated analysis tasks such as region identification and densitometric analysis, or even for 3D visualization purposes. In this work we present a fully automated volumetric pulmonary segmentation algorithm based on intensity discrimination and morphologic procedures. Our method first identifies the trachea as well as primary bronchi and then the pulmonary region is identified by applying a threshold and morphologic operations. When both lungs are in contact, additional procedures are performed to obtain two separated lung volumes. To evaluate the performance of the method, we compared contours extracted from 3D lung surfaces with reference contours, using several figures of merit. Results show that the worst case generally occurs at the middle sections of high resolution CT exams, due the presence of aerial and vascular structures. Nevertheless, the average error is inferior to the average error associated with radiologist inter-observer variability, which suggests that our method produces lung contours similar to those drawn by radiologists. The information created by our segmentation algorithm is used by an identification and representation method in pulmonary emphysema that also classifies emphysema according to its severity degree. Two clinically proved thresholds are applied which identify regions with severe emphysema, and with highly severe emphysema. Based on this thresholding strategy, an application for volumetric emphysema assessment was developed offering new display paradigms concerning the visualization of classification results. This framework is easily extendable to accommodate other classifiers namely those related with texture based segmentation as it is often the case with interstitial diseases.

Computer-aided measurement of liver volumes in CT by means of geodesic active contour segmentation coupled with level-set algorithms

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Suzuki, Kenji; Kohlbrenner, Ryan; Epstein, Mark L.; Obajuluwa, Ademola M.; Xu Jianwu; Hori, Masatoshi [Department of Radiology, University of Chicago, 5841 South Maryland Avenue, Chicago, Illinois 60637 (United States)

2010-05-15

Purpose: Computerized liver extraction from hepatic CT images is challenging because the liver often abuts other organs of a similar density. The purpose of this study was to develop a computer-aided measurement of liver volumes in hepatic CT. Methods: The authors developed a computerized liver extraction scheme based on geodesic active contour segmentation coupled with level-set contour evolution. First, an anisotropic diffusion filter was applied to portal-venous-phase CT images for noise reduction while preserving the liver structure, followed by a scale-specific gradient magnitude filter to enhance the liver boundaries. Then, a nonlinear grayscale converter enhanced the contrast of the liver parenchyma. By using the liver-parenchyma-enhanced image as a speed function, a fast-marching level-set algorithm generated an initial contour that roughly estimated the liver shape. A geodesic active contour segmentation algorithm coupled with level-set contour evolution refined the initial contour to define the liver boundaries more precisely. The liver volume was then calculated using these refined boundaries. Hepatic CT scans of 15 prospective liver donors were obtained under a liver transplant protocol with a multidetector CT system. The liver volumes extracted by the computerized scheme were compared to those traced manually by a radiologist, used as ''gold standard.''Results: The mean liver volume obtained with our scheme was 1504 cc, whereas the mean gold standard manual volume was 1457 cc, resulting in a mean absolute difference of 105 cc (7.2%). The computer-estimated liver volumetrics agreed excellently with the gold-standard manual volumetrics (intraclass correlation coefficient was 0.95) with no statistically significant difference (F=0.77; p(F{<=}f)=0.32). The average accuracy, sensitivity, specificity, and percent volume error were 98.4%, 91.1%, 99.1%, and 7.2%, respectively. Computerized CT liver volumetry would require substantially less

Computer-aided measurement of liver volumes in CT by means of geodesic active contour segmentation coupled with level-set algorithms

International Nuclear Information System (INIS)

Suzuki, Kenji; Kohlbrenner, Ryan; Epstein, Mark L.; Obajuluwa, Ademola M.; Xu Jianwu; Hori, Masatoshi

2010-01-01

Purpose: Computerized liver extraction from hepatic CT images is challenging because the liver often abuts other organs of a similar density. The purpose of this study was to develop a computer-aided measurement of liver volumes in hepatic CT. Methods: The authors developed a computerized liver extraction scheme based on geodesic active contour segmentation coupled with level-set contour evolution. First, an anisotropic diffusion filter was applied to portal-venous-phase CT images for noise reduction while preserving the liver structure, followed by a scale-specific gradient magnitude filter to enhance the liver boundaries. Then, a nonlinear grayscale converter enhanced the contrast of the liver parenchyma. By using the liver-parenchyma-enhanced image as a speed function, a fast-marching level-set algorithm generated an initial contour that roughly estimated the liver shape. A geodesic active contour segmentation algorithm coupled with level-set contour evolution refined the initial contour to define the liver boundaries more precisely. The liver volume was then calculated using these refined boundaries. Hepatic CT scans of 15 prospective liver donors were obtained under a liver transplant protocol with a multidetector CT system. The liver volumes extracted by the computerized scheme were compared to those traced manually by a radiologist, used as ''gold standard.''Results: The mean liver volume obtained with our scheme was 1504 cc, whereas the mean gold standard manual volume was 1457 cc, resulting in a mean absolute difference of 105 cc (7.2%). The computer-estimated liver volumetrics agreed excellently with the gold-standard manual volumetrics (intraclass correlation coefficient was 0.95) with no statistically significant difference (F=0.77; p(F≤f)=0.32). The average accuracy, sensitivity, specificity, and percent volume error were 98.4%, 91.1%, 99.1%, and 7.2%, respectively. Computerized CT liver volumetry would require substantially less completion time

An angle-dependent estimation of CT x-ray spectrum from rotational transmission measurements

International Nuclear Information System (INIS)

Lin, Yuan; Samei, Ehsan; Ramirez-Giraldo, Juan Carlos; Gauthier, Daniel J.; Stierstorfer, Karl

2014-01-01

Purpose: Computed tomography (CT) performance as well as dose and image quality is directly affected by the x-ray spectrum. However, the current assessment approaches of the CT x-ray spectrum require costly measurement equipment and complicated operational procedures, and are often limited to the spectrum corresponding to the center of rotation. In order to address these limitations, the authors propose an angle-dependent estimation technique, where the incident spectra across a wide range of angular trajectories can be estimated accurately with only a single phantom and a single axial scan in the absence of the knowledge of the bowtie filter. Methods: The proposed technique uses a uniform cylindrical phantom, made of ultra-high-molecular-weight polyethylene and positioned in an off-centered geometry. The projection data acquired with an axial scan have a twofold purpose. First, they serve as a reflection of the transmission measurements across different angular trajectories. Second, they are used to reconstruct the cross sectional image of the phantom, which is then utilized to compute the intersection length of each transmission measurement. With each CT detector element recording a range of transmission measurements for a single angular trajectory, the spectrum is estimated for that trajectory. A data conditioning procedure is used to combine information from hundreds of collected transmission measurements to accelerate the estimation speed, to reduce noise, and to improve estimation stability. The proposed spectral estimation technique was validated experimentally using a clinical scanner (Somatom Definition Flash, Siemens Healthcare, Germany) with spectra provided by the manufacturer serving as the comparison standard. Results obtained with the proposed technique were compared against those obtained from a second conventional transmission measurement technique with two materials (i.e., Cu and Al). After validation, the proposed technique was applied to measure

CT and MRI assessment and characterization using segmentation and 3D modeling techniques: applications to muscle, bone and brain

Directory of Open Access Journals (Sweden)

Paolo Gargiulo

2014-03-01

Full Text Available This paper reviews the novel use of CT and MRI data and image processing tools to segment and reconstruct tissue images in 3D to determine characteristics of muscle, bone and brain.This to study and simulate the structural changes occurring in healthy and pathological conditions as well as in response to clinical treatments. Here we report the application of this methodology to evaluate and quantify: 1. progression of atrophy in human muscle subsequent to permanent lower motor neuron (LMN denervation, 2. muscle recovery as induced by functional electrical stimulation (FES, 3. bone quality in patients undergoing total hip replacement and 4. to model the electrical activity of the brain. Study 1: CT data and segmentation techniques were used to quantify changes in muscle density and composition by associating the Hounsfield unit values of muscle, adipose and fibrous connective tissue with different colors. This method was employed to monitor patients who have permanent muscle LMN denervation in the lower extremities under two different conditions: permanent LMN denervated not electrically stimulated and stimulated. Study 2: CT data and segmentation techniques were employed, however, in this work we assessed bone and muscle conditions in the pre-operative CT scans of patients scheduled to undergo total hip replacement. In this work, the overall anatomical structure, the bone mineral density (BMD and compactness of quadriceps muscles and proximal femoral was computed to provide a more complete view for surgeons when deciding which implant technology to use. Further, a Finite element analysis provided a map of the strains around the proximal femur socket when solicited by typical stresses caused by an implant press fitting. Study 3 describes a method to model the electrical behavior of human brain using segmented MR images. The aim of the work is to use these models to predict the electrical activity of the human brain under normal and pathological

Juxta-Vascular Pulmonary Nodule Segmentation in PET-CT Imaging Based on an LBF Active Contour Model with Information Entropy and Joint Vector

Directory of Open Access Journals (Sweden)

Rui Hao

2018-01-01

Full Text Available The accurate segmentation of pulmonary nodules is an important preprocessing step in computer-aided diagnoses of lung cancers. However, the existing segmentation methods may cause the problem of edge leakage and cannot segment juxta-vascular pulmonary nodules accurately. To address this problem, a novel automatic segmentation method based on an LBF active contour model with information entropy and joint vector is proposed in this paper. Our method extracts the interest area of pulmonary nodules by a standard uptake value (SUV in Positron Emission Tomography (PET images, and automatic threshold iteration is used to construct an initial contour roughly. The SUV information entropy and the gray-value joint vector of Positron Emission Tomography–Computed Tomography (PET-CT images are calculated to drive the evolution of contour curve. At the edge of pulmonary nodules, evolution will be stopped and accurate results of pulmonary nodule segmentation can be obtained. Experimental results show that our method can achieve 92.35% average dice similarity coefficient, 2.19 mm Hausdorff distance, and 3.33% false positive with the manual segmentation results. Compared with the existing methods, our proposed method that segments juxta-vascular pulmonary nodules in PET-CT images is more accurate and efficient.

Automatic quantification of mammary glands on non-contrast x-ray CT by using a novel segmentation approach

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Zhou, Xiangrong; Kano, Takuya; Cai, Yunliang; Li, Shuo; Zhou, Xinxin; Hara, Takeshi; Yokoyama, Ryujiro; Fujita, Hiroshi

2016-03-01

This paper describes a brand new automatic segmentation method for quantifying volume and density of mammary gland regions on non-contrast CT images. The proposed method uses two processing steps: (1) breast region localization, and (2) breast region decomposition to accomplish a robust mammary gland segmentation task on CT images. The first step detects two minimum bounding boxes of left and right breast regions, respectively, based on a machine-learning approach that adapts to a large variance of the breast appearances on different age levels. The second step divides the whole breast region in each side into mammary gland, fat tissue, and other regions by using spectral clustering technique that focuses on intra-region similarities of each patient and aims to overcome the image variance caused by different scan-parameters. The whole approach is designed as a simple structure with very minimum number of parameters to gain a superior robustness and computational efficiency for real clinical setting. We applied this approach to a dataset of 300 CT scans, which are sampled with the equal number from 30 to 50 years-old-women. Comparing to human annotations, the proposed approach can measure volume and quantify distributions of the CT numbers of mammary gland regions successfully. The experimental results demonstrated that the proposed approach achieves results consistent with manual annotations. Through our proposed framework, an efficient and effective low cost clinical screening scheme may be easily implemented to predict breast cancer risk, especially on those already acquired scans.

Dosimetric impact of dual-energy CT tissue segmentation for low-energy prostate brachytherapy: a Monte Carlo study

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Remy, Charlotte; Lalonde, Arthur; Béliveau-Nadeau, Dominic; Carrier, Jean-François; Bouchard, Hugo

2018-01-01

The purpose of this study is to evaluate the impact of a novel tissue characterization method using dual-energy over single-energy computed tomography (DECT and SECT) on Monte Carlo (MC) dose calculations for low-dose rate (LDR) prostate brachytherapy performed in a patient like geometry. A virtual patient geometry is created using contours from a real patient pelvis CT scan, where known elemental compositions and varying densities are overwritten in each voxel. A second phantom is made with additional calcifications. Both phantoms are the ground truth with which all results are compared. Simulated CT images are generated from them using attenuation coefficients taken from the XCOM database with a 100 kVp spectrum for SECT and 80 and 140Sn kVp for DECT. Tissue segmentation for Monte Carlo dose calculation is made using a stoichiometric calibration method for the simulated SECT images. For the DECT images, Bayesian eigentissue decomposition is used. A LDR prostate brachytherapy plan is defined with 125I sources and then calculated using the EGSnrc user-code Brachydose for each case. Dose distributions and dose-volume histograms (DVH) are compared to ground truth to assess the accuracy of tissue segmentation. For noiseless images, DECT-based tissue segmentation outperforms the SECT procedure with a root mean square error (RMS) on relative errors on dose distributions respectively of 2.39% versus 7.77%, and provides DVHs closest to the reference DVHs for all tissues. For a medium level of CT noise, Bayesian eigentissue decomposition still performs better on the overall dose calculation as the RMS error is found to be of 7.83% compared to 9.15% for SECT. Both methods give a similar DVH for the prostate while the DECT segmentation remains more accurate for organs at risk and in presence of calcifications, with less than 5% of RMS errors within the calcifications versus up to 154% for SECT. In a patient-like geometry, DECT-based tissue segmentation provides dose

Automated segmentation of muscle and adipose tissue on CT images for human body composition analysis

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Chung, Howard; Cobzas, Dana; Birdsell, Laura; Lieffers, Jessica; Baracos, Vickie

2009-02-01

The ability to compute body composition in cancer patients lends itself to determining the specific clinical outcomes associated with fat and lean tissue stores. For example, a wasting syndrome of advanced disease associates with shortened survival. Moreover, certain tissue compartments represent sites for drug distribution and are likely determinants of chemotherapy efficacy and toxicity. CT images are abundant, but these cannot be fully exploited unless there exist practical and fast approaches for tissue quantification. Here we propose a fully automated method for segmenting muscle, visceral and subcutaneous adipose tissues, taking the approach of shape modeling for the analysis of skeletal muscle. Muscle shape is represented using PCA encoded Free Form Deformations with respect to a mean shape. The shape model is learned from manually segmented images and used in conjunction with a tissue appearance prior. VAT and SAT are segmented based on the final deformed muscle shape. In comparing the automatic and manual methods, coefficients of variation (COV) (1 - 2%), were similar to or smaller than inter- and intra-observer COVs reported for manual segmentation.

Segmental omental infarction in childhood: a typical case diagnosed by CT allowing successful conservative treatment

International Nuclear Information System (INIS)

Coulier, Bruno

2006-01-01

Segmental omental infarction (SOI) is an uncommon cause of right lower quadrant pain in children that is often misdiagnosed as appendicitis. During the last decade, imaging findings of SOI have proved to be sufficiently typical to avoid unnecessary surgery in the majority of reported adult patients. The condition has a spontaneous favourable evolution under medical treatment. In children the surgical option remains controversial. We report a typical case of SOI in a 10-year-old boy. The diagnosis was suspected by sonography, unambiguously confirmed by multidetector CT and successfully treated conservatively. This report emphasizes the use of CT in selected acute abdominal situations, peculiarly in obese children, to avoid unnecessary surgery. (orig.)

Segmentation of urinary bladder in CT urography (CTU) using CLASS with enhanced contour conjoint procedure

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Cha, Kenny; Hadjiiski, Lubomir; Chan, Heang-Ping; Cohan, Richard H.; Caoili, Elaine M.; Zhou, Chuan

2014-03-01

We are developing a computerized method for bladder segmentation in CT urography (CTU) for computeraided diagnosis of bladder cancer. A challenge for computerized bladder segmentation in CTU is that the bladder often contains regions filled with intravenous (IV) contrast and without contrast. Previously, we proposed a Conjoint Level set Analysis and Segmentation System (CLASS) consisting of four stages: preprocessing and initial segmentation, 3D and 2D level set segmentation, and post-processing. In case the bladder is partially filled with contrast, CLASS segments the non-contrast (NC) region and the contrast (C) filled region separately and conjoins the contours with a Contour Conjoint Procedure (CCP). The CCP is not trivial. Inaccuracies in the NC and C contours may cause CCP to exclude portions of the bladder. To alleviate this problem, we implemented model-guided refinement to propagate the C contour if the level set propagation in the region stops prematurely due to substantial non-uniformity of the contrast. An enhanced CCP with regularized energies further propagates the conjoint contours to the correct bladder boundary. Segmentation performance was evaluated using 70 cases. For all cases, 3D hand segmented contours were obtained as reference standard, and computerized segmentation accuracy was evaluated in terms of average volume intersection %, average % volume error, and average minimum distance. With enhanced CCP, those values were 84.4±10.6%, 8.3±16.1%, 3.4±1.8 mm, respectively. With CLASS, those values were 74.6±13.1%, 19.6±18.6%, 4.4±2.2 mm, respectively. The enhanced CCP improved bladder segmentation significantly (p<0.001) for all three performance measures.

Comparison of CLASS and ITK-SNAP in segmentation of urinary bladder in CT urography

Science.gov (United States)

Cha, Kenny; Hadjiiski, Lubomir; Chan, Heang-Ping; Caoili, Elaine M.; Cohan, Richard H.; Zhou, Chuan

2014-03-01

We are developing a computerized method for bladder segmentation in CT urography (CTU) for computeraided diagnosis of bladder cancer. We have developed a Conjoint Level set Analysis and Segmentation System (CLASS) consisting of four stages: preprocessing and initial segmentation, 3D and 2D level set segmentation, and post-processing. In case the bladder contains regions filled with intravenous (IV) contrast and without contrast, CLASS segments the noncontrast (NC) region and the contrast (C) filled region separately and conjoins the contours. In this study, we compared the performance of CLASS to ITK-SNAP 2.4, which is a publicly available software application for segmentation of structures in 3D medical images. ITK-SNAP performs segmentation by using the edge-based level set on preprocessed images. The level set were initialized by manually placing a sphere at the boundary between the C and NC parts of the bladders with C and NC regions, and in the middle of the bladders that had only C or NC region. Level set parameters and the number of iterations were chosen after experimentation with bladder cases. Segmentation performances were compared using 30 randomly selected bladders. 3D hand-segmented contours were obtained as reference standard, and computerized segmentation accuracy was evaluated in terms of the average volume intersection %, average % volume error, average absolute % volume error, average minimum distance, and average Jaccard index. For CLASS, the values for these performance metrics were 79.0±8.2%, 16.1±16.3%, 19.9±11.1%, 3.5±1.3 mm, 75.7±8.4%, respectively. For ITK-SNAP, the corresponding values were 78.8±8.2%, 8.3±33.1%, 24.2±23.7%, 5.2±2.6 mm, 71.0±15.4%, respectively. CLASS on average performed better and exhibited less variations than ITK-SNAP for bladder segmentation.

A method for robust segmentation of arbitrarily shaped radiopaque structures in cone-beam CT projections

International Nuclear Information System (INIS)

Poulsen, Per Rugaard; Fledelius, Walther; Keall, Paul J.; Weiss, Elisabeth; Lu Jun; Brackbill, Emily; Hugo, Geoffrey D.

2011-01-01

Purpose: Implanted markers are commonly used in radiotherapy for x-ray based target localization. The projected marker position in a series of cone-beam CT (CBCT) projections can be used to estimate the three dimensional (3D) target trajectory during the CBCT acquisition. This has important applications in tumor motion management such as motion inclusive, gating, and tumor tracking strategies. However, for irregularly shaped markers, reliable segmentation is challenged by large variations in the marker shape with projection angle. The purpose of this study was to develop a semiautomated method for robust and reliable segmentation of arbitrarily shaped radiopaque markers in CBCT projections. Methods: The segmentation method involved the following three steps: (1) Threshold based segmentation of the marker in three to six selected projections with large angular separation, good marker contrast, and uniform background; (2) construction of a 3D marker model by coalignment and backprojection of the threshold-based segmentations; and (3) construction of marker templates at all imaging angles by projection of the 3D model and use of these templates for template-based segmentation. The versatility of the segmentation method was demonstrated by segmentation of the following structures in the projections from two clinical CBCT scans: (1) Three linear fiducial markers (Visicoil) implanted in or near a lung tumor and (2) an artificial cardiac valve in a lung cancer patient. Results: Automatic marker segmentation was obtained in more than 99.9% of the cases. The segmentation failed in a few cases where the marker was either close to a structure of similar appearance or hidden behind a dense structure (data cable). Conclusions: A robust template-based method for segmentation of arbitrarily shaped radiopaque markers in CBCT projections was developed.

Automated temporal tracking and segmentation of lymphoma on serial CT examinations

Energy Technology Data Exchange (ETDEWEB)

Xu Jiajing; Greenspan, Hayit; Napel, Sandy; Rubin, Daniel L. [Department of Electrical Engineering, Stanford University, Stanford, California 94305 (United States); Department of Biomedical Engineering, Tel Aviv University, Tel Aviv, 69978 (Israel); Department of Electrical Engineering, Stanford University, Stanford, California 94305 and Department of Radiology, Stanford University, Stanford, California 94305 (United States); Department of Radiology, Stanford University, Stanford, California 94305 (United States)

2011-11-15

Purpose: It is challenging to reproducibly measure and compare cancer lesions on numerous follow-up studies; the process is time-consuming and error-prone. In this paper, we show a method to automatically and reproducibly identify and segment abnormal lymph nodes in serial computed tomography (CT) exams. Methods: Our method leverages initial identification of enlarged (abnormal) lymph nodes in the baseline scan. We then identify an approximate region for the node in the follow-up scans using nonrigid image registration. The baseline scan is also used to locate regions of normal, non-nodal tissue surrounding the lymph node and to map them onto the follow-up scans, in order to reduce the search space to locate the lymph node on the follow-up scans. Adaptive region-growing and clustering algorithms are then used to obtain the final contours for segmentation. We applied our method to 24 distinct enlarged lymph nodes at multiple time points from 14 patients. The scan at the earlier time point was used as the baseline scan to be used in evaluating the follow-up scan, resulting in 70 total test cases (e.g., a series of scans obtained at 4 time points results in 3 test cases). For each of the 70 cases, a ''reference standard'' was obtained by manual segmentation by a radiologist. Assessment according to response evaluation criteria in solid tumors (RECIST) using our method agreed with RECIST assessments made using the reference standard segmentations in all test cases, and by calculating node overlap ratio and Hausdorff distance between the computer and radiologist-generated contours. Results: Compared to the reference standard, our method made the correct RECIST assessment for all 70 cases. The average overlap ratio was 80.7 {+-} 9.7% s.d., and the average Hausdorff distance was 3.2 {+-} 1.8 mm s.d. The concordance correlation between automated and manual segmentations was 0.978 (95% confidence interval 0.962, 0.984). The 100% agreement in our sample

Spine segmentation from C-arm CT data sets: application to region-of-interest volumes for spinal interventions

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Buerger, C.; Lorenz, C.; Babic, D.; Hoppenbrouwers, J.; Homan, R.; Nachabe, R.; Racadio, J. M.; Grass, M.

2017-03-01

Spinal fusion is a common procedure to stabilize the spinal column by fixating parts of the spine. In such procedures, metal screws are inserted through the patients back into a vertebra, and the screws of adjacent vertebrae are connected by metal rods to generate a fixed bridge. In these procedures, 3D image guidance for intervention planning and outcome control is required. Here, for anatomical guidance, an automated approach for vertebra segmentation from C-arm CT images of the spine is introduced and evaluated. As a prerequisite, 3D C-arm CT images are acquired covering the vertebrae of interest. An automatic model-based segmentation approach is applied to delineate the outline of the vertebrae of interest. The segmentation approach is based on 24 partial models of the cervical, thoracic and lumbar vertebrae which aggregate information about (i) the basic shape itself, (ii) trained features for image based adaptation, and (iii) potential shape variations. Since the volume data sets generated by the C-arm system are limited to a certain region of the spine the target vertebra and hence initial model position is assigned interactively. The approach was trained and tested on 21 human cadaver scans. A 3-fold cross validation to ground truth annotations yields overall mean segmentation errors of 0.5 mm for T1 to 1.1 mm for C6. The results are promising and show potential to support the clinician in pedicle screw path and rod planning to allow accurate and reproducible insertions.

TH-C-18A-02: Machine Learning and STAPLE Based Simultaneous Longitudinal Segmentation of Bone and Marrow Structures From Dual Energy CT

International Nuclear Information System (INIS)

Fehr, D; Schmidtlein, C; Hwang, S; Deasy, J; Veeraraghavan, H

2014-01-01

Purpose: To develop a fully-automatic longitudinal bone and marrow segmentation method in the pelvic region from dual energy computed tomography (DECT). Methods: We developed a two-step automatic bone and marrow segmentation method for simultaneous longitudinal evaluation of patients with metastatic bone disease using dual energy CT (DECT). Our approach transforms the DECT images into a multi-material decomposition (MMD) model that represents the voxels as a mixture of multiple materials. A support vector machine (SVM) was trained using a single scan. In the first step of the longitudinal segmentation the trained SVM model detects bone and marrow structures on all available longitudinal scans. Segmentation is further refined through active contour segmentation. In the second step, the segmentations from the individual scans are merged by employing the simultaneous truth and performance level estimation (STAPLE) algorithm. The scans are registered using affine and deformable registration. We found that our approach improves the segmentation in all the scans under reliable registration performance between the same scans. Improving registration was not under the scope of this work. Results: We applied our approach to segment bone and marrow in DECT scans in the pelvic regions for multiple patients. Each patient had three to five follow up scans. All the patients in the analysis had artificial metal prostheses which introduced challenges for the registration. Our algorithm achieved reasonable accurate segmentation despite the presence of metal artifacts and high-density oral contrast in neighboring structures. Our approach obtained an overall segmentation accuracy of 80% using DICE metric. Conclusion: We developed a two-step automatic longitudinal segmentation technique for bone and marrow region structures in the pelvic areas from dual energy CT. Our approach achieves robust segmentation despite the presence of confounding structures with similar intensities as the

SU-E-J-272: Auto-Segmentation of Regions with Differentiating CT Numbers for Treatment Response Assessment

International Nuclear Information System (INIS)

Yang, C; Noid, G; Dalah, E; Paulson, E; Li, X; Gilat-Schmidt, T

2015-01-01

Purpose: It has been reported recently that the change of CT number (CTN) during and after radiation therapy (RT) may be used to assess RT response. The purpose of this work is to develop a tool to automatically segment the regions with differentiating CTN and/or with change of CTN in a series of CTs. Methods: A software tool was developed to identify regions with differentiating CTN using K-mean Cluster of CT numbers and to automatically delineate these regions using convex hull enclosing method. Pre- and Post-RT CT, PET, or MRI images acquired for sample lung and pancreatic cancer cases were used to test the software tool. K-mean cluster of CT numbers within the gross tumor volumes (GTVs) delineated based on PET SUV (standard uptake value of fludeoxyglucose) and/or MRI ADC (apparent diffusion coefficient) map was analyzed. The cluster centers with higher value were considered as active tumor volumes (ATV). The convex hull contours enclosing preset clusters were used to delineate these ATVs with color washed displays. The CTN defined ATVs were compared with the SUV- or ADC-defined ATVs. Results: CTN stability of the CT scanner used to acquire the CTs in this work is less than 1.5 Hounsfield Unit (HU) variation annually. K-mean cluster centers in the GTV have difference of ∼20 HU, much larger than variation due to CTN stability, for the lung cancer cases studied. The dice coefficient between the ATVs delineated based on convex hull enclosure of high CTN centers and the PET defined GTVs based on SUV cutoff value of 2.5 was 90(±5)%. Conclusion: A software tool was developed using K-mean cluster and convex hull contour to automatically segment high CTN regions which may not be identifiable using a simple threshold method. These CTN regions were reasonably overlapped with the PET or MRI defined GTVs

Early small-bowel ischemia: dual-energy CT improves conspicuity compared with conventional CT in a swine model.

Science.gov (United States)

Potretzke, Theodora A; Brace, Christopher L; Lubner, Meghan G; Sampson, Lisa A; Willey, Bridgett J; Lee, Fred T

2015-04-01

To compare dual-energy computed tomography (CT) with conventional CT for the detection of small-bowel ischemia in an experimental animal model. The study was approved by the animal care and use committee and was performed in accordance with the Guide for Care and Use of Laboratory Animals issued by the National Research Council. Ischemic bowel segments (n = 8) were created in swine (n = 4) by means of surgical occlusion of distal mesenteric arteries and veins. Contrast material-enhanced dual-energy CT and conventional single-energy CT (120 kVp) sequences were performed during the portal venous phase with a single-source fast-switching dual-energy CT scanner. Attenuation values and contrast-to-noise ratios of ischemic and perfused segments on iodine material-density, monospectral dual-energy CT (51 keV, 65 keV, and 70 keV), and conventional 120-kVp CT images were compared. Linear mixed-effects models were used for comparisons. The attenuation difference between ischemic and perfused segments was significantly greater on dual-energy 51-keV CT images than on conventional 120-kVp CT images (mean difference, 91.7 HU vs 47.6 HU; P conventional CT by increasing attenuation differences between ischemic and perfused segments on low-kiloelectron volt and iodine material density images. © RSNA, 2014.

Development of an automated 3D segmentation program for volume quantification of body fat distribution using CT

International Nuclear Information System (INIS)

Ohshima, Shunsuke; Yamamoto, Shuji; Yamaji, Taiki

2008-01-01

The objective of this study was to develop a computing tool for full-automatic segmentation of body fat distributions on volumetric CT images. We developed an algorithm to automatically identify the body perimeter and the inner contour that separates visceral fat from subcutaneous fat. Diaphragmatic surfaces can be extracted by model-based segmentation to match the bottom surface of the lung in CT images for determination of the upper limitation of the abdomen. The functions for quantitative evaluation of abdominal obesity or obesity-related metabolic syndrome were implemented with a prototype three-dimensional (3D) image processing workstation. The volumetric ratios of visceral fat to total fat and visceral fat to subcutaneous fat for each subject can be calculated. Additionally, color intensity mapping of subcutaneous areas and the visceral fat layer is quite obvious in understanding the risk of abdominal obesity with the 3D surface display. Preliminary results obtained have been useful in medical checkups and have contributed to improved efficiency in checking obesity throughout the whole range of the abdomen with 3D visualization and analysis. (author)

MO-C-17A-11: A Segmentation and Point Matching Enhanced Deformable Image Registration Method for Dose Accumulation Between HDR CT Images

International Nuclear Information System (INIS)

Zhen, X; Chen, H; Zhou, L; Yan, H; Jiang, S; Jia, X; Gu, X; Mell, L; Yashar, C; Cervino, L

2014-01-01

Purpose: To propose and validate a novel and accurate deformable image registration (DIR) scheme to facilitate dose accumulation among treatment fractions of high-dose-rate (HDR) gynecological brachytherapy. Method: We have developed a method to adapt DIR algorithms to gynecologic anatomies with HDR applicators by incorporating a segmentation step and a point-matching step into an existing DIR framework. In the segmentation step, random walks algorithm is used to accurately segment and remove the applicator region (AR) in the HDR CT image. A semi-automatic seed point generation approach is developed to obtain the incremented foreground and background point sets to feed the random walks algorithm. In the subsequent point-matching step, a feature-based thin-plate spline-robust point matching (TPS-RPM) algorithm is employed for AR surface point matching. With the resulting mapping, a DVF characteristic of the deformation between the two AR surfaces is generated by B-spline approximation, which serves as the initial DVF for the following Demons DIR between the two AR-free HDR CT images. Finally, the calculated DVF via Demons combined with the initial one serve as the final DVF to map doses between HDR fractions. Results: The segmentation and registration accuracy are quantitatively assessed by nine clinical HDR cases from three gynecological cancer patients. The quantitative results as well as the visual inspection of the DIR indicate that our proposed method can suppress the interference of the applicator with the DIR algorithm, and accurately register HDR CT images as well as deform and add interfractional HDR doses. Conclusions: We have developed a novel and robust DIR scheme that can perform registration between HDR gynecological CT images and yield accurate registration results. This new DIR scheme has potential for accurate interfractional HDR dose accumulation. This work is supported in part by the National Natural ScienceFoundation of China (no 30970866 and no

Engineering a segmented dual-reservoir polyurethane intravaginal ring for simultaneous prevention of HIV transmission and unwanted pregnancy.

Directory of Open Access Journals (Sweden)

Justin T Clark

Full Text Available The HIV/AIDS pandemic and its impact on women prompt the investigation of prevention strategies to interrupt sexual transmission of HIV. Long-acting drug delivery systems that simultaneously protect womenfrom sexual transmission of HIV and unwanted pregnancy could be important tools in combating the pandemic. We describe the design, in silico, in vitro and in vivo evaluation of a dual-reservoir intravaginal ring that delivers the HIV-1 reverse transcriptase inhibitor tenofovir and the contraceptive levonorgestrel for 90 days. Two polyether urethanes with two different hard segment volume fractions were used to make coaxial extruded reservoir segments with a 100 µm thick rate controlling membrane and a diameter of 5.5 mm that contain 1.3 wt% levonorgestrel. A new mechanistic diffusion model accurately described the levonorgestrel burst release in early time points and pseudo-steady state behavior at later time points. As previously described, tenofovir was formulated as a glycerol paste and filled into a hydrophilic polyurethane, hollow tube reservoir that was melt-sealed by induction welding. These tenofovir-eluting segments and 2 cm long coaxially extruded levonorgestrel eluting segments were joined by induction welding to form rings that released an average of 7.5 mg tenofovir and 21 µg levonorgestrel per day in vitro for 90 days. Levonorgestrel segments placed intravaginally in rabbits resulted in sustained, dose-dependent levels of levonorgestrel in plasma and cervical tissue for 90 days. Polyurethane caps placed between segments successfully prevented diffusion of levonorgestrel into the tenofovir-releasing segment during storage.Hydrated rings endured between 152 N and 354 N tensile load before failure during uniaxial extension testing. In summary, this system represents a significant advance in vaginal drug delivery technology, and is the first in a new class of long-acting multipurpose prevention drug delivery systems.

Acquisition and automated 3-D segmentation of respiratory/cardiac-gated PET transmission images

International Nuclear Information System (INIS)

Reutter, B.W.; Klein, G.J.; Brennan, K.M.; Huesman, R.H.

1996-01-01

To evaluate the impact of respiratory motion on attenuation correction of cardiac PET data, we acquired and automatically segmented gated transmission data for a dog breathing on its own under gas anesthesia. Data were acquired for 20 min on a CTI/Siemens ECAT EXACT HR (47-slice) scanner configured for 12 gates in a static study, Two respiratory gates were obtained using data from a pneumatic bellows placed around the dog's chest, in conjunction with 6 cardiac gates from standard EKG gating. Both signals were directed to a LabVIEW-controlled Macintosh, which translated them into one of 12 gate addresses. The respiratory gating threshold was placed near end-expiration to acquire 6 cardiac-gated datasets at end-expiration and 6 cardiac-gated datasets during breaths. Breaths occurred about once every 10 sec and lasted about 1-1.5 sec. For each respiratory gate, data were summed over cardiac gates and torso and lung surfaces were segmented automatically using a differential 3-D edge detection algorithm. Three-dimensional visualizations showed that lung surfaces adjacent to the heart translated 9 mm inferiorly during breaths. Our results suggest that respiration-compensated attenuation correction is feasible with a modest amount of gated transmission data and is necessary for accurate quantitation of high-resolution gated cardiac PET data

Rate Adaptive Selective Segment Assignment for Reliable Wireless Video Transmission

Directory of Open Access Journals (Sweden)

Sajid Nazir

2012-01-01

Full Text Available A reliable video communication system is proposed based on data partitioning feature of H.264/AVC, used to create a layered stream, and LT codes for erasure protection. The proposed scheme termed rate adaptive selective segment assignment (RASSA is an adaptive low-complexity solution to varying channel conditions. The comparison of the results of the proposed scheme is also provided for slice-partitioned H.264/AVC data. Simulation results show competitiveness of the proposed scheme compared to optimized unequal and equal error protection solutions. The simulation results also demonstrate that a high visual quality video transmission can be maintained despite the adverse effect of varying channel conditions and the number of decoding failures can be reduced.

Metal artifact reduction of CT scans to improve PET/CT

NARCIS (Netherlands)

Van Der Vos, Charlotte S.; Arens, Anne I.J.; Hamill, James J.; Hofmann, Christian; Panin, Vladimir Y.; Meeuwis, Antoi P.W.; Visser, Eric P.; De Geus-Oei, Lioe Fee

2017-01-01

In recent years, different metal artifact reduction methods have been developed for CT. These methods have only recently been introduced for PET/CT even though they could be beneficial for interpretation, segmentation, and quantification of the PET/CT images. In this study, phantom and patient scans

Metal Artifact Reduction of CT Scans to Improve PET/CT

NARCIS (Netherlands)

Vos, C.S. van der; Arens, A.I.J.; Hamill, J.J.; Hofmann, C.; Panin, V.Y.; Meeuwis, A.P.W.; Visser, E.P.; Geus-Oei, L.F. de

2017-01-01

In recent years, different metal artifact reduction methods have been developed for CT. These methods have only recently been introduced for PET/CT even though they could be beneficial for interpretation, segmentation, and quantification of the PET/CT images. In this study, phantom and patient scans

WE-AB-303-05: Breathing Motion of Liver Segments From Fiducial Tracking During Robotic Radiosurgery and Comparison with 4D-CT-Derived Fiducial Motion

International Nuclear Information System (INIS)

Sutherland, J; Pantarotto, J; Nair, V; Cook, G; Plourde, M; Vandervoort, E

2015-01-01

Purpose: To quantify respiratory-induced motion of liver segments using the positions of implanted fiducials during robotic radiosurgery. This study also compared fiducial motion derived from four-dimensional computed tomography (4D-CT) maximum intensity projections (MIP) with motion derived from imaging during treatment. Methods: Forty-two consecutive liver patients treated with liver ablative radiotherapy were accrued to an ethics approved retrospective study. The liver segment in which each fiducial resided was identified. Fiducial positions throughout each treatment fraction were determined using orthogonal kilovoltage images. Any data due to patient repositioning or motion was removed. Mean fiducial positions were calculated. Fiducial positions beyond two standard deviations of the mean were discarded and remaining positions were fit to a line segment using least squares minimization (LSM). For eight patients, fiducial motion was derived from 4D-CT MIPs by calculating the CT number weighted mean position of the fiducial on each slice and fitting a line segment to these points using LSM. Treatment derived fiducial trajectories were corrected for patient rotation and compared to MIP derived trajectories. Results: The mean total magnitude of fiducial motion across all liver segments in left-right, anteroposterior, and superoinferior (SI) directions were 3.0 ± 0.2 mm, 9.3 ± 0.4 mm, and 20.5 ± 0.5 mm, respectively. Differences in per-segment mean fiducial motion were found with SI motion ranging from 12.6 ± 0.8 mm to 22.6 ± 0.9 mm for segments 3 and 8, respectively. Large, varied differences between treatment and MIP derived motion at simulation were found with the mean difference for SI motion being 2.6 mm (10.8 mm standard deviation). Conclusion: The magnitude of liver fiducial motion was found to differ by liver segment. MIP derived liver fiducial motion differed from motion observed during treatment, implying that 4D-CTs may not accurately capture the

WE-AB-303-05: Breathing Motion of Liver Segments From Fiducial Tracking During Robotic Radiosurgery and Comparison with 4D-CT-Derived Fiducial Motion

Energy Technology Data Exchange (ETDEWEB)

Sutherland, J; Pantarotto, J; Nair, V; Cook, G; Plourde, M; Vandervoort, E [The Ottawa Hospital Cancer Centre, Ottawa, Ontario (Canada)

2015-06-15

Purpose: To quantify respiratory-induced motion of liver segments using the positions of implanted fiducials during robotic radiosurgery. This study also compared fiducial motion derived from four-dimensional computed tomography (4D-CT) maximum intensity projections (MIP) with motion derived from imaging during treatment. Methods: Forty-two consecutive liver patients treated with liver ablative radiotherapy were accrued to an ethics approved retrospective study. The liver segment in which each fiducial resided was identified. Fiducial positions throughout each treatment fraction were determined using orthogonal kilovoltage images. Any data due to patient repositioning or motion was removed. Mean fiducial positions were calculated. Fiducial positions beyond two standard deviations of the mean were discarded and remaining positions were fit to a line segment using least squares minimization (LSM). For eight patients, fiducial motion was derived from 4D-CT MIPs by calculating the CT number weighted mean position of the fiducial on each slice and fitting a line segment to these points using LSM. Treatment derived fiducial trajectories were corrected for patient rotation and compared to MIP derived trajectories. Results: The mean total magnitude of fiducial motion across all liver segments in left-right, anteroposterior, and superoinferior (SI) directions were 3.0 ± 0.2 mm, 9.3 ± 0.4 mm, and 20.5 ± 0.5 mm, respectively. Differences in per-segment mean fiducial motion were found with SI motion ranging from 12.6 ± 0.8 mm to 22.6 ± 0.9 mm for segments 3 and 8, respectively. Large, varied differences between treatment and MIP derived motion at simulation were found with the mean difference for SI motion being 2.6 mm (10.8 mm standard deviation). Conclusion: The magnitude of liver fiducial motion was found to differ by liver segment. MIP derived liver fiducial motion differed from motion observed during treatment, implying that 4D-CTs may not accurately capture the

Regional deep hyperthermia: impact of observer variability in CT-based manual tissue segmentation on simulated temperature distribution

Science.gov (United States)

Aklan, Bassim; Hartmann, Josefin; Zink, Diana; Siavooshhaghighi, Hadi; Merten, Ricarda; Putz, Florian; Ott, Oliver; Fietkau, Rainer; Bert, Christoph

2017-06-01

The aim of this study was to systematically investigate the influence of the inter- and intra-observer segmentation variation of tumors and organs at risk on the simulated temperature coverage of the target. CT scans of six patients with tumors in the pelvic region acquired for radiotherapy treatment planning were used for hyperthermia treatment planning. To study the effect of inter-observer variation, three observers manually segmented in the CT images of each patient the following structures: fat, muscle, bone and the bladder. The gross tumor volumes (GTV) were contoured by three radiation oncology residents and used as the hyperthermia target volumes. For intra-observer variation, one of the observers of each group contoured the structures of each patient three times with a time span of one week between the segmentations. Moreover, the impact of segmentation variations in organs at risk (OARs) between the three inter-observers was investigated on simulated temperature distributions using only one GTV. The spatial overlap between individual segmentations was assessed by the Dice similarity coefficient (DSC) and the mean surface distance (MSD). Additionally, the temperatures T90/T10 delivered to 90%/10% of the GTV, respectively, were assessed for each observer combination. The results of the segmentation similarity evaluation showed that the DSC of the inter-observer variation of fat, muscle, the bladder, bone and the target was 0.68  ±  0.12, 0.88  ±  0.05, 0.73  ±  0.14, 0.91  ±  0.04 and 0.64  ±  0.11, respectively. Similar results were found for the intra-observer variation. The MSD results were similar to the DSCs for both observer variations. A statistically significant difference (p  <  0.05) was found for T90 and T10 in the predicted target temperature due to the observer variability. The conclusion is that intra- and inter-observer variations have a significant impact on the temperature coverage of the

Lung segment geometry study: simulation of largest possible tumours that fit into bronchopulmonary segments.

Science.gov (United States)

Welter, S; Stöcker, C; Dicken, V; Kühl, H; Krass, S; Stamatis, G

2012-03-01

Segmental resection in stage I non-small cell lung cancer (NSCLC) has been well described and is considered to have similar survival rates as lobectomy but with increased rates of local tumour recurrence due to inadequate parenchymal margins. In consequence, today segmentectomy is only performed when the tumour is smaller than 2 cm. Three-dimensional reconstructions from 11 thin-slice CT scans of bronchopulmonary segments were generated, and virtual spherical tumours were placed over the segments, respecting all segmental borders. As a next step, virtual parenchymal safety margins of 2 cm and 3 cm were subtracted and the size of the remaining tumour calculated. The maximum tumour diameters with a 30-mm parenchymal safety margin ranged from 26.1 mm in right-sided segments 7 + 8 to 59.8 mm in the left apical segments 1-3. Using a three-dimensional reconstruction of lung CT scans, we demonstrated that segmentectomy or resection of segmental groups should be feasible with adequate margins, even for larger tumours in selected cases. Thieme Medical Publishers 333 Seventh Avenue, New York, NY 10001, USA.

Semiautomatic methods for segmentation of the proliferative tumour volume on sequential FLT PET/CT images in head and neck carcinomas and their relation to clinical outcome

Energy Technology Data Exchange (ETDEWEB)

Arens, Anne I.J.; Grootjans, Willem; Oyen, Wim J.G.; Visser, Eric P. [Radboud University Medical Center, Department of Nuclear Medicine, P.O. Box 9101, Nijmegen (Netherlands); Troost, Esther G.C. [Radboud University Medical Center, Department of Radiation Oncology, Nijmegen (Netherlands); Maastricht University Medical Centre, MAASTRO clinic, GROW School for Oncology and Developmental Biology, Maastricht (Netherlands); Hoeben, Bianca A.W.; Bussink, Johan; Kaanders, Johannes H.A.M. [Radboud University Medical Center, Department of Radiation Oncology, Nijmegen (Netherlands); Lee, John A.; Gregoire, Vincent [St-Luc University Hospital, Department of Radiation Oncology, Universite Catholique de Louvain, Brussels (Belgium); Hatt, Mathieu; Visvikis, Dimitris [Laboratoire de Traitement de l' Information Medicale (LaTIM), INSERM UMR1101, Brest (France)

2014-05-15

Radiotherapy of head and neck cancer induces changes in tumour cell proliferation during treatment, which can be depicted by the PET tracer {sup 18}F-fluorothymidine (FLT). In this study, three advanced semiautomatic PET segmentation methods for delineation of the proliferative tumour volume (PV) before and during (chemo)radiotherapy were compared and related to clinical outcome. The study group comprised 46 patients with 48 squamous cell carcinomas of the head and neck, treated with accelerated (chemo)radiotherapy, who underwent FLT PET/CT prior to treatment and in the 2nd and 4th week of therapy. Primary gross tumour volumes were visually delineated on CT images (GTV{sub CT}). PVs were visually determined on all PET scans (PV{sub VIS}). The following semiautomatic segmentation methods were applied to sequential PET scans: background-subtracted relative-threshold level (PV{sub RTL}), a gradient-based method using the watershed transform algorithm and hierarchical clustering analysis (PV{sub W} and {sub C}), and a fuzzy locally adaptive Bayesian algorithm (PV{sub FLAB}). Pretreatment PV{sub VIS} correlated best with PV{sub FLAB} and GTV{sub CT}. Correlations with PV{sub RTL} and PV{sub W} and {sub C} were weaker although statistically significant. During treatment, the PV{sub VIS}, PV{sub W} and {sub C} and PV{sub FLAB} significant decreased over time with the steepest decline over time for PV{sub FLAB}. Among these advanced segmentation methods, PV{sub FLAB} was the most robust in segmenting volumes in the third scan (67 % of tumours as compared to 40 % for PV{sub W} and {sub C} and 27 % for PV{sub RTL}). A decrease in PV{sub FLAB} above the median between the pretreatment scan and the scan obtained in the 4th week was associated with better disease-free survival (4 years 90 % versus 53 %). In patients with head and neck cancer, FLAB proved to be the best performing method for segmentation of the PV on repeat FLT PET/CT scans during (chemo)radiotherapy. This may

Computer-based automated left atrium segmentation and volumetry from ECG-gated coronary CT angiography data. Comparison with manual slice segmentation and ultrasound planimetric methods

Energy Technology Data Exchange (ETDEWEB)

Bauer, R.W.; Kraus, B.; Kerl, J.M.; Lehnert, T.; Vogl, T.J. [Universitaetsklinikum Frankfurt (Germany). Inst. fuer Diagnostische und Interventionelle Radiologie; Bernhardt, D.; Vega-Higuera, F. [Siemens AG, Healthcare Sector, Forchheim (Germany). Computed Tomography; Ackermann, H. [Universitaetsklinikum Frankfurt (Germany). Inst. fuer Biostatistik und Mathematische Modellierung

2010-12-15

Purpose: Enlargement of the left atrium is a risk factor for cardiovascular or cerebrovascular events. We evaluated the performance of prototype software for fully automated segmentation and volumetry of the left atrium. Materials and Methods: In 34 retrospectively ECG-gated coronary CT angiography scans, the end-systolic (LAVsys) and end-diastolic (LAVdia) volume of the left atrium was calculated fully automatically by prototype software. Manual slice segmentation by two independent experienced radiologists served as the reference standard. Furthermore, two independent observers calculated the LAV utilizing two ultrasound planimetric methods ('area length' and 'prolate ellipse') on CTA images. Measurement periods were compared for all methods. Results: The left atrial volumes calculated with the prototype software were in excellent agreement with the results from manual slice segmentation (r = 0.97 - 0.99; p < 0.001; Bland-Altman) with excellent interobserver agreement between both radiologists (r = 0.99; p < 0.001). Ultrasound planimetric methods clearly showed a higher variation (r = 0.72 - 0.86) with moderate interobserver agreement (r = 0.51 - 0.79). The measurement period was significantly lower with the software (267 {+-} 28 sec; p < 0.001) than with ultrasound methods (431 {+-} 68 sec) or manual slice segmentation (567 {+-} 91 sec). Conclusion: The prototype software showed excellent agreement with manual slice segmentation with the least time consumption. This will facilitate the routine assessment of the LA volume from coronary CTA data and therefore risk stratification. (orig.)

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

Segmentation-less Digital Rock Physics

Science.gov (United States)

Tisato, N.; Ikeda, K.; Goldfarb, E. J.; Spikes, K. T.

2017-12-01

In the last decade, Digital Rock Physics (DRP) has become an avenue to investigate physical and mechanical properties of geomaterials. DRP offers the advantage of simulating laboratory experiments on numerical samples that are obtained from analytical methods. Potentially, DRP could allow sparing part of the time and resources that are allocated to perform complicated laboratory tests. Like classic laboratory tests, the goal of DRP is to estimate accurately physical properties of rocks like hydraulic permeability or elastic moduli. Nevertheless, the physical properties of samples imaged using micro-computed tomography (μCT) are estimated through segmentation of the μCT dataset. Segmentation proves to be a challenging and arbitrary procedure that typically leads to inaccurate estimates of physical properties. Here we present a novel technique to extract physical properties from a μCT dataset without the use of segmentation. We show examples in which we use segmentation-less method to simulate elastic wave propagation and pressure wave diffusion to estimate elastic properties and permeability, respectively. The proposed method takes advantage of effective medium theories and uses the density and the porosity that are measured in the laboratory to constrain the results. We discuss the results and highlight that segmentation-less DRP is more accurate than segmentation based DRP approaches and theoretical modeling for the studied rock. In conclusion, the segmentation-less approach here presented seems to be a promising method to improve accuracy and to ease the overall workflow of DRP.

Attenuation correction for the HRRT PET-scanner using transmission scatter correction and total variation regularization

DEFF Research Database (Denmark)

Keller, Sune H; Svarer, Claus; Sibomana, Merence

2013-01-01

scatter correction in the μ-map reconstruction and total variation filtering to the transmission processing. Results: Comparing MAP-TR and the new TXTV with gold standard CT-based attenuation correction, we found that TXTV has less bias as compared to MAP-TR. We also compared images acquired at the HRRT......In the standard software for the Siemens high-resolution research tomograph (HRRT) positron emission tomography (PET) scanner the most commonly used segmentation in the μ -map reconstruction for human brain scans is maximum a posteriori for transmission (MAP-TR). Bias in the lower cerebellum...

Review methods for image segmentation from computed tomography images

International Nuclear Information System (INIS)

Mamat, Nurwahidah; Rahman, Wan Eny Zarina Wan Abdul; Soh, Shaharuddin Cik; Mahmud, Rozi

2014-01-01

Image segmentation is a challenging process in order to get the accuracy of segmentation, automation and robustness especially in medical images. There exist many segmentation methods that can be implemented to medical images but not all methods are suitable. For the medical purposes, the aims of image segmentation are to study the anatomical structure, identify the region of interest, measure tissue volume to measure growth of tumor and help in treatment planning prior to radiation therapy. In this paper, we present a review method for segmentation purposes using Computed Tomography (CT) images. CT images has their own characteristics that affect the ability to visualize anatomic structures and pathologic features such as blurring of the image and visual noise. The details about the methods, the goodness and the problem incurred in the methods will be defined and explained. It is necessary to know the suitable segmentation method in order to get accurate segmentation. This paper can be a guide to researcher to choose the suitable segmentation method especially in segmenting the images from CT scan

ACM-based automatic liver segmentation from 3-D CT images by combining multiple atlases and improved mean-shift techniques.

Science.gov (United States)

Ji, Hongwei; He, Jiangping; Yang, Xin; Deklerck, Rudi; Cornelis, Jan

2013-05-01

In this paper, we present an autocontext model(ACM)-based automatic liver segmentation algorithm, which combines ACM, multiatlases, and mean-shift techniques to segment liver from 3-D CT images. Our algorithm is a learning-based method and can be divided into two stages. At the first stage, i.e., the training stage, ACM is performed to learn a sequence of classifiers in each atlas space (based on each atlas and other aligned atlases). With the use of multiple atlases, multiple sequences of ACM-based classifiers are obtained. At the second stage, i.e., the segmentation stage, the test image will be segmented in each atlas space by applying each sequence of ACM-based classifiers. The final segmentation result will be obtained by fusing segmentation results from all atlas spaces via a multiclassifier fusion technique. Specially, in order to speed up segmentation, given a test image, we first use an improved mean-shift algorithm to perform over-segmentation and then implement the region-based image labeling instead of the original inefficient pixel-based image labeling. The proposed method is evaluated on the datasets of MICCAI 2007 liver segmentation challenge. The experimental results show that the average volume overlap error and the average surface distance achieved by our method are 8.3% and 1.5 m, respectively, which are comparable to the results reported in the existing state-of-the-art work on liver segmentation.

A “loop” shape descriptor and its application to automated segmentation of airways from CT scans

Energy Technology Data Exchange (ETDEWEB)

Pu, Jiantao [Department of Radiology, First Affiliated Hospital of Medical College, Xi’an Jiaotong University, Shaanxi 710061, People’s Republic of China, and Departments of Radiology and Bioengineering, University of Pittsburgh, Pittsburgh, Pennsylvania 15213 (United States); Jin, Chenwang, E-mail: jcw76@163.com; Yu, Nan; Qian, Yongqiang; Guo, Youmin [Department of Radiology, First Affiliated Hospital of Medical College, Xi’an Jiaotong University, Shaanxi 710061 (China); Wang, Xiaohua [Third Affiliated Hospital, Peking University, Beijing, People’s Republic of China, 100029 (China); Meng, Xin [Department of Radiology, University of Pittsburgh, Pittsburgh, Pennsylvania 15213 (United States)

2015-06-15

Purpose: A novel shape descriptor is presented to aid an automated identification of the airways depicted on computed tomography (CT) images. Methods: Instead of simplifying the tubular characteristic of the airways as an ideal mathematical cylindrical or circular shape, the proposed “loop” shape descriptor exploits the fact that the cross sections of any tubular structure (regardless of its regularity) always appear as a loop. In implementation, the authors first reconstruct the anatomical structures in volumetric CT as a three-dimensional surface model using the classical marching cubes algorithm. Then, the loop descriptor is applied to locate the airways with a concave loop cross section. To deal with the variation of the airway walls in density as depicted on CT images, a multiple threshold strategy is proposed. A publicly available chest CT database consisting of 20 CT scans, which was designed specifically for evaluating an airway segmentation algorithm, was used for quantitative performance assessment. Measures, including length, branch count, and generations, were computed under the aid of a skeletonization operation. Results: For the test dataset, the airway length ranged from 64.6 to 429.8 cm, the generation ranged from 7 to 11, and the branch number ranged from 48 to 312. These results were comparable to the performance of the state-of-the-art algorithms validated on the same dataset. Conclusions: The authors’ quantitative experiment demonstrated the feasibility and reliability of the developed shape descriptor in identifying lung airways.

Fat segmentation on chest CT images via fuzzy models

Science.gov (United States)

Tong, Yubing; Udupa, Jayaram K.; Wu, Caiyun; Pednekar, Gargi; Subramanian, Janani Rajan; Lederer, David J.; Christie, Jason; Torigian, Drew A.

2016-03-01

Quantification of fat throughout the body is vital for the study of many diseases. In the thorax, it is important for lung transplant candidates since obesity and being underweight are contraindications to lung transplantation given their associations with increased mortality. Common approaches for thoracic fat segmentation are all interactive in nature, requiring significant manual effort to draw the interfaces between fat and muscle with low efficiency and questionable repeatability. The goal of this paper is to explore a practical way for the segmentation of subcutaneous adipose tissue (SAT) and visceral adipose tissue (VAT) components of chest fat based on a recently developed body-wide automatic anatomy recognition (AAR) methodology. The AAR approach involves 3 main steps: building a fuzzy anatomy model of the body region involving all its major representative objects, recognizing objects in any given test image, and delineating the objects. We made several modifications to these steps to develop an effective solution to delineate SAT/VAT components of fat. Two new objects representing interfaces of SAT and VAT regions with other tissues, SatIn and VatIn are defined, rather than using directly the SAT and VAT components as objects for constructing the models. A hierarchical arrangement of these new and other reference objects is built to facilitate their recognition in the hierarchical order. Subsequently, accurate delineations of the SAT/VAT components are derived from these objects. Unenhanced CT images from 40 lung transplant candidates were utilized in experimentally evaluating this new strategy. Mean object location error achieved was about 2 voxels and delineation error in terms of false positive and false negative volume fractions were, respectively, 0.07 and 0.1 for SAT and 0.04 and 0.2 for VAT.

Giant-cell arteritis. Concordance study between aortic CT angiography and FDG-PET/CT in detection of large-vessel involvement

International Nuclear Information System (INIS)

Boysson, Hubert de; Dumont, Anael; Boutemy, Jonathan; Maigne, Gwenola; Martin Silva, Nicolas; Sultan, Audrey; Bienvenu, Boris; Aouba, Achille; Liozon, Eric; Ly, Kim Heang; Lambert, Marc; Aide, Nicolas; Manrique, Alain

2017-01-01

The purpose of our study was to assess the concordance of aortic CT angiography (CTA) and FDG-PET/CT in the detection of large-vessel involvement at diagnosis in patients with giant-cell arteritis (GCA). We created a multicenter cohort of patients with GCA diagnosed between 2010 and 2015, and who underwent both FDG-PET/CT and aortic CTA before or in the first ten days following treatment introduction. Eight vascular segments were studied on each procedure. We calculated concordance between both imaging techniques in a per-patient and a per-segment analysis, using Cohen's kappa concordance index. We included 28 patients (21/7 women/men, median age 67 [56-82]). Nineteen patients had large-vessel involvement on PET/CT and 18 of these patients also presented positive findings on CTA. In a per-segment analysis, a median of 5 [1-7] and 3 [1-6] vascular territories were involved on positive PET/CT and CTA, respectively (p = 0.03). In qualitative analysis, i.e., positivity of the procedure suggesting a large-vessel involvement, the concordance rate between both procedures was 0.85 [0.64-1]. In quantitative analysis, i.e., per-segment analysis in both procedures, the global concordance rate was 0.64 [0.54-0.75]. Using FDG-PET/CT as a reference, CTA showed excellent sensitivity (95%) and specificity (100%) in a per-patient analysis. In a per-segment analysis, sensitivity and specificity were 61% and 97.9%, respectively. CTA and FDG-PET/CT were both able to detect large-vessel involvement in GCA with comparable results in a per-patient analysis. However, PET/CT showed higher performance in a per-segment analysis, especially in the detection of inflammation of the aorta's branches. (orig.)

Giant-cell arteritis. Concordance study between aortic CT angiography and FDG-PET/CT in detection of large-vessel involvement

Energy Technology Data Exchange (ETDEWEB)

Boysson, Hubert de; Dumont, Anael; Boutemy, Jonathan; Maigne, Gwenola; Martin Silva, Nicolas; Sultan, Audrey; Bienvenu, Boris; Aouba, Achille [Caen University Hospital, Department of Internal Medicine, Caen (France); Liozon, Eric; Ly, Kim Heang [Limoges University Hospital, Department of Internal Medicine, Limoges (France); Lambert, Marc [Lille University Hospital, Department of Internal Medicine, Lille (France); Aide, Nicolas [Caen University Hospital, Department of Nuclear Medicine, Caen (France); INSERM U1086 ' ' ANTICIPE' ' , Francois Baclesse Cancer Centre, Caen (France); Manrique, Alain [Caen University Hospital, Department of Nuclear Medicine, Caen (France); Normandy University, Caen (France)

2017-12-15

The purpose of our study was to assess the concordance of aortic CT angiography (CTA) and FDG-PET/CT in the detection of large-vessel involvement at diagnosis in patients with giant-cell arteritis (GCA). We created a multicenter cohort of patients with GCA diagnosed between 2010 and 2015, and who underwent both FDG-PET/CT and aortic CTA before or in the first ten days following treatment introduction. Eight vascular segments were studied on each procedure. We calculated concordance between both imaging techniques in a per-patient and a per-segment analysis, using Cohen's kappa concordance index. We included 28 patients (21/7 women/men, median age 67 [56-82]). Nineteen patients had large-vessel involvement on PET/CT and 18 of these patients also presented positive findings on CTA. In a per-segment analysis, a median of 5 [1-7] and 3 [1-6] vascular territories were involved on positive PET/CT and CTA, respectively (p = 0.03). In qualitative analysis, i.e., positivity of the procedure suggesting a large-vessel involvement, the concordance rate between both procedures was 0.85 [0.64-1]. In quantitative analysis, i.e., per-segment analysis in both procedures, the global concordance rate was 0.64 [0.54-0.75]. Using FDG-PET/CT as a reference, CTA showed excellent sensitivity (95%) and specificity (100%) in a per-patient analysis. In a per-segment analysis, sensitivity and specificity were 61% and 97.9%, respectively. CTA and FDG-PET/CT were both able to detect large-vessel involvement in GCA with comparable results in a per-patient analysis. However, PET/CT showed higher performance in a per-segment analysis, especially in the detection of inflammation of the aorta's branches. (orig.)

Global left ventricular function in cardiac CT. Evaluation of an automated 3D region-growing segmentation algorithm

International Nuclear Information System (INIS)

Muehlenbruch, Georg; Das, Marco; Hohl, Christian; Wildberger, Joachim E.; Guenther, Rolf W.; Mahnken, Andreas H.; Rinck, Daniel; Flohr, Thomas G.; Koos, Ralf; Knackstedt, Christian

2006-01-01

The purpose was to evaluate a new semi-automated 3D region-growing segmentation algorithm for functional analysis of the left ventricle in multislice CT (MSCT) of the heart. Twenty patients underwent contrast-enhanced MSCT of the heart (collimation 16 x 0.75 mm; 120 kV; 550 mAseff). Multiphase image reconstructions with 1-mm axial slices and 8-mm short-axis slices were performed. Left ventricular volume measurements (end-diastolic volume, end-systolic volume, ejection fraction and stroke volume) from manually drawn endocardial contours in the short axis slices were compared to semi-automated region-growing segmentation of the left ventricle from the 1-mm axial slices. The post-processing-time for both methods was recorded. Applying the new region-growing algorithm in 13/20 patients (65%), proper segmentation of the left ventricle was feasible. In these patients, the signal-to-noise ratio was higher than in the remaining patients (3.2±1.0 vs. 2.6±0.6). Volume measurements of both segmentation algorithms showed an excellent correlation (all P≤0.0001); the limits of agreement for the ejection fraction were 2.3±8.3 ml. In the patients with proper segmentation the mean post-processing time using the region-growing algorithm was diminished by 44.2%. On the basis of a good contrast-enhanced data set, a left ventricular volume analysis using the new semi-automated region-growing segmentation algorithm is technically feasible, accurate and more time-effective. (orig.)

Automated identification of best-quality coronary artery segments from multiple-phase coronary CT angiography (cCTA) for vessel analysis

Science.gov (United States)

Zhou, Chuan; Chan, Heang-Ping; Hadjiiski, Lubomir M.; Chughtai, Aamer; Wei, Jun; Kazerooni, Ella A.

2016-03-01

We are developing an automated method to identify the best quality segment among the corresponding segments in multiple-phase cCTA. The coronary artery trees are automatically extracted from different cCTA phases using our multi-scale vessel segmentation and tracking method. An automated registration method is then used to align the multiple-phase artery trees. The corresponding coronary artery segments are identified in the registered vessel trees and are straightened by curved planar reformation (CPR). Four features are extracted from each segment in each phase as quality indicators in the original CT volume and the straightened CPR volume. Each quality indicator is used as a voting classifier to vote the corresponding segments. A newly designed weighted voting ensemble (WVE) classifier is finally used to determine the best-quality coronary segment. An observer preference study is conducted with three readers to visually rate the quality of the vessels in 1 to 6 rankings. Six and 10 cCTA cases are used as training and test set in this preliminary study. For the 10 test cases, the agreement between automatically identified best-quality (AI-BQ) segments and radiologist's top 2 rankings is 79.7%, and between AI-BQ and the other two readers are 74.8% and 83.7%, respectively. The results demonstrated that the performance of our automated method was comparable to those of experienced readers for identification of the best-quality coronary segments.

3D statistical shape models incorporating 3D random forest regression voting for robust CT liver segmentation

Science.gov (United States)

Norajitra, Tobias; Meinzer, Hans-Peter; Maier-Hein, Klaus H.

2015-03-01

During image segmentation, 3D Statistical Shape Models (SSM) usually conduct a limited search for target landmarks within one-dimensional search profiles perpendicular to the model surface. In addition, landmark appearance is modeled only locally based on linear profiles and weak learners, altogether leading to segmentation errors from landmark ambiguities and limited search coverage. We present a new method for 3D SSM segmentation based on 3D Random Forest Regression Voting. For each surface landmark, a Random Regression Forest is trained that learns a 3D spatial displacement function between the according reference landmark and a set of surrounding sample points, based on an infinite set of non-local randomized 3D Haar-like features. Landmark search is then conducted omni-directionally within 3D search spaces, where voxelwise forest predictions on landmark position contribute to a common voting map which reflects the overall position estimate. Segmentation experiments were conducted on a set of 45 CT volumes of the human liver, of which 40 images were randomly chosen for training and 5 for testing. Without parameter optimization, using a simple candidate selection and a single resolution approach, excellent results were achieved, while faster convergence and better concavity segmentation were observed, altogether underlining the potential of our approach in terms of increased robustness from distinct landmark detection and from better search coverage.

Value of a probabilistic atlas in medical image segmentation regarding non-rigid registration of abdominal CT scans

Science.gov (United States)

Park, Hyunjin; Meyer, Charles R.

2012-10-01

A probabilistic atlas provides important information to help segmentation and registration applications in medical image analysis. We construct a probabilistic atlas by picking a target geometry and mapping other training scans onto that target and then summing the results into one probabilistic atlas. By choosing an atlas space close to the desired target, we construct an atlas that represents the population well. Image registration used to map one image geometry onto another is a primary task in atlas building. One of the main parameters of registration is the choice of degrees of freedom (DOFs) of the geometric transform. Herein, we measure the effect of the registration's DOFs on the segmentation performance of the resulting probabilistic atlas. Twenty-three normal abdominal CT scans were used, and four organs (liver, spinal cord, left and right kidneys) were segmented for each scan. A well-known manifold learning method, ISOMAP, was used to find the best target space to build an atlas. In summary, segmentation performance was high for high DOF registrations regardless of the chosen target space, while segmentation performance was lowered for low DOF registrations if a target space was far from the best target space. At the 0.05 level of statistical significance, there were no significant differences at high DOF registrations while there were significant differences at low DOF registrations when choosing different targets.

Comparison of atlas-based techniques for whole-body bone segmentation

DEFF Research Database (Denmark)

Arabi, Hossein; Zaidi, Habib

2017-01-01

out in terms of estimating bone extraction accuracy from whole-body MRI using standard metrics, such as Dice similarity (DSC) and relative volume difference (RVD) considering bony structures obtained from intensity thresholding of the reference CT images as the ground truth. Considering the Dice....../MRI. To this end, a variety of atlas-based segmentation strategies commonly used in medical image segmentation and pseudo-CT generation were implemented and evaluated in terms of whole-body bone segmentation accuracy. Bone segmentation was performed on 23 whole-body CT/MR image pairs via leave-one-out cross...... validation procedure. The evaluated segmentation techniques include: (i) intensity averaging (IA), (ii) majority voting (MV), (iii) global and (iv) local (voxel-wise) weighting atlas fusion frameworks implemented utilizing normalized mutual information (NMI), normalized cross-correlation (NCC) and mean...

CT urography: segmentation of urinary bladder using CLASS with local contour refinement

International Nuclear Information System (INIS)

Cha, Kenny; Hadjiiski, Lubomir; Chan, Heang-Ping; Caoili, Elaine M; Cohan, Richard H; Zhou, Chuan

2014-01-01

We are developing a computerized system for bladder segmentation on CT urography (CTU), as a critical component for computer-aided detection of bladder cancer. The presence of regions filled with intravenous contrast and without contrast presents a challenge for bladder segmentation. Previously, we proposed a conjoint level set analysis and segmentation system (CLASS). In case the bladder is partially filled with contrast, CLASS segments the non-contrast (NC) region and the contrast-filled (C) region separately and automatically conjoins the NC and C region contours; however, inaccuracies in the NC and C region contours may cause the conjoint contour to exclude portions of the bladder. To alleviate this problem, we implemented a local contour refinement (LCR) method that exploits model-guided refinement (MGR) and energy-driven wavefront propagation (EDWP). MGR propagates the C region contours if the level set propagation in the C region stops prematurely due to substantial non-uniformity of the contrast. EDWP with regularized energies further propagates the conjoint contours to the correct bladder boundary. EDWP uses changes in energies, smoothness criteria of the contour, and previous slice contour to determine when to stop the propagation, following decision rules derived from training. A data set of 173 cases was collected for this study: 81 cases in the training set (42 lesions, 21 wall thickenings, 18 normal bladders) and 92 cases in the test set (43 lesions, 36 wall thickenings, 13 normal bladders). For all cases, 3D hand segmented contours were obtained as reference standard and used for the evaluation of the computerized segmentation accuracy. For CLASS with LCR, the average volume intersection ratio, average volume error, absolute average volume error, average minimum distance and Jaccard index were 84.2 ± 11.4%, 8.2 ± 17.4%, 13.0 ± 14.1%, 3.5 ± 1.9 mm, 78.8 ± 11.6%, respectively, for the training set and 78.0 ± 14.7%, 16.4 ± 16.9%, 18.2 ± 15

SU-E-J-123: Assessing Segmentation Accuracy of Internal Volumes and Sub-Volumes in 4D PET/CT of Lung Tumors Using a Novel 3D Printed Phantom

International Nuclear Information System (INIS)

Soultan, D; Murphy, J; James, C; Hoh, C; Moiseenko, V; Cervino, L; Gill, B

2015-01-01

Purpose: To assess the accuracy of internal target volume (ITV) segmentation of lung tumors for treatment planning of simultaneous integrated boost (SIB) radiotherapy as seen in 4D PET/CT images, using a novel 3D-printed phantom. Methods: The insert mimics high PET tracer uptake in the core and 50% uptake in the periphery, by using a porous design at the periphery. A lung phantom with the insert was placed on a programmable moving platform. Seven breathing waveforms of ideal and patient-specific respiratory motion patterns were fed to the platform, and 4D PET/CT scans were acquired of each of them. CT images were binned into 10 phases, and PET images were binned into 5 phases following the clinical protocol. Two scenarios were investigated for segmentation: a gate 30–70 window, and no gating. The radiation oncologist contoured the outer ITV of the porous insert with on CT images, while the internal void volume with 100% uptake was contoured on PET images for being indistinguishable from the outer volume in CT images. Segmented ITVs were compared to the expected volumes based on known target size and motion. Results: 3 ideal breathing patterns, 2 regular-breathing patient waveforms, and 2 irregular-breathing patient waveforms were used for this study. 18F-FDG was used as the PET tracer. The segmented ITVs from CT closely matched the expected motion for both no gating and gate 30–70 window, with disagreement of contoured ITV with respect to the expected volume not exceeding 13%. PET contours were seen to overestimate volumes in all the cases, up to more than 40%. Conclusion: 4DPET images of a novel 3D printed phantom designed to mimic different uptake values were obtained. 4DPET contours overestimated ITV volumes in all cases, while 4DCT contours matched expected ITV volume values. Investigation of the cause and effects of the discrepancies is undergoing

Quantitative 3D ultrashort time-to-echo (UTE) MRI and micro-CT (μCT) evaluation of the temporomandibular joint (TMJ) condylar morphology

Energy Technology Data Exchange (ETDEWEB)

Geiger, Daniel [Sapienza University of Rome, Department of Radiological, Oncological and Pathological Sciences, Rome (Italy); Bae, Won C.; Statum, Sheronda; Du, Jiang; Chung, Christine B. [University of California-San Diego, Department of Radiology, San Diego, CA (United States)

2014-01-15

Temporomandibular dysfunction involves osteoarthritis of the TMJ, including degeneration and morphologic changes of the mandibular condyle. The purpose of this study was to determine the accuracy of novel 3D-UTE MRI versus micro-CT (μCT) for quantitative evaluation of mandibular condyle morphology. Nine TMJ condyle specimens were harvested from cadavers (2 M, 3 F; age 85 ± 10 years, mean ± SD). 3D-UTE MRI (TR = 50 ms, TE = 0.05 ms, 104-μm isotropic-voxel) was performed using a 3-T MR scanner and μCT (18-μm isotropic-voxel) was also performed. MR datasets were spatially registered with a μCT dataset. Two observers segmented bony contours of the condyles. Fibrocartilage was segmented on the MR dataset. Using a custom program, bone and fibrocartilage surface coordinates, Gaussian curvature, volume of segmented regions, and fibrocartilage thickness were determined for quantitative evaluation of joint morphology. Agreement between techniques (MRI vs. μCT) and observers (MRI vs. MRI) for Gaussian curvature, mean curvature, and segmented volume of the bone were determined using intraclass correlation coefficient (ICC) analysis. Between MRI and μCT, the average deviation of surface coordinates was 0.19 ± 0.15 mm, slightly higher than the spatial resolution of MRI. Average deviation of the Gaussian curvature and volume of segmented regions, from MRI to μCT, was 5.7 ± 6.5 % and 6.6 ± 6.2 %, respectively. ICC coefficients (MRI vs. μCT) for Gaussian curvature, mean curvature, and segmented volumes were 0.892, 0.893, and 0.972, respectively. Between observers (MRI vs. MRI), the ICC coefficients were 0.998, 0.999, and 0.997, respectively. Fibrocartilage thickness was 0.55 ± 0.11 mm, as previously described in the literature for grossly normal TMJ samples. 3D-UTE MR quantitative evaluation of TMJ condyle morphology ex-vivo, including surface, curvature, and segmented volume, shows high correlation against μCT and between observers. In addition, UTE MRI allows

Poster - 32: Atlas Selection for Automated Segmentation of Pelvic CT for Prostate Radiotherapy

Energy Technology Data Exchange (ETDEWEB)

Mallawi, Abrar; Farrell, TomTom; Diamond, Kevin-Ross; Wierzbicki, Marcin [McMaster University / National Guard Health Affairs, Radiation Oncology Department, Riyadh, Saudi Arabia, McMaster University / Juravinski Cancer Centre, McMaster University / Juravinski Cancer Centre, McMaster University / Juravinski Cancer Centre (Saudi Arabia)

2016-08-15

Atlas based-segmentation has recently been evaluated for use in prostate radiotherapy. In a typical approach, the essential step is the selection of an atlas from a database that the best matches of the target image. This work proposes an atlas selection strategy and evaluate it impacts on final segmentation accuracy. Several anatomical parameters were measured to indicate the overall prostate and body shape, all of these measurements obtained on CT images. A brute force procedure was first performed for a training dataset of 20 patients using image registration to pair subject with similar contours; each subject was served as a target image to which all reaming 19 images were affinity registered. The overlap between the prostate and femoral heads was quantified for each pair using the Dice Similarity Coefficient (DSC). Finally, an atlas selection procedure was designed; relying on the computation of a similarity score defined as a weighted sum of differences between the target and atlas subject anatomical measurement. The algorithm ability to predict the most similar atlas was excellent, achieving mean DSCs of 0.78 ± 0.07 and 0.90 ± 0.02 for the CTV and either femoral head. The proposed atlas selection yielded 0.72 ± 0.11 and 0.87 ± 0.03 for CTV and either femoral head. The DSC obtained with the proposed selection method were slightly lower than the maximum established using brute force, but this does not include potential improvements expected with deformable registration. The proposed atlas selection method provides reasonable segmentation accuracy.

Poster - 32: Atlas Selection for Automated Segmentation of Pelvic CT for Prostate Radiotherapy

International Nuclear Information System (INIS)

Mallawi, Abrar; Farrell, TomTom; Diamond, Kevin-Ross; Wierzbicki, Marcin

2016-01-01

Atlas based-segmentation has recently been evaluated for use in prostate radiotherapy. In a typical approach, the essential step is the selection of an atlas from a database that the best matches of the target image. This work proposes an atlas selection strategy and evaluate it impacts on final segmentation accuracy. Several anatomical parameters were measured to indicate the overall prostate and body shape, all of these measurements obtained on CT images. A brute force procedure was first performed for a training dataset of 20 patients using image registration to pair subject with similar contours; each subject was served as a target image to which all reaming 19 images were affinity registered. The overlap between the prostate and femoral heads was quantified for each pair using the Dice Similarity Coefficient (DSC). Finally, an atlas selection procedure was designed; relying on the computation of a similarity score defined as a weighted sum of differences between the target and atlas subject anatomical measurement. The algorithm ability to predict the most similar atlas was excellent, achieving mean DSCs of 0.78 ± 0.07 and 0.90 ± 0.02 for the CTV and either femoral head. The proposed atlas selection yielded 0.72 ± 0.11 and 0.87 ± 0.03 for CTV and either femoral head. The DSC obtained with the proposed selection method were slightly lower than the maximum established using brute force, but this does not include potential improvements expected with deformable registration. The proposed atlas selection method provides reasonable segmentation accuracy.

Blood vessel-based liver segmentation through the portal phase of a CT dataset

Science.gov (United States)

Maklad, Ahmed S.; Matsuhiro, Mikio; Suzuki, Hidenobu; Kawata, Yoshiki; Niki, Noboru; Moriyama, Noriyuki; Utsunomiya, Toru; Shimada, Mitsuo

2013-02-01

Blood vessels are dispersed throughout the human body organs and carry unique information for each person. This information can be used to delineate organ boundaries. The proposed method relies on abdominal blood vessels (ABV) to segment the liver considering the potential presence of tumors through the portal phase of a CT dataset. ABV are extracted and classified into hepatic (HBV) and nonhepatic (non-HBV) with a small number of interactions. HBV and non-HBV are used to guide an automatic segmentation of the liver. HBV are used to individually segment the core region of the liver. This region and non-HBV are used to construct a boundary surface between the liver and other organs to separate them. The core region is classified based on extracted posterior distributions of its histogram into low intensity tumor (LIT) and non-LIT core regions. Non-LIT case includes normal part of liver, HBV, and high intensity tumors if exist. Each core region is extended based on its corresponding posterior distribution. Extension is completed when it reaches either a variation in intensity or the constructed boundary surface. The method was applied to 80 datasets (30 Medical Image Computing and Computer Assisted Intervention (MICCAI) and 50 non-MICCAI data) including 60 datasets with tumors. Our results for the MICCAI-test data were evaluated by sliver07 [1] with an overall score of 79.7, which ranks seventh best on the site (December 2013). This approach seems a promising method for extraction of liver volumetry of various shapes and sizes and low intensity hepatic tumors.

Poster — Thur Eve — 59: Atlas Selection for Automated Segmentation of Pelvic CT for Prostate Radiotherapy

International Nuclear Information System (INIS)

Mallawi, A; Farrell, T; Diamond, K; Wierzbicki, M

2014-01-01

Automated atlas-based segmentation has recently been evaluated for use in planning prostate cancer radiotherapy. In the typical approach, the essential step is the selection of an atlas from a database that best matches the target image. This work proposes an atlas selection strategy and evaluates its impact on the final segmentation accuracy. Prostate length (PL), right femoral head diameter (RFHD), and left femoral head diameter (LFHD) were measured in CT images of 20 patients. Each subject was then taken as the target image to which all remaining 19 images were affinely registered. For each pair of registered images, the overlap between prostate and femoral head contours was quantified using the Dice Similarity Coefficient (DSC). Finally, we designed an atlas selection strategy that computed the ratio of PL (prostate segmentation), RFHD (right femur segmentation), and LFHD (left femur segmentation) between the target subject and each subject in the atlas database. Five atlas subjects yielding ratios nearest to one were then selected for further analysis. RFHD and LFHD were excellent parameters for atlas selection, achieving a mean femoral head DSC of 0.82 ± 0.06. PL had a moderate ability to select the most similar prostate, with a mean DSC of 0.63 ± 0.18. The DSC obtained with the proposed selection method were slightly lower than the maximums established using brute force, but this does not include potential improvements expected with deformable registration. Atlas selection based on PL for prostate and femoral diameter for femoral heads provides reasonable segmentation accuracy

Poster — Thur Eve — 59: Atlas Selection for Automated Segmentation of Pelvic CT for Prostate Radiotherapy

Energy Technology Data Exchange (ETDEWEB)

Mallawi, A [McMaster University, Medical Physics and Applied Radiation Sciences Department, Hamilton, Ontario (Canada); Farrell, T; Diamond, K; Wierzbicki, M [McMaster University, Medical Physics and Applied Radiation Sciences Department, Hamilton, Ontario (Canada); Juravinski Cancer Centre, Medical Physics Department, Hamilton, Ontario (Canada)

2014-08-15

Automated atlas-based segmentation has recently been evaluated for use in planning prostate cancer radiotherapy. In the typical approach, the essential step is the selection of an atlas from a database that best matches the target image. This work proposes an atlas selection strategy and evaluates its impact on the final segmentation accuracy. Prostate length (PL), right femoral head diameter (RFHD), and left femoral head diameter (LFHD) were measured in CT images of 20 patients. Each subject was then taken as the target image to which all remaining 19 images were affinely registered. For each pair of registered images, the overlap between prostate and femoral head contours was quantified using the Dice Similarity Coefficient (DSC). Finally, we designed an atlas selection strategy that computed the ratio of PL (prostate segmentation), RFHD (right femur segmentation), and LFHD (left femur segmentation) between the target subject and each subject in the atlas database. Five atlas subjects yielding ratios nearest to one were then selected for further analysis. RFHD and LFHD were excellent parameters for atlas selection, achieving a mean femoral head DSC of 0.82 ± 0.06. PL had a moderate ability to select the most similar prostate, with a mean DSC of 0.63 ± 0.18. The DSC obtained with the proposed selection method were slightly lower than the maximums established using brute force, but this does not include potential improvements expected with deformable registration. Atlas selection based on PL for prostate and femoral diameter for femoral heads provides reasonable segmentation accuracy.

Segmentation-DrivenTomographic Reconstruction

DEFF Research Database (Denmark)

Kongskov, Rasmus Dalgas

such that the segmentation subsequently can be carried out by use of a simple segmentation method, for instance just a thresholding method. We tested the advantages of going from a two-stage reconstruction method to a one stage segmentation-driven reconstruction method for the phase contrast tomography reconstruction......The tomographic reconstruction problem is concerned with creating a model of the interior of an object from some measured data, typically projections of the object. After reconstructing an object it is often desired to segment it, either automatically or manually. For computed tomography (CT...

CT in childhood allergic bronchopulmonary aspergillosis

International Nuclear Information System (INIS)

Shah, A.; Bhagat, R.; Panchal, N.; Pant, C.S.

1992-01-01

CT of the thorax done during acute severe asthma in two paediatric patients demonstrated central bronchiectasis, a sine qua non for the diagnosis of allergic bronchopulmonary aspergillosis. Bronchography, regarded as the gold standard, was done subsequently on recovery. A comparative segmental analysis revealed that CT was able to identify immediately 24 of 27 segments which showed central bronchiectasis on bronchography. Early diagnosis with the aid of CT enabled immediate intervention which may have helped to prevent further lung damage in the paediatric patients. (orig.)

Segmentation of medical images using explicit anatomical knowledge

Science.gov (United States)

Wilson, Laurie S.; Brown, Stephen; Brown, Matthew S.; Young, Jeanne; Li, Rongxin; Luo, Suhuai; Brandt, Lee

1999-07-01

Knowledge-based image segmentation is defined in terms of the separation of image analysis procedures and representation of knowledge. Such architecture is particularly suitable for medical image segmentation, because of the large amount of structured domain knowledge. A general methodology for the application of knowledge-based methods to medical image segmentation is described. This includes frames for knowledge representation, fuzzy logic for anatomical variations, and a strategy for determining the order of segmentation from the modal specification. This method has been applied to three separate problems, 3D thoracic CT, chest X-rays and CT angiography. The application of the same methodology to such a range of applications suggests a major role in medical imaging for segmentation methods incorporating representation of anatomical knowledge.

Multilevel segmentation of intracranial aneurysms in CT angiography images

Energy Technology Data Exchange (ETDEWEB)

Wang, Yan [Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, California 94122 and University of Lyon, CREATIS, CNRS UMR 5220, INSERM U1206, UCB Lyon1, INSA Lyon, Lyon 69100 (France); Zhang, Yue, E-mail: y.zhang525@gmail.com [Veterans Affairs Medical Center, San Francisco, California 94121 and University of Lyon, CREATIS, CNRS UMR 5220, INSERM U1206, UCB Lyon1, INSA Lyon, Lyon 69100 (France); Navarro, Laurent [Ecole Nationale Superieure des Mines de Saint-Etienne, Saint-Etienne 42015 (France); Eker, Omer Faruk [CHU Montpellier, Neuroradiologie, Montpellier 34000 (France); Corredor Jerez, Ricardo A. [Ecole Polytechnique Federale de Lausanne, Lausanne 1015 (Switzerland); Chen, Yu; Zhu, Yuemin; Courbebaisse, Guy [University of Lyon, CREATIS, CNRS UMR 5220, INSERM U1206, UCB Lyon1, INSA Lyon, Lyon 69100 (France)

2016-04-15

Purpose: Segmentation of aneurysms plays an important role in interventional planning. Yet, the segmentation of both the lumen and the thrombus of an intracranial aneurysm in computed tomography angiography (CTA) remains a challenge. This paper proposes a multilevel segmentation methodology for efficiently segmenting intracranial aneurysms in CTA images. Methods: The proposed methodology first uses the lattice Boltzmann method (LBM) to extract the lumen part directly from the original image. Then, the LBM is applied again on an intermediate image whose lumen part is filled by the mean gray-level value outside the lumen, to yield an image region containing part of the aneurysm boundary. After that, an expanding disk is introduced to estimate the complete contour of the aneurysm. Finally, the contour detected is used as the initial contour of the level set with ellipse to refine the aneurysm. Results: The results obtained on 11 patients from different hospitals showed that the proposed segmentation was comparable with manual segmentation, and that quantitatively, the average segmentation matching factor (SMF) reached 86.99%, demonstrating good segmentation accuracy. Chan–Vese method, Sen’s model, and Luca’s model were used to compare the proposed method and their average SMF values were 39.98%, 40.76%, and 77.11%, respectively. Conclusions: The authors have presented a multilevel segmentation method based on the LBM and level set with ellipse for accurate segmentation of intracranial aneurysms. Compared to three existing methods, for all eleven patients, the proposed method can successfully segment the lumen with the highest SMF values for nine patients and second highest SMF values for the two. It also segments the entire aneurysm with the highest SMF values for ten patients and second highest SMF value for the one. This makes it potential for clinical assessment of the volume and aspect ratio of the intracranial aneurysms.

Coronary imaging quality in routine ECG-gated multidetector CT examinations of the entire thorax: preliminary experience with a 64-slice CT system in 133 patients

International Nuclear Information System (INIS)

Delhaye, Damien; Remy-Jardin, Martine; Salem, Randa; Teisseire, Antoine; Khalil, Chadi; Remy, Jacques; Delannoy-Deken, Valerie; Duhamel, Alain

2007-01-01

To evaluate image quality in the assessment of the coronary arteries during routine ECG-gated multidetector CT (MDCT) of the chest. One hundred and thirty three patients in sinus rhythm underwent an ECG-gated CT angiographic examination of the entire chest without β-blockers with a 64-slice CT system. In 127 patients (95%), it was possible to assess the coronary arteries partially or totally; coronary artery imaging failed in six patients (5%), leading to a detailed description of the coronary arteries in 127 patients. Considering ten coronary artery segments per patient, 75% of coronary segments were assessable (948/1270 segments). When the distal segments were excluded from the analysis (i.e., seven coronary segments evaluated per patient), the percentage of assessable segments was 86% (768/889 proximal and mid coronary segments) and reached 93% (474/508) when assessing proximal segments exclusively. The mean number of assessable segments was significantly higher in patients with a heart rate ≤80 bpm (n=95) than in patients with a heart rate >80 bpm (n=38) (p<0.002). Proximal and mid-coronary segments can be adequately assessed during a whole-chest ECG-gated CT angiographic examination without administration of β-blockers in patients with a heart rate below 80 bpm. (orig.)

Automatic liver volume segmentation and fibrosis classification

Science.gov (United States)

Bal, Evgeny; Klang, Eyal; Amitai, Michal; Greenspan, Hayit

2018-02-01

In this work, we present an automatic method for liver segmentation and fibrosis classification in liver computed-tomography (CT) portal phase scans. The input is a full abdomen CT scan with an unknown number of slices, and the output is a liver volume segmentation mask and a fibrosis grade. A multi-stage analysis scheme is applied to each scan, including: volume segmentation, texture features extraction and SVM based classification. Data contains portal phase CT examinations from 80 patients, taken with different scanners. Each examination has a matching Fibroscan grade. The dataset was subdivided into two groups: first group contains healthy cases and mild fibrosis, second group contains moderate fibrosis, severe fibrosis and cirrhosis. Using our automated algorithm, we achieved an average dice index of 0.93 ± 0.05 for segmentation and a sensitivity of 0.92 and specificity of 0.81for classification. To the best of our knowledge, this is a first end to end automatic framework for liver fibrosis classification; an approach that, once validated, can have a great potential value in the clinic.

The change of volume of each hepatic segment in liver cirrhosis

International Nuclear Information System (INIS)

Arai, Kazunori; Takashima, Tsutomu; Matsui, Osamu; Kadoya, Masumi; Kameyama, Tomiaki; Nishijima, Hiroshi; Takanaka, Tsuyoshi; Gabata, Toshifumi

1986-01-01

We studied morphological changes of liver due to liver cirrhosis by evaluating the volume of liver and each hepatic segments (left lateral, left medial, right anterior, right posterior, and caudate lobe) divided using dynamic sequential CT during arterial portography. In liver cirrhosis, left lateral segment and caudate lobe were relatively enlarged, while right lobe and left medial segment showed significant shrinkage. But when posterior inferior right hepatic vein was evident on CT, right posterior segment did not shrink. (author)

ADVANCED CLUSTER BASED IMAGE SEGMENTATION

Directory of Open Access Journals (Sweden)

D. Kesavaraja

2011-11-01

Full Text Available This paper presents efficient and portable implementations of a useful image segmentation technique which makes use of the faster and a variant of the conventional connected components algorithm which we call parallel Components. In the Modern world majority of the doctors are need image segmentation as the service for various purposes and also they expect this system is run faster and secure. Usually Image segmentation Algorithms are not working faster. In spite of several ongoing researches in Conventional Segmentation and its Algorithms might not be able to run faster. So we propose a cluster computing environment for parallel image Segmentation to provide faster result. This paper is the real time implementation of Distributed Image Segmentation in Clustering of Nodes. We demonstrate the effectiveness and feasibility of our method on a set of Medical CT Scan Images. Our general framework is a single address space, distributed memory programming model. We use efficient techniques for distributing and coalescing data as well as efficient combinations of task and data parallelism. The image segmentation algorithm makes use of an efficient cluster process which uses a novel approach for parallel merging. Our experimental results are consistent with the theoretical analysis and practical results. It provides the faster execution time for segmentation, when compared with Conventional method. Our test data is different CT scan images from the Medical database. More efficient implementations of Image Segmentation will likely result in even faster execution times.

Segmentation of Extrapulmonary Tuberculosis Infection Using Modified Automatic Seeded Region Growing

Directory of Open Access Journals (Sweden)

Nordin Abdul

2009-01-01

Full Text Available Abstract In the image segmentation process of positron emission tomography combined with computed tomography (PET/CT imaging, previous works used information in CT only for segmenting the image without utilizing the information that can be provided by PET. This paper proposes to utilize the hot spot values in PET to guide the segmentation in CT, in automatic image segmentation using seeded region growing (SRG technique. This automatic segmentation routine can be used as part of automatic diagnostic tools. In addition to the original initial seed selection using hot spot values in PET, this paper also introduces a new SRG growing criterion, the sliding windows. Fourteen images of patients having extrapulmonary tuberculosis have been examined using the above-mentioned method. To evaluate the performance of the modified SRG, three fidelity criteria are measured: percentage of under-segmentation area, percentage of over-segmentation area, and average time consumption. In terms of the under-segmentation percentage, SRG with average of the region growing criterion shows the least error percentage (51.85%. Meanwhile, SRG with local averaging and variance yielded the best results (2.67% for the over-segmentation percentage. In terms of the time complexity, the modified SRG with local averaging and variance growing criterion shows the best performance with 5.273 s average execution time. The results indicate that the proposed methods yield fairly good performance in terms of the over- and under-segmentation area. The results also demonstrated that the hot spot values in PET can be used to guide the automatic segmentation in CT image.

Application of single- and dual-energy CT brain tissue segmentation to PET monitoring of proton therapy

Science.gov (United States)

Berndt, Bianca; Landry, Guillaume; Schwarz, Florian; Tessonnier, Thomas; Kamp, Florian; Dedes, George; Thieke, Christian; Würl, Matthias; Kurz, Christopher; Ganswindt, Ute; Verhaegen, Frank; Debus, Jürgen; Belka, Claus; Sommer, Wieland; Reiser, Maximilian; Bauer, Julia; Parodi, Katia

2017-03-01

The purpose of this work was to evaluate the ability of single and dual energy computed tomography (SECT, DECT) to estimate tissue composition and density for usage in Monte Carlo (MC) simulations of irradiation induced β + activity distributions. This was done to assess the impact on positron emission tomography (PET) range verification in proton therapy. A DECT-based brain tissue segmentation method was developed for white matter (WM), grey matter (GM) and cerebrospinal fluid (CSF). The elemental composition of reference tissues was assigned to closest CT numbers in DECT space (DECTdist). The method was also applied to SECT data (SECTdist). In a validation experiment, the proton irradiation induced PET activity of three brain equivalent solutions (BES) was compared to simulations based on different tissue segmentations. Five patients scanned with a dual source DECT scanner were analyzed to compare the different segmentation methods. A single magnetic resonance (MR) scan was used for comparison with an established segmentation toolkit. Additionally, one patient with SECT and post-treatment PET scans was investigated. For BES, DECTdist and SECTdist reduced differences to the reference simulation by up to 62% when compared to the conventional stoichiometric segmentation (SECTSchneider). In comparison to MR brain segmentation, Dice similarity coefficients for WM, GM and CSF were 0.61, 0.67 and 0.66 for DECTdist and 0.54, 0.41 and 0.66 for SECTdist. MC simulations of PET treatment verification in patients showed important differences between DECTdist/SECTdist and SECTSchneider for patients with large CSF areas within the treatment field but not in WM and GM. Differences could be misinterpreted as PET derived range shifts of up to 4 mm. DECTdist and SECTdist yielded comparable activity distributions, and comparison of SECTdist to a measured patient PET scan showed improved agreement when compared to SECTSchneider. The agreement between predicted and measured PET

Automated torso organ segmentation from 3D CT images using structured perceptron and dual decomposition

Science.gov (United States)

Nimura, Yukitaka; Hayashi, Yuichiro; Kitasaka, Takayuki; Mori, Kensaku

2015-03-01

This paper presents a method for torso organ segmentation from abdominal CT images using structured perceptron and dual decomposition. A lot of methods have been proposed to enable automated extraction of organ regions from volumetric medical images. However, it is necessary to adjust empirical parameters of them to obtain precise organ regions. This paper proposes an organ segmentation method using structured output learning. Our method utilizes a graphical model and binary features which represent the relationship between voxel intensities and organ labels. Also we optimize the weights of the graphical model by structured perceptron and estimate the best organ label for a given image by dynamic programming and dual decomposition. The experimental result revealed that the proposed method can extract organ regions automatically using structured output learning. The error of organ label estimation was 4.4%. The DICE coefficients of left lung, right lung, heart, liver, spleen, pancreas, left kidney, right kidney, and gallbladder were 0.91, 0.95, 0.77, 0.81, 0.74, 0.08, 0.83, 0.84, and 0.03, respectively.

SU-C-BRA-04: Automated Segmentation of Head-And-Neck CT Images for Radiotherapy Treatment Planning Via Multi-Atlas Machine Learning (MAML)

International Nuclear Information System (INIS)

Ren, X; Gao, H; Sharp, G

2016-01-01

Purpose: Accurate image segmentation is a crucial step during image guided radiation therapy. This work proposes multi-atlas machine learning (MAML) algorithm for automated segmentation of head-and-neck CT images. Methods: As the first step, the algorithm utilizes normalized mutual information as similarity metric, affine registration combined with multiresolution B-Spline registration, and then fuses together using the label fusion strategy via Plastimatch. As the second step, the following feature selection strategy is proposed to extract five feature components from reference or atlas images: intensity (I), distance map (D), box (B), center of gravity (C) and stable point (S). The box feature B is novel. It describes a relative position from each point to minimum inscribed rectangle of ROI. The center-of-gravity feature C is the 3D Euclidean distance from a sample point to the ROI center of gravity, and then S is the distance of the sample point to the landmarks. Then, we adopt random forest (RF) in Scikit-learn, a Python module integrating a wide range of state-of-the-art machine learning algorithms as classifier. Different feature and atlas strategies are used for different ROIs for improved performance, such as multi-atlas strategy with reference box for brainstem, and single-atlas strategy with reference landmark for optic chiasm. Results: The algorithm was validated on a set of 33 CT images with manual contours using a leave-one-out cross-validation strategy. Dice similarity coefficients between manual contours and automated contours were calculated: the proposed MAML method had an improvement from 0.79 to 0.83 for brainstem and 0.11 to 0.52 for optic chiasm with respect to multi-atlas segmentation method (MA). Conclusion: A MAML method has been proposed for automated segmentation of head-and-neck CT images with improved performance. It provides the comparable result in brainstem and the improved result in optic chiasm compared with MA. Xuhua Ren and Hao

SU-C-BRA-04: Automated Segmentation of Head-And-Neck CT Images for Radiotherapy Treatment Planning Via Multi-Atlas Machine Learning (MAML)

Energy Technology Data Exchange (ETDEWEB)

Ren, X; Gao, H [Shanghai Jiao Tong University, Shanghai, Shanghai (China); Sharp, G [Massachusetts General Hospital, Boston, MA (United States)

2016-06-15

Purpose: Accurate image segmentation is a crucial step during image guided radiation therapy. This work proposes multi-atlas machine learning (MAML) algorithm for automated segmentation of head-and-neck CT images. Methods: As the first step, the algorithm utilizes normalized mutual information as similarity metric, affine registration combined with multiresolution B-Spline registration, and then fuses together using the label fusion strategy via Plastimatch. As the second step, the following feature selection strategy is proposed to extract five feature components from reference or atlas images: intensity (I), distance map (D), box (B), center of gravity (C) and stable point (S). The box feature B is novel. It describes a relative position from each point to minimum inscribed rectangle of ROI. The center-of-gravity feature C is the 3D Euclidean distance from a sample point to the ROI center of gravity, and then S is the distance of the sample point to the landmarks. Then, we adopt random forest (RF) in Scikit-learn, a Python module integrating a wide range of state-of-the-art machine learning algorithms as classifier. Different feature and atlas strategies are used for different ROIs for improved performance, such as multi-atlas strategy with reference box for brainstem, and single-atlas strategy with reference landmark for optic chiasm. Results: The algorithm was validated on a set of 33 CT images with manual contours using a leave-one-out cross-validation strategy. Dice similarity coefficients between manual contours and automated contours were calculated: the proposed MAML method had an improvement from 0.79 to 0.83 for brainstem and 0.11 to 0.52 for optic chiasm with respect to multi-atlas segmentation method (MA). Conclusion: A MAML method has been proposed for automated segmentation of head-and-neck CT images with improved performance. It provides the comparable result in brainstem and the improved result in optic chiasm compared with MA. Xuhua Ren and Hao

Automated volume analysis of head and neck lesions on CT scans using 3D level set segmentation

International Nuclear Information System (INIS)

Street, Ethan; Hadjiiski, Lubomir; Sahiner, Berkman; Gujar, Sachin; Ibrahim, Mohannad; Mukherji, Suresh K.; Chan, Heang-Ping

2007-01-01

The authors have developed a semiautomatic system for segmentation of a diverse set of lesions in head and neck CT scans. The system takes as input an approximate bounding box, and uses a multistage level set to perform the final segmentation. A data set consisting of 69 lesions marked on 33 scans from 23 patients was used to evaluate the performance of the system. The contours from automatic segmentation were compared to both 2D and 3D gold standard contours manually drawn by three experienced radiologists. Three performance metric measures were used for the comparison. In addition, a radiologist provided quality ratings on a 1 to 10 scale for all of the automatic segmentations. For this pilot study, the authors observed that the differences between the automatic and gold standard contours were larger than the interobserver differences. However, the system performed comparably to the radiologists, achieving an average area intersection ratio of 85.4% compared to an average of 91.2% between two radiologists. The average absolute area error was 21.1% compared to 10.8%, and the average 2D distance was 1.38 mm compared to 0.84 mm between the radiologists. In addition, the quality rating data showed that, despite the very lax assumptions made on the lesion characteristics in designing the system, the automatic contours approximated many of the lesions very well

Application of industrial CT in reverse engineering technology

International Nuclear Information System (INIS)

Fang Liyong; Li Hui; Bai Jinping; Li Bailin

2013-01-01

The basic principle and basic steps of reverse engineering technology based on industrial CT are described. The recent research progresses and situation at home and abroad of reverse engineering technology based on industrial CT image are respectively described, analyzed and summarized from two routes which are surface segmentation and volume segmentation. An example of conch is used to exhibit the results from the two routes in reverse engineering technology based on industrial CT image. Finally, some difficulties in application and the future developments of reverse engineering technology based on industrial CT are prospected. (authors)

Modeling and segmentation of intra-cochlear anatomy in conventional CT

Science.gov (United States)

Noble, Jack H.; Rutherford, Robert B.; Labadie, Robert F.; Majdani, Omid; Dawant, Benoit M.

2010-03-01

Cochlear implant surgery is a procedure performed to treat profound hearing loss. Since the cochlea is not visible in surgery, the physician uses anatomical landmarks to estimate the pose of the cochlea. Research has indicated that implanting the electrode in a particular cavity of the cochlea, the scala tympani, results in better hearing restoration. The success of the scala tympani implantation is largely dependent on the point of entry and angle of electrode insertion. Errors can occur due to the imprecise nature of landmark-based, manual navigation as well as inter-patient variations between scala tympani and the anatomical landmarks. In this work, we use point distribution models of the intra-cochlear anatomy to study the inter-patient variations between the cochlea and the typical anatomic landmarks, and we implement an active shape model technique to automatically localize intra-cochlear anatomy in conventional CT images, where intra-cochlear structures are not visible. This fully automatic segmentation could aid the surgeon to choose the point of entry and angle of approach to maximize the likelihood of scala tympani insertion, resulting in more substantial hearing restoration.

Decomposing the Hounsfield unit: probabilistic segmentation of brain tissue in computed tomography.

Science.gov (United States)

Kemmling, A; Wersching, H; Berger, K; Knecht, S; Groden, C; Nölte, I

2012-03-01

The aim of this study was to present and evaluate a standardized technique for brain segmentation of cranial computed tomography (CT) using probabilistic partial volume tissue maps based on a database of high resolution T1 magnetic resonance images (MRI). Probabilistic tissue maps of white matter (WM), gray matter (GM) and cerebrospinal fluid (CSF) were derived from 600 normal brain MRIs (3.0 Tesla, T1-3D-turbo-field-echo) of 2 large community-based population studies (BiDirect and SEARCH Health studies). After partial tissue segmentation (FAST 4.0), MR images were linearly registered to MNI-152 standard space (FLIRT 5.5) with non-linear refinement (FNIRT 1.0) to obtain non-binary probabilistic volume images for each tissue class which were subsequently used for CT segmentation. From 150 normal cerebral CT scans a customized reference image in standard space was constructed with iterative non-linear registration to MNI-152 space. The inverse warp of tissue-specific probability maps to CT space (MNI-152 to individual CT) was used to decompose a CT image into tissue specific components (GM, WM, CSF). Potential benefits and utility of this novel approach with regard to unsupervised quantification of CT images and possible visual enhancement are addressed. Illustrative examples of tissue segmentation in different pathological cases including perfusion CT are presented. Automated tissue segmentation of cranial CT images using highly refined tissue probability maps derived from high resolution MR images is feasible. Potential applications include automated quantification of WM in leukoaraiosis, CSF in hydrocephalic patients, GM in neurodegeneration and ischemia and perfusion maps with separate assessment of GM and WM.

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

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

Energy Technology Data Exchange (ETDEWEB)

Aristophanous, M; Yang, J; Beadle, B [UT MD Anderson Cancer Center, Houston, TX (United States)

2014-06-01

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

Level-set segmentation of pulmonary nodules in megavolt electronic portal images using a CT prior

International Nuclear Information System (INIS)

Schildkraut, J. S.; Prosser, N.; Savakis, A.; Gomez, J.; Nazareth, D.; Singh, A. K.; Malhotra, H. K.

2010-01-01

Purpose: Pulmonary nodules present unique problems during radiation treatment due to nodule position uncertainty that is caused by respiration. The radiation field has to be enlarged to account for nodule motion during treatment. The purpose of this work is to provide a method of locating a pulmonary nodule in a megavolt portal image that can be used to reduce the internal target volume (ITV) during radiation therapy. A reduction in the ITV would result in a decrease in radiation toxicity to healthy tissue. Methods: Eight patients with nonsmall cell lung cancer were used in this study. CT scans that include the pulmonary nodule were captured with a GE Healthcare LightSpeed RT 16 scanner. Megavolt portal images were acquired with a Varian Trilogy unit equipped with an AS1000 electronic portal imaging device. The nodule localization method uses grayscale morphological filtering and level-set segmentation with a prior. The treatment-time portion of the algorithm is implemented on a graphical processing unit. Results: The method was retrospectively tested on eight cases that include a total of 151 megavolt portal image frames. The method reduced the nodule position uncertainty by an average of 40% for seven out of the eight cases. The treatment phase portion of the method has a subsecond execution time that makes it suitable for near-real-time nodule localization. Conclusions: A method was developed to localize a pulmonary nodule in a megavolt portal image. The method uses the characteristics of the nodule in a prior CT scan to enhance the nodule in the portal image and to identify the nodule region by level-set segmentation. In a retrospective study, the method reduced the nodule position uncertainty by an average of 40% for seven out of the eight cases studied.

Effects of alveolar bone displacement with segmental osteotomy: micro-CT and histomorphometric analysis in rats

Directory of Open Access Journals (Sweden)

Taegun KIM

Full Text Available Abstract The purpose of this study was to evaluate the effects of segmental osteotomy on the blood vessels and osteoclasts in rats using micro-computed tomography (micro-CT and histomorphometric analysis. After segmental osteotomy was performed around the maxillary first molars of 36 male Sprague-Dawley rats (n = 72, the samples were divided into a control group (no displacement, 0.5 D group (0.5 mm buccal displacement and 1.0 D group (1.0 mm buccal displacement (n = 24/group. At 1, 2, 4 and 8 weeks after surgery, changes in the blood vessel volume were investigated using micro-CT with perfusion of radiopaque silicone rubber. Tartrate-resistant acid phosphatase (TRAP staining was used for histomorphometric analysis. Two-way repeated measures analysis of variance (rmANOVA was performed to compare the volume of blood vessels and number of TRAP-positive osteoclasts among the groups. Regarding blood vessel volume, the displacement groups had no significant effects, while the time points had significant effects (p = 0.014. The blood vessel volume at 1 week was significantly smaller than that at 2, 4, and 8 weeks (p = 0.004, p = 0.026, and p = 0.005, respectively. Regarding TRAP cell count, the displacement groups had no significant effects, while the time points had significant effects (p < 0.001. The number of TRAP-positive osteoclasts at 8 weeks was significantly smaller than that at 1, 2, and 4 weeks (p < 0.001, p < 0.001, and p = 0.002, respectively, and the count at 4 weeks was smaller than that at 1 week (p = 0.011. Therefore, a regional osteoclast-related acceleratory phenomenon was maintained until 4 weeks after surgery.

Extraction of airways from CT (EXACT’09)

DEFF Research Database (Denmark)

Lo, Pechin Chien Pau; Ginneken, Bram van; Reinhardt, Joseph M.

2012-01-01

or not it is a correctly segmented part of the airway tree. Finally, the reference airway trees are constructed by taking the union of all correctly extracted branch segments. Fifteen airway tree extraction algorithms from different research groups are evaluated on a diverse set of 20 chest computed tomography (CT) scans...... of subjects ranging from healthy volunteers to patients with severe pathologies, scanned at different sites, with different CT scanner brands, models, and scanning protocols. Three performance measures covering different aspects of segmentation quality were computed for all participating algorithms. Results...

Coronary artery analysis: Computer-assisted selection of best-quality segments in multiple-phase coronary CT angiography

Energy Technology Data Exchange (ETDEWEB)

Zhou, Chuan, E-mail: chuan@umich.edu; Chan, Heang-Ping; Hadjiyski, Lubomir M.; Chughtai, Aamer; Wei, Jun; Kazerooni, Ella A. [Department of Radiology, The University of Michigan, Ann Arbor, Michigan 48109-0904 (United States)

2016-10-15

Purpose: The authors are developing an automated method to identify the best-quality coronary arterial segment from multiple-phase coronary CT angiography (cCTA) acquisitions, which may be used by either interpreting physicians or computer-aided detection systems to optimally and efficiently utilize the diagnostic information available in multiple-phase cCTA for the detection of coronary artery disease. Methods: After initialization with a manually identified seed point, each coronary artery tree is automatically extracted from multiple cCTA phases using our multiscale coronary artery response enhancement and 3D rolling balloon region growing vessel segmentation and tracking method. The coronary artery trees from multiple phases are then aligned by a global registration using an affine transformation with quadratic terms and nonlinear simplex optimization, followed by a local registration using a cubic B-spline method with fast localized optimization. The corresponding coronary arteries among the available phases are identified using a recursive coronary segment matching method. Each of the identified vessel segments is transformed by the curved planar reformation (CPR) method. Four features are extracted from each corresponding segment as quality indicators in the original computed tomography volume and the straightened CPR volume, and each quality indicator is used as a voting classifier for the arterial segment. A weighted voting ensemble (WVE) classifier is designed to combine the votes of the four voting classifiers for each corresponding segment. The segment with the highest WVE vote is then selected as the best-quality segment. In this study, the training and test sets consisted of 6 and 20 cCTA cases, respectively, each with 6 phases, containing a total of 156 cCTA volumes and 312 coronary artery trees. An observer preference study was also conducted with one expert cardiothoracic radiologist and four nonradiologist readers to visually rank vessel segment

Coronary artery analysis: Computer-assisted selection of best-quality segments in multiple-phase coronary CT angiography

International Nuclear Information System (INIS)

Zhou, Chuan; Chan, Heang-Ping; Hadjiyski, Lubomir M.; Chughtai, Aamer; Wei, Jun; Kazerooni, Ella A.

2016-01-01

Purpose: The authors are developing an automated method to identify the best-quality coronary arterial segment from multiple-phase coronary CT angiography (cCTA) acquisitions, which may be used by either interpreting physicians or computer-aided detection systems to optimally and efficiently utilize the diagnostic information available in multiple-phase cCTA for the detection of coronary artery disease. Methods: After initialization with a manually identified seed point, each coronary artery tree is automatically extracted from multiple cCTA phases using our multiscale coronary artery response enhancement and 3D rolling balloon region growing vessel segmentation and tracking method. The coronary artery trees from multiple phases are then aligned by a global registration using an affine transformation with quadratic terms and nonlinear simplex optimization, followed by a local registration using a cubic B-spline method with fast localized optimization. The corresponding coronary arteries among the available phases are identified using a recursive coronary segment matching method. Each of the identified vessel segments is transformed by the curved planar reformation (CPR) method. Four features are extracted from each corresponding segment as quality indicators in the original computed tomography volume and the straightened CPR volume, and each quality indicator is used as a voting classifier for the arterial segment. A weighted voting ensemble (WVE) classifier is designed to combine the votes of the four voting classifiers for each corresponding segment. The segment with the highest WVE vote is then selected as the best-quality segment. In this study, the training and test sets consisted of 6 and 20 cCTA cases, respectively, each with 6 phases, containing a total of 156 cCTA volumes and 312 coronary artery trees. An observer preference study was also conducted with one expert cardiothoracic radiologist and four nonradiologist readers to visually rank vessel segment

Detection of relevant colonic neoplasms with PET/CT: promising accuracy with minimal CT dose and a standardised PET cut-off

Energy Technology Data Exchange (ETDEWEB)

Luboldt, Wolfgang [Multiorgan Screening Foundation, Frankfurt (Germany); University Hospital Frankfurt, Department of Radiology, Frankfurt am Main (Germany); University Hospital Dresden, Clinic and Policlinic of Nuclear Medicine, Dresden (Germany); Volker, Teresa; Zoephel, Klaus; Kotzerke, Joerg [University Hospital Dresden, Clinic and Policlinic of Nuclear Medicine, Dresden (Germany); Wiedemann, Baerbel [University Hospital Dresden, Institute of Medical Informatics and Biometrics, Dresden (Germany); Wehrmann, Ursula [University Hospital Dresden, Clinic and Policlinic of Surgery, Dresden (Germany); Koch, Arne; Abolmaali, Nasreddin [University Hospital Dresden, Oncoray, Dresden (Germany); Toussaint, Todd; Luboldt, Hans-Joachim [Multiorgan Screening Foundation, Frankfurt (Germany); Middendorp, Markus; Gruenwald, Frank [University Hospital Frankfurt, Department of Nuclear Medicine, Frankfurt (Germany); Aust, Daniela [University Hospital Dresden, Department of Pathology, Dresden (Germany); Vogl, Thomas J. [University Hospital Frankfurt, Department of Radiology, Frankfurt am Main (Germany)

2010-09-15

To determine the performance of FDG-PET/CT in the detection of relevant colorectal neoplasms (adenomas {>=}10 mm, with high-grade dysplasia, cancer) in relation to CT dose and contrast administration and to find a PET cut-off. 84 patients, who underwent PET/CT and colonoscopy (n=79)/sigmoidoscopy (n=5) for (79 x 6+5 x 2)=484 colonic segments, were included in a retrospective study. The accuracy of low-dose PET/CT in detecting mass-positive segments was evaluated by ROC analysis by two blinded independent reviewers relative to contrast-enhanced PET/CT. On a per-lesion basis characteristic PET values were tested as cut-offs. Low-dose PET/CT and contrast-enhanced PET/CT provide similar accuracies (area under the curve for the average ROC ratings 0.925 vs. 0.929, respectively). PET demonstrated all carcinomas (n=23) and 83% (30/36) of relevant adenomas. In all carcinomas and adenomas with high-grade dysplasia (n=10) the SUV{sub max} was {>=}5. This cut-off resulted in a better per-segment sensitivity and negative predictive value (NPV) than the average PET/CT reviews (sensitivity: 89% vs. 82%; NPV: 99% vs. 98%). All other tested cut-offs were inferior to the SUV{sub max}. FDG-PET/CT provides promising accuracy for colorectal mass detection. Low dose and lack of iodine contrast in the CT component do not impact the accuracy. The PET cut-off SUV{sub max}{>=} 5 improves the accuracy. (orig.)

Detection of relevant colonic neoplasms with PET/CT: promising accuracy with minimal CT dose and a standardised PET cut-off

International Nuclear Information System (INIS)

Luboldt, Wolfgang; Volker, Teresa; Zoephel, Klaus; Kotzerke, Joerg; Wiedemann, Baerbel; Wehrmann, Ursula; Koch, Arne; Abolmaali, Nasreddin; Toussaint, Todd; Luboldt, Hans-Joachim; Middendorp, Markus; Gruenwald, Frank; Aust, Daniela; Vogl, Thomas J.

2010-01-01

To determine the performance of FDG-PET/CT in the detection of relevant colorectal neoplasms (adenomas ≥10 mm, with high-grade dysplasia, cancer) in relation to CT dose and contrast administration and to find a PET cut-off. 84 patients, who underwent PET/CT and colonoscopy (n=79)/sigmoidoscopy (n=5) for (79 x 6+5 x 2)=484 colonic segments, were included in a retrospective study. The accuracy of low-dose PET/CT in detecting mass-positive segments was evaluated by ROC analysis by two blinded independent reviewers relative to contrast-enhanced PET/CT. On a per-lesion basis characteristic PET values were tested as cut-offs. Low-dose PET/CT and contrast-enhanced PET/CT provide similar accuracies (area under the curve for the average ROC ratings 0.925 vs. 0.929, respectively). PET demonstrated all carcinomas (n=23) and 83% (30/36) of relevant adenomas. In all carcinomas and adenomas with high-grade dysplasia (n=10) the SUV max was ≥5. This cut-off resulted in a better per-segment sensitivity and negative predictive value (NPV) than the average PET/CT reviews (sensitivity: 89% vs. 82%; NPV: 99% vs. 98%). All other tested cut-offs were inferior to the SUV max . FDG-PET/CT provides promising accuracy for colorectal mass detection. Low dose and lack of iodine contrast in the CT component do not impact the accuracy. The PET cut-off SUV max ≥ 5 improves the accuracy. (orig.)

Atlas ranking and selection for automatic segmentation of the esophagus from CT scans

Science.gov (United States)

Yang, Jinzhong; Haas, Benjamin; Fang, Raymond; Beadle, Beth M.; Garden, Adam S.; Liao, Zhongxing; Zhang, Lifei; Balter, Peter; Court, Laurence

2017-12-01

In radiation treatment planning, the esophagus is an important organ-at-risk that should be spared in patients with head and neck cancer or thoracic cancer who undergo intensity-modulated radiation therapy. However, automatic segmentation of the esophagus from CT scans is extremely challenging because of the structure’s inconsistent intensity, low contrast against the surrounding tissues, complex and variable shape and location, and random air bubbles. The goal of this study is to develop an online atlas selection approach to choose a subset of optimal atlases for multi-atlas segmentation to the delineate esophagus automatically. We performed atlas selection in two phases. In the first phase, we used the correlation coefficient of the image content in a cubic region between each atlas and the new image to evaluate their similarity and to rank the atlases in an atlas pool. A subset of atlases based on this ranking was selected, and deformable image registration was performed to generate deformed contours and deformed images in the new image space. In the second phase of atlas selection, we used Kullback-Leibler divergence to measure the similarity of local-intensity histograms between the new image and each of the deformed images, and the measurements were used to rank the previously selected atlases. Deformed contours were overlapped sequentially, from the most to the least similar, and the overlap ratio was examined. We further identified a subset of optimal atlases by analyzing the variation of the overlap ratio versus the number of atlases. The deformed contours from these optimal atlases were fused together using a modified simultaneous truth and performance level estimation algorithm to produce the final segmentation. The approach was validated with promising results using both internal data sets (21 head and neck cancer patients and 15 thoracic cancer patients) and external data sets (30 thoracic patients).

Radiation dose reduction through combining positron emission tomography/computed tomography (PET/CT) and diagnostic CT in children and young adults with lymphoma.

Science.gov (United States)

Qi, Zhihua; Gates, Erica L; O'Brien, Maureen M; Trout, Andrew T

2018-02-01

Both [F-18]2-fluoro-2-deoxyglucose positron emission tomography/computed tomography ( 18 F-FDG PET/CT) and diagnostic CT are at times required for lymphoma staging. This means some body segments are exposed twice to X-rays for generation of CT data (diagnostic CT + localization CT). To describe a combined PET/diagnostic CT approach that modulates CT tube current along the z-axis, providing diagnostic CT of some body segments and localization CT of the remaining body segments, thereby reducing patient radiation dose. We retrospectively compared total patient radiation dose between combined PET/diagnostic CT and separately acquired PET/CT and diagnostic CT exams. When available, we calculated effective doses for both approaches in the same patient; otherwise, we used data from patients of similar size. To confirm image quality, we compared image noise (Hounsfield unit [HU] standard deviation) as measured in the liver on both combined and separately acquired diagnostic CT images. We used t-tests for dose comparisons and two one-sided tests for image-quality equivalence testing. Mean total effective dose for the CT component of the combined and separately acquired diagnostic CT exams were 6.20±2.69 and 8.17±2.61 mSv, respectively (PCT effective dose. Image noise was not statistically significantly different between approaches (12.2±1.8 HU vs. 11.7±1.5 HU for the combined and separately acquired diagnostic CT images, respectively). A combined PET/diagnostic CT approach as described offers dose savings at similar image quality for children and young adults with lymphoma who have indications for both PET and diagnostic CT examinations.

TH-CD-207B-06: Swank Factor of Segmented Scintillators in Multi-Slice CT Detectors: Pulse Height Spectra and Light Escape

Energy Technology Data Exchange (ETDEWEB)

Howansky, A; Peng, B; Lubinsky, A; Zhao, W [Stony Brook University, Stony Brook, NY (United States)

2016-06-15

Purpose: Pulse height spectra (PHS) have been used to determine the Swank factor of a scintillator by measuring fluctuations in its light output per x-ray interaction. The Swank factor and x-ray quantum efficiency of a scintillator define the upper limit to its imaging performance, i.e. DQE(0). The Swank factor below the K-edge is dominated by optical properties, i.e. variations in light escape efficiency from different depths of interaction, denoted e(z). These variations can be optimized to improve tradeoffs in x-ray absorption, light yield, and spatial resolution. This work develops a quantitative model for interpreting measured PHS, and estimating e(z) on an absolute scale. The method is used to investigate segmented ceramic GOS scintillators used in multi-slice CT detectors. Methods: PHS of a ceramic GOS plate (1 mm thickness) and segmented GOS array (1.4 mm thick) were measured at 46 keV. Signal and noise propagation through x-ray conversion gain, light escape, detection by a photomultiplier tube and dynode amplification were modeled using a cascade of stochastic gain stages. PHS were calculated with these expressions and compared to measurements. Light escape parameters were varied until modeled PHS agreed with measurements. The resulting estimates of e(z) were used to calculate PHS without measurement noise to determine the inherent Swank factor. Results: The variation in e(z) was 67.2–89.7% in the plate and 40.2–70.8% in the segmented sample, corresponding to conversion gains of 28.6–38.1 keV{sup −1} and 17.1–30.1 keV{sup −1}, respectively. The inherent Swank factors of the plate and segmented sample were 0.99 and 0.95, respectively. Conclusion: The high light escape efficiency in the ceramic GOS samples yields high Swank factors and DQE(0) in CT applications. The PHS model allows the intrinsic optical properties of scintillators to be deduced from PHS measurements, thus it provides new insights for evaluating the imaging performance of

Validation of an enhanced knowledge-based method for segmentation and quantitative analysis of intrathoracic airway trees from three-dimensional CT images

International Nuclear Information System (INIS)

Sonka, M.; Park, W.; Hoffman, E.A.

1995-01-01

Accurate assessment of airway physiology, evaluated in terms of geometric changes, is critically dependent upon the accurate imaging and image segmentation of the three-dimensional airway tree structure. The authors have previously reported a knowledge-based method for three-dimensional airway tree segmentation from high resolution CT (HRCT) images. Here, they report a substantially improved version of the method. In the current implementation, the method consists of several stages. First, the lung borders are automatically determined in the three-dimensional set of HRCT data. The primary airway tree is semi-automatically identified. In the next stage, potential airways are determined in individual CT slices using a rule-based system that uses contextual information and a priori knowledge about pulmonary anatomy. Using three-dimensional connectivity properties of the pulmonary airway tree, the three-dimensional tree is constructed from the set of adjacent slices. The method's performance and accuracy were assessed in five 3D HRCT canine images. Computer-identified airways matched 226/258 observer-defined airways (87.6%); the computer method failed to detect the airways in the remaining 32 locations. By visual assessment of rendered airway trees, the experienced observers judged the computer-detected airway trees as highly realistic

Attenuation correction for the HRRT PET-scanner using transmission scatter correction and total variation regularization.

Science.gov (United States)

Keller, Sune H; Svarer, Claus; Sibomana, Merence

2013-09-01

In the standard software for the Siemens high-resolution research tomograph (HRRT) positron emission tomography (PET) scanner the most commonly used segmentation in the μ -map reconstruction for human brain scans is maximum a posteriori for transmission (MAP-TR). Bias in the lower cerebellum and pons in HRRT brain images have been reported. The two main sources of the problem with MAP-TR are poor bone/soft tissue segmentation below the brain and overestimation of bone mass in the skull. We developed the new transmission processing with total variation (TXTV) method that introduces scatter correction in the μ-map reconstruction and total variation filtering to the transmission processing. Comparing MAP-TR and the new TXTV with gold standard CT-based attenuation correction, we found that TXTV has less bias as compared to MAP-TR. We also compared images acquired at the HRRT scanner using TXTV to the GE Advance scanner images and found high quantitative correspondence. TXTV has been used to reconstruct more than 4000 HRRT scans at seven different sites with no reports of biases. TXTV-based reconstruction is recommended for human brain scans on the HRRT.

Wireless power transmission for battery charging

Science.gov (United States)

Mi, Chris; Li, Siqi; Nguyen, Trong-Duy; Wang, Junhua; Li, Jiangui; Li, Weihan; Xu, Jun

2016-11-15

A wireless power transmission system is provided for high power applications. The power transmission system is comprised generally of a charging unit configured to generate an alternating electromagnetic field and a receive unit configured to receive the alternating electromagnetic field from the charging unit. The charging unit includes a power source; an input rectifier; an inverter; and a transmit coil. The transmit coil has a spirangle arrangement segmented into n coil segments with capacitors interconnecting adjacent coil segments. The receive unit includes a receive coil and an output rectifier. The receive coil also has a spirangle arrangement segmented into m coil segments with capacitors interconnecting adjacent coil segments.

Iatrogenic injury in the lateral segment of the liver after pancreatoduodenectomy: Early follow up CT features and clinical implications

Energy Technology Data Exchange (ETDEWEB)

Kim, Yaena; Yu, Jeong Sik; Chung, Jae Joon; Kim, Joo Hee; Cho, Eun Suk; Ahn, Jhii Hyun; Kim, Ki Whang [Yonsei Univ. College of Medicine, Seoul (Korea, Republic of)

2012-05-15

To investigate the incidence, predisposing factors and image features of iatrogenically induced focal parenchymal changes in the lateral segment of the liver after a pancreatoduodenectomy. A follow up CT taken on the seventh day after an uneventful pancreatoduodenectomy were retrospectively reviewed for 123 patients for newly developed focal hepatic lesions. The location, size, and shape of the lesions were analyzed along with preoperative anatomic variation of the hepatic artery, for the degree of intrahepatic bile duct dilatation and procedure duration. Other than two patients with hepatic metastases, 13 (10.6%) patients showed newly developed irregular (n = 9), linear (n = 2) or wedge like (n = 2) hypovascular areas (1.4-8.5 cm; mean, 2.8 cm) in the posterior subcapsular portion of the lateral segment. There were only two patients (15.4%) with an aberrant origin of the segmental hepatic artery from the left gastric artery, and the degree of bile duct dilatation was nonspecific for the 13 subjected patients. Mean procedure time was not significantly different between the subjected patients and the others (541 min vs. 507 min; p = 0.160). Focal iatrogenic injury in the lateral segment after a pancreatoduodenectomy would not be a common event regardless of preoperative vascular anatomic variation, bile duct dilatation, or procedure duration.

Iatrogenic injury in the lateral segment of the liver after pancreatoduodenectomy: Early follow up CT features and clinical implications

International Nuclear Information System (INIS)

Kim, Yaena; Yu, Jeong Sik; Chung, Jae Joon; Kim, Joo Hee; Cho, Eun Suk; Ahn, Jhii Hyun; Kim, Ki Whang

2012-01-01

To investigate the incidence, predisposing factors and image features of iatrogenically induced focal parenchymal changes in the lateral segment of the liver after a pancreatoduodenectomy. A follow up CT taken on the seventh day after an uneventful pancreatoduodenectomy were retrospectively reviewed for 123 patients for newly developed focal hepatic lesions. The location, size, and shape of the lesions were analyzed along with preoperative anatomic variation of the hepatic artery, for the degree of intrahepatic bile duct dilatation and procedure duration. Other than two patients with hepatic metastases, 13 (10.6%) patients showed newly developed irregular (n = 9), linear (n = 2) or wedge like (n = 2) hypovascular areas (1.4-8.5 cm; mean, 2.8 cm) in the posterior subcapsular portion of the lateral segment. There were only two patients (15.4%) with an aberrant origin of the segmental hepatic artery from the left gastric artery, and the degree of bile duct dilatation was nonspecific for the 13 subjected patients. Mean procedure time was not significantly different between the subjected patients and the others (541 min vs. 507 min; p = 0.160). Focal iatrogenic injury in the lateral segment after a pancreatoduodenectomy would not be a common event regardless of preoperative vascular anatomic variation, bile duct dilatation, or procedure duration

Radiation dose reduction through combining positron emission tomography/computed tomography (PET/CT) and diagnostic CT in children and young adults with lymphoma

International Nuclear Information System (INIS)

Qi, Zhihua; Gates, Erica L.; Trout, Andrew T.; O'Brien, Maureen M.

2018-01-01

Both [F-18]2-fluoro-2-deoxyglucose positron emission tomography/computed tomography ( 18 F-FDG PET/CT) and diagnostic CT are at times required for lymphoma staging. This means some body segments are exposed twice to X-rays for generation of CT data (diagnostic CT + localization CT). To describe a combined PET/diagnostic CT approach that modulates CT tube current along the z-axis, providing diagnostic CT of some body segments and localization CT of the remaining body segments, thereby reducing patient radiation dose. We retrospectively compared total patient radiation dose between combined PET/diagnostic CT and separately acquired PET/CT and diagnostic CT exams. When available, we calculated effective doses for both approaches in the same patient; otherwise, we used data from patients of similar size. To confirm image quality, we compared image noise (Hounsfield unit [HU] standard deviation) as measured in the liver on both combined and separately acquired diagnostic CT images. We used t-tests for dose comparisons and two one-sided tests for image-quality equivalence testing. Mean total effective dose for the CT component of the combined and separately acquired diagnostic CT exams were 6.20±2.69 and 8.17±2.61 mSv, respectively (P<0.0001). Average dose savings with the combined approach was 24.8±17.8% (2.60±2.51 mSv [range: 0.32-4.72 mSv]) of total CT effective dose. Image noise was not statistically significantly different between approaches (12.2±1.8 HU vs. 11.7±1.5 HU for the combined and separately acquired diagnostic CT images, respectively). A combined PET/diagnostic CT approach as described offers dose savings at similar image quality for children and young adults with lymphoma who have indications for both PET and diagnostic CT examinations. (orig.)

Radiation dose reduction through combining positron emission tomography/computed tomography (PET/CT) and diagnostic CT in children and young adults with lymphoma

Energy Technology Data Exchange (ETDEWEB)

Qi, Zhihua; Gates, Erica L.; Trout, Andrew T. [Cincinnati Children' s Hospital Medical Center, Department of Radiology, Cincinnati, OH (United States); O' Brien, Maureen M. [Cincinnati Children' s Hospital Medical Center, Division of Oncology, Cancer and Blood Disease Institute, Cincinnati, OH (United States)

2018-02-15

Both [F-18]2-fluoro-2-deoxyglucose positron emission tomography/computed tomography ({sup 18}F-FDG PET/CT) and diagnostic CT are at times required for lymphoma staging. This means some body segments are exposed twice to X-rays for generation of CT data (diagnostic CT + localization CT). To describe a combined PET/diagnostic CT approach that modulates CT tube current along the z-axis, providing diagnostic CT of some body segments and localization CT of the remaining body segments, thereby reducing patient radiation dose. We retrospectively compared total patient radiation dose between combined PET/diagnostic CT and separately acquired PET/CT and diagnostic CT exams. When available, we calculated effective doses for both approaches in the same patient; otherwise, we used data from patients of similar size. To confirm image quality, we compared image noise (Hounsfield unit [HU] standard deviation) as measured in the liver on both combined and separately acquired diagnostic CT images. We used t-tests for dose comparisons and two one-sided tests for image-quality equivalence testing. Mean total effective dose for the CT component of the combined and separately acquired diagnostic CT exams were 6.20±2.69 and 8.17±2.61 mSv, respectively (P<0.0001). Average dose savings with the combined approach was 24.8±17.8% (2.60±2.51 mSv [range: 0.32-4.72 mSv]) of total CT effective dose. Image noise was not statistically significantly different between approaches (12.2±1.8 HU vs. 11.7±1.5 HU for the combined and separately acquired diagnostic CT images, respectively). A combined PET/diagnostic CT approach as described offers dose savings at similar image quality for children and young adults with lymphoma who have indications for both PET and diagnostic CT examinations. (orig.)

Spatial relationship between intrahepatic artery and portal vein based on the fusion image of CT-arterial portography (CTAP) and CT-angiography (CTA): New classification for hepatic artery at hepatic hilum and the segmentation of right anterior section of the liver

International Nuclear Information System (INIS)

Ibukuro, Kenji; Takeguchi, Takaya; Fukuda, Hozumi; Abe, Shoko; Tobe, Kimiko; Tanaka, Rei; Tagawa, Kazumi

2012-01-01

Purpose: To clarify the variations of the intrahepatic artery and portal vein and to verify the proper segmentation for the right anterior section of the liver. Materials and methods: CT during arterial portography and CT angiography were performed on 64-slice multi detector row CT in 147 patients. All images were transferred to a workstation for analysis using multi-image-fusion mode. We investigated the spatial relationship between hepatic artery and portal vein in the right hemiliver and the segmentation of the right anterior hepatic artery and portal vein. Results: The spatial anatomy of right hepatic arteries and portal vein was (1) anterior and posterior hepatic artery run superior and inferior to anterior portal vein, respectively (47.6%), (2) one anterior hepatic artery runs superior to and another one runs inferior to anterior portal vein (15%), (3) anterior and posterior hepatic arteries run superior to anterior portal vein (11.6%), (4) anterior and posterior hepatic arteries run inferior to anterior portal vein (7.5%), and (5) one posterior hepatic artery runs superior to and another one runs inferior to anterior portal vein (6.8%). The combined anatomy of right anterior artery and portal vein with regard to segmentation was classified as (1) dorso-ventral (26.5%), (2) dorso-ventral and inferior (10.9%), (3) multiple (18.4%), and (4) superior and inferior segments (1.4%). Conclusion: There are various types of spatial anatomy of intrahepatic artery and portal vein. The hepatic arteries as well as portal veins of right anterior section of the liver could be divided into dorsal and ventral, not superior and inferior.

Respiratory-gated segment reconstruction for radiation treatment planning using 256-slice CT-scanner during free breathing

Science.gov (United States)

Mori, Shinichiro; Endo, Masahiro; Kohno, Ryosuke; Minohara, Shinichi; Kohno, Kazutoshi; Asakura, Hiroshi; Fujiwara, Hideaki; Murase, Kenya

2005-04-01

The conventional respiratory-gated CT scan technique includes anatomic motion induced artifacts due to the low temporal resolution. They are a significant source of error in radiotherapy treatment planning for the thorax and upper abdomen. Temporal resolution and image quality are important factors to minimize planning target volume margin due to the respiratory motion. To achieve high temporal resolution and high signal-to-noise ratio, we developed a respiratory gated segment reconstruction algorithm and adapted it to Feldkamp-Davis-Kress algorithm (FDK) with a 256-detector row CT. The 256-detector row CT could scan approximately 100 mm in the cranio-caudal direction with 0.5 mm slice thickness in one rotation. Data acquisition for the RS-FDK relies on the assistance of the respiratory sensing system by a cine scan mode (table remains stationary). We evaluated RS-FDK in phantom study with the 256-detector row CT and compared it with full scan (FS-FDK) and HS-FDK results with regard to volume accuracy and image noise, and finally adapted the RS-FDK to an animal study. The RS-FDK gave a more accurate volume than the others and it had the same signal-to-noise ratio as the FS-FDK. In the animal study, the RS-FDK visualized the clearest edges of the liver and pulmonary vessels of all the algorithms. In conclusion, the RS-FDK algorithm has a capability of high temporal resolution and high signal-to-noise ratio. Therefore it will be useful when combined with new radiotherapy techniques including image guided radiation therapy (IGRT) and 4D radiation therapy.

Computerized analysis of coronary artery disease: Performance evaluation of segmentation and tracking of coronary arteries in CT angiograms

Energy Technology Data Exchange (ETDEWEB)

Zhou, Chuan, E-mail: chuan@umich.edu; Chan, Heang-Ping; Chughtai, Aamer; Kuriakose, Jean; Agarwal, Prachi; Kazerooni, Ella A.; Hadjiiski, Lubomir M.; Patel, Smita; Wei, Jun [Department of Radiology, University of Michigan, Ann Arbor, Michigan 48109 (United States)

2014-08-15

Purpose: The authors are developing a computer-aided detection system to assist radiologists in analysis of coronary artery disease in coronary CT angiograms (cCTA). This study evaluated the accuracy of the authors’ coronary artery segmentation and tracking method which are the essential steps to define the search space for the detection of atherosclerotic plaques. Methods: The heart region in cCTA is segmented and the vascular structures are enhanced using the authors’ multiscale coronary artery response (MSCAR) method that performed 3D multiscale filtering and analysis of the eigenvalues of Hessian matrices. Starting from seed points at the origins of the left and right coronary arteries, a 3D rolling balloon region growing (RBG) method that adapts to the local vessel size segmented and tracked each of the coronary arteries and identifies the branches along the tracked vessels. The branches are queued and subsequently tracked until the queue is exhausted. With Institutional Review Board approval, 62 cCTA were collected retrospectively from the authors’ patient files. Three experienced cardiothoracic radiologists manually tracked and marked center points of the coronary arteries as reference standard following the 17-segment model that includes clinically significant coronary arteries. Two radiologists visually examined the computer-segmented vessels and marked the mistakenly tracked veins and noisy structures as false positives (FPs). For the 62 cases, the radiologists marked a total of 10191 center points on 865 visible coronary artery segments. Results: The computer-segmented vessels overlapped with 83.6% (8520/10191) of the center points. Relative to the 865 radiologist-marked segments, the sensitivity reached 91.9% (795/865) if a true positive is defined as a computer-segmented vessel that overlapped with at least 10% of the reference center points marked on the segment. When the overlap threshold is increased to 50% and 100%, the sensitivities were 86

Prevalence of first-pass myocardial perfusion defects detected by contrast-enhanced dual-source CT in patients with non-ST segment elevation acute coronary syndromes

Energy Technology Data Exchange (ETDEWEB)

Schepis, Tiziano; Achenbach, Stephan; Marwan, Mohamed; Muschiol, Gerd; Ropers, Dieter; Daniel, Werner G.; Pflederer, Tobias [University of Erlangen, Department of Internal Medicine 2 (Cardiology), Erlangen (Germany)

2010-07-15

To investigate the prevalence and diagnostic value of first-pass myocardial perfusion defects (PD) visualised by contrast-enhanced multidetector computed tomography (MDCT) in patients admitted for a first acute coronary syndrome (ACS). Thirty-eight patients with non-ST segment elevation myocardial infarction (NSTEMI) or unstable angina (UA) and scheduled for percutaneous coronary intervention underwent dual-source CT immediately before catheterisation. CT images were analysed for the presence of any PD by using a 17-segment model. Results were compared with peak cardiac troponin-I (cTnI) and angiography findings. PD were seen in 21 of the 24 patients with NSTEMI (median peak cTnI level 7.07 ng/mL; range 0.72-37.07 ng/mL) and in 2 of 14 patients with UA. PD corresponded with the territory of the infarct-related artery in 20 out of 22 patients. In a patient-based analysis, sensitivity, specificity, negative and positive predictive values of any PD for predicting NSTEMI were 88%, 86%, 80% and 91%. Per culprit artery, the respective values were 86%, 75%, 80% and 83%. In patients with non-ST segment elevation ACS, first-pass myocardial PD in contrast-enhanced MDCT correlate closely with the presence of myocardial necrosis, as determined by increases in cTnI levels. (orig.)

Automated segmentation of knee and ankle regions of rats from CT images to quantify bone mineral density for monitoring treatments of rheumatoid arthritis

Science.gov (United States)

Cruz, Francisco; Sevilla, Raquel; Zhu, Joe; Vanko, Amy; Lee, Jung Hoon; Dogdas, Belma; Zhang, Weisheng

2014-03-01

Bone mineral density (BMD) obtained from a CT image is an imaging biomarker used pre-clinically for characterizing the Rheumatoid arthritis (RA) phenotype. We use this biomarker in animal studies for evaluating disease progression and for testing various compounds. In the current setting, BMD measurements are obtained manually by selecting the regions of interest from three-dimensional (3-D) CT images of rat legs, which results in a laborious and low-throughput process. Combining image processing techniques, such as intensity thresholding and skeletonization, with mathematical techniques in curve fitting and curvature calculations, we developed an algorithm for quick, consistent, and automatic detection of joints in large CT data sets. The implemented algorithm has reduced analysis time for a study with 200 CT images from 10 days to 3 days and has improved the robust detection of the obtained regions of interest compared with manual segmentation. This algorithm has been used successfully in over 40 studies.

Color-coded volume rendering for three-dimensional reconstructions of CT data

International Nuclear Information System (INIS)

Rieker, O.; Mildenberger, P.; Thelen, M.

1999-01-01

Purpose: To evaluate a technique of colored three-dimensional reconstructions without segmentation. Material and methods: Color-coded volume rendered images were reconstructed from the volume data of 25 thoracic, abdominal, musculoskeletal, and vascular helical CT scans using commercial software. The CT volume rendered voxels were encoded with color in the following manner. Opacity, hue, lightness, and chroma were assigned to each of four classes defined by CT number. Color-coded reconstructions were compared to the corresponding grey-scale coded reconstructions. Results: Color-coded volume rendering enabled realistic visualization of pathologic findings when there was sufficient difference in CT density. Segmentation was necessary in some cases to demonstrate small details in a complex volume. Conclusion: Color-coded volume rendering allowed lifelike visualisation of CT volumes without the need of segmentation in most cases. (orig.) [de

Pleural effusion segmentation in thin-slice CT

Science.gov (United States)

Donohue, Rory; Shearer, Andrew; Bruzzi, John; Khosa, Huma

2009-02-01

A pleural effusion is excess fluid that collects in the pleural cavity, the fluid-filled space that surrounds the lungs. Surplus amounts of such fluid can impair breathing by limiting the expansion of the lungs during inhalation. Measuring the fluid volume is indicative of the effectiveness of any treatment but, due to the similarity to surround regions, fragments of collapsed lung present and topological changes; accurate quantification of the effusion volume is a difficult imaging problem. A novel code is presented which performs conditional region growth to accurately segment the effusion shape across a dataset. We demonstrate the applicability of our technique in the segmentation of pleural effusion and pulmonary masses.

Multi-atlas segmentation for abdominal organs with Gaussian mixture models

Science.gov (United States)

Burke, Ryan P.; Xu, Zhoubing; Lee, Christopher P.; Baucom, Rebeccah B.; Poulose, Benjamin K.; Abramson, Richard G.; Landman, Bennett A.

2015-03-01

Abdominal organ segmentation with clinically acquired computed tomography (CT) is drawing increasing interest in the medical imaging community. Gaussian mixture models (GMM) have been extensively used through medical segmentation, most notably in the brain for cerebrospinal fluid / gray matter / white matter differentiation. Because abdominal CT exhibit strong localized intensity characteristics, GMM have recently been incorporated in multi-stage abdominal segmentation algorithms. In the context of variable abdominal anatomy and rich algorithms, it is difficult to assess the marginal contribution of GMM. Herein, we characterize the efficacy of an a posteriori framework that integrates GMM of organ-wise intensity likelihood with spatial priors from multiple target-specific registered labels. In our study, we first manually labeled 100 CT images. Then, we assigned 40 images to use as training data for constructing target-specific spatial priors and intensity likelihoods. The remaining 60 images were evaluated as test targets for segmenting 12 abdominal organs. The overlap between the true and the automatic segmentations was measured by Dice similarity coefficient (DSC). A median improvement of 145% was achieved by integrating the GMM intensity likelihood against the specific spatial prior. The proposed framework opens the opportunities for abdominal organ segmentation by efficiently using both the spatial and appearance information from the atlases, and creates a benchmark for large-scale automatic abdominal segmentation.

Abdomen and spinal cord segmentation with augmented active shape models.

Science.gov (United States)

Xu, Zhoubing; Conrad, Benjamin N; Baucom, Rebeccah B; Smith, Seth A; Poulose, Benjamin K; Landman, Bennett A

2016-07-01

Active shape models (ASMs) have been widely used for extracting human anatomies in medical images given their capability for shape regularization of topology preservation. However, sensitivity to model initialization and local correspondence search often undermines their performances, especially around highly variable contexts in computed-tomography (CT) and magnetic resonance (MR) images. In this study, we propose an augmented ASM (AASM) by integrating the multiatlas label fusion (MALF) and level set (LS) techniques into the traditional ASM framework. Using AASM, landmark updates are optimized globally via a region-based LS evolution applied on the probability map generated from MALF. This augmentation effectively extends the searching range of correspondent landmarks while reducing sensitivity to the image contexts and improves the segmentation robustness. We propose the AASM framework as a two-dimensional segmentation technique targeting structures with one axis of regularity. We apply AASM approach to abdomen CT and spinal cord (SC) MR segmentation challenges. On 20 CT scans, the AASM segmentation of the whole abdominal wall enables the subcutaneous/visceral fat measurement, with high correlation to the measurement derived from manual segmentation. On 28 3T MR scans, AASM yields better performances than other state-of-the-art approaches in segmenting white/gray matter in SC.

Cardiac 82rubidium PET/CT: initial European experience

International Nuclear Information System (INIS)

Groves, Ashley M.; Dickson, John C.; Kayani, Irfan; Endozo, Raymondo; Blanchard, Patty; Shastry, Manu; Prvulovich, Elizabeth; Waddington, Wendy A.; Ben-Haim, Simona; Bomanji, Jamshed B.; Ell, Peter J.; Speechly-Dick, Marie-Elsya; McEwan, Jean R.

2007-01-01

Myocardial perfusion with PET/CT has advantages over conventional SPECT. We describe our initial European experience using 82 Rubidium-PET/CT, as part of a clinical myocardial perfusion service. We studied the first 100 patients (64 male; 36 female, mean age = 60: SD +/-12.5y, mean body mass index = 30: SD +/-6.9kg/m 2 ) who underwent 82 Rubidium cardiac PET/CT in our institution. Thirty patients had recently undergone coronary angiography. Patients underwent imaging during adenosine infusion and at rest. Images were acquired over 5 minutes using a GE-PET/CT instrument. Image quality was described as good, adequate or inadequate. Images were reported patient-by-patient by a minimum of five nuclear medicine physicians. A segment-by-segment analysis (17-segment model) was also performed. Image quality was good in 77%, adequate 23% and inadequate 0%. There was no statistical difference in image quality between obese and non-obese patients (Fisher's exact test, p = 0.2864). 59% had normal perfusion studies, 29% had inducible ischaemia, 12% had myocardial infarction (11% with super added ischaemia). There was reduced 82 Rubidium uptake in 132/1700 segments during stress. There was reduced 82 Rubidium uptake at rest in 42/1700 segments. The 82 Rubidium PET/CT findings were consistent with the angiographic findings in 28/30 cases. We show that, even from initial use of 82 Rubidium, it is possible to perform myocardial perfusion studies quickly with good image quality, even in the obese. The PET findings correlated well in the third of the cases where angiography was available. As such, 82 Rubidium cardiac PET/CT is likely to be an exciting addition to the European nuclear physician/ cardiologist's radionuclide imaging arsenal. (orig.)

Multi-atlas pancreas segmentation: Atlas selection based on vessel structure.

Science.gov (United States)

Karasawa, Ken'ichi; Oda, Masahiro; Kitasaka, Takayuki; Misawa, Kazunari; Fujiwara, Michitaka; Chu, Chengwen; Zheng, Guoyan; Rueckert, Daniel; Mori, Kensaku

2017-07-01

Automated organ segmentation from medical images is an indispensable component for clinical applications such as computer-aided diagnosis (CAD) and computer-assisted surgery (CAS). We utilize a multi-atlas segmentation scheme, which has recently been used in different approaches in the literature to achieve more accurate and robust segmentation of anatomical structures in computed tomography (CT) volume data. Among abdominal organs, the pancreas has large inter-patient variability in its position, size and shape. Moreover, the CT intensity of the pancreas closely resembles adjacent tissues, rendering its segmentation a challenging task. Due to this, conventional intensity-based atlas selection for pancreas segmentation often fails to select atlases that are similar in pancreas position and shape to those of the unlabeled target volume. In this paper, we propose a new atlas selection strategy based on vessel structure around the pancreatic tissue and demonstrate its application to a multi-atlas pancreas segmentation. Our method utilizes vessel structure around the pancreas to select atlases with high pancreatic resemblance to the unlabeled volume. Also, we investigate two types of applications of the vessel structure information to the atlas selection. Our segmentations were evaluated on 150 abdominal contrast-enhanced CT volumes. The experimental results showed that our approach can segment the pancreas with an average Jaccard index of 66.3% and an average Dice overlap coefficient of 78.5%. Copyright © 2017 Elsevier B.V. All rights reserved.

Prospectively ECG-triggered sequential dual-source coronary CT angiography in patients with atrial fibrillation: comparison with retrospectively ECG-gated helical CT

Energy Technology Data Exchange (ETDEWEB)

Xu, Lei; Yang, Lin; Zhang, Zhaoqi [Capital Medical University, Department of Radiology, Beijing Anzhen Hospital, Beijing (China); Wang, Yining; Jin, Zhengyu [Chinese Academy of Medical Sciences, Department of Radiology, Peking Union Medical College Hospital, Beijing (China); Zhang, Longjiang; Lu, Guangming [Nanjing University, Department of Medical Imaging, Jinling Hospital, Clinical School of Medical College, Nanjing, Jiangsu (China)

2013-07-15

To investigate the feasibility of applying prospectively ECG-triggered sequential coronary CT angiography (CCTA) to patients with atrial fibrillation (AF) and evaluate the image quality and radiation dose compared with a retrospectively ECG-gated helical protocol. 100 patients with persistent AF were enrolled. Fifty patients were randomly assigned to a prospective protocol and the other patients to a retrospective protocol using a second-generation dual-source CT (DS-CT). Image quality was evaluated using a four-point grading scale (1 = excellent, 2 = good, 3 = moderate, 4 = poor) by two reviewers on a per-segment basis. The coronary artery segments were considered non-diagnostic with a quality score of 4. The radiation dose was evaluated. Diagnostic segment rate in the prospective group was 99.4 % (642/646 segments), while that in the retrospective group was 96.5 % (604/626 segments) (P < 0.001). Effective dose was 4.29 {+-} 1.86 and 11.95 {+-} 5.34 mSv for each of the two protocols (P < 0.001), which was a 64 % reduction in the radiation dose for prospective sequential imaging compared with retrospective helical imaging. In AF patients, prospectively ECG-triggered sequential CCTA is feasible using second-generation DS-CT and can decrease >60 % radiation exposure compared with retrospectively ECG-gated helical imaging while improving diagnostic image quality. (orig.)

Automated assessment of aortic and main pulmonary arterial diameters using model-based blood vessel segmentation for predicting chronic thromboembolic pulmonary hypertension in low-dose CT lung screening

Science.gov (United States)

Suzuki, Hidenobu; Kawata, Yoshiki; Niki, Noboru; Sugiura, Toshihiko; Tanabe, Nobuhiro; Kusumoto, Masahiko; Eguchi, Kenji; Kaneko, Masahiro

2018-02-01

Chronic thromboembolic pulmonary hypertension (CTEPH) is characterized by obstruction of the pulmonary vasculature by residual organized thrombi. A morphological abnormality inside mediastinum of CTEPH patient is enlargement of pulmonary artery. This paper presents an automated assessment of aortic and main pulmonary arterial diameters for predicting CTEPH in low-dose CT lung screening. The distinctive feature of our method is to segment aorta and main pulmonary artery using both of prior probability and vascular direction which were estimated from mediastinal vascular region using principal curvatures of four-dimensional hyper surface. The method was applied to two datasets, 64 lowdose CT scans of lung cancer screening and 19 normal-dose CT scans of CTEPH patients through the training phase with 121 low-dose CT scans. This paper demonstrates effectiveness of our method for predicting CTEPH in low-dose CT screening.

Dependent lung opacity at thin-section CT: evaluation by spirometrically-gated CT of the influence of lung volume

International Nuclear Information System (INIS)

Lee, Ki Nam; Yoon, Seong Kuk; Sohn, Choon Hee; Choi, Pil Jo; Webb, W. Richard

2002-01-01

To evaluate the influence of lung volume on dependent lung opacity seen at thin-section CT. In thirteen healthy volunteers, thin-section CT scans were performed at three levels (upper, mid, and lower portion of the lung) and at different lung volumes (10, 30, 50, and 100% vital capacity), using spirometric gated CT. Using a three-point scale, two radiologists determined whether dependent opacity was present, and estimated its degree. Regional lung attenuation at a level 2 cm above the diaphragm was determined using semiautomatic segmentation, and the diameter of a branch of the right lower posterior basal segmental artery was measured at each different vital capacity. At all three anatomic levels, dependent opacity occurred significantly more often at lower vital capacities (10, 30%) than at 100% vital capacity (p = 0.001). Visually estimated dependent opacity was significantly related to regional lung attenuation (p < 0.0001), which in dependent areas progressively increased as vital capacity decreased (p < 0.0001). The presence of dependent opacity and regional lung attenuation of a dependent area correlated significantly with increased diameter of a segmental arterial branch (r = 0.493 and p = 0.0002; r = 0.486 and p 0.0003, respectively). Visual estimation and CT measurements of dependent opacity obtained by semiautomatic segmentation are significantly influenced by lung volume and are related to vascular diameter

Cardiac CT angiography after coronary artery surgery in children using 64-slice CT scan

International Nuclear Information System (INIS)

Marini, Davide; Agnoletti, Gabriella; Brunelle, Francis; Sidi, Daniel; Bonnet, Damien; Ou, Phalla

2009-01-01

Objective: The purpose of this study was to compare the diagnostic accuracy of 64-slice CT with that of invasive angiography in the detection of graft and/or coronary angioplasty stenosis in children who had undergone coronary artery surgery. Population and methods: Fifteen consecutive children (8 male and 7 female; age 9.2 ± 6.1 years) underwent 64-slice CT because of chest pain or ECG changes mean 4.8 ± 3.7 years after surgical coronary artery surgery; 10 patients had coronary angioplasty using a patch from the saphenous vein, four had mammary artery bypass, and one had saphenous vein bypass. Six main segments of the coronary arteries and all the bypass graft considered as a single segment were analyzed and compared with invasive angiography used as the reference standard. Results: CT correctly identified the four children with coronary angioplasty and mammary graft lesions that were confirmed by conventional angiography: one patient had a significant stenosis (>50% stenosis) at the mammary bypass graft anastomosis site; three other had non-significant stenosis (<50% stenosis) including a mild lesion of the saphenous vein patch in two patients and a mild lesion at the anastomosis site of the mammary bypass in one. All segments identified as normal by CT in the other 11 children were also found to be normal by conventional angiography. Conclusion: In centers expert in this technique, 64-slice CT scanning is a promising, rapid, and useful diagnostic technique for evaluating both coronary angioplasty and bypass graft lesions in children who had undergone coronary artery surgery.

Automated segmentation of pulmonary structures in thoracic computed tomography scans: a review

International Nuclear Information System (INIS)

Van Rikxoort, Eva M; Van Ginneken, Bram

2013-01-01

Computed tomography (CT) is the modality of choice for imaging the lungs in vivo. Sub-millimeter isotropic images of the lungs can be obtained within seconds, allowing the detection of small lesions and detailed analysis of disease processes. The high resolution of thoracic CT and the high prevalence of lung diseases require a high degree of automation in the analysis pipeline. The automated segmentation of pulmonary structures in thoracic CT has been an important research topic for over a decade now. This systematic review provides an overview of current literature. We discuss segmentation methods for the lungs, the pulmonary vasculature, the airways, including airway tree construction and airway wall segmentation, the fissures, the lobes and the pulmonary segments. For each topic, the current state of the art is summarized, and topics for future research are identified. (topical review)

Volumetric quantification of bone-implant contact using micro-computed tomography analysis based on region-based segmentation.

Science.gov (United States)

Kang, Sung-Won; Lee, Woo-Jin; Choi, Soon-Chul; Lee, Sam-Sun; Heo, Min-Suk; Huh, Kyung-Hoe; Kim, Tae-Il; Yi, Won-Jin

2015-03-01

We have developed a new method of segmenting the areas of absorbable implants and bone using region-based segmentation of micro-computed tomography (micro-CT) images, which allowed us to quantify volumetric bone-implant contact (VBIC) and volumetric absorption (VA). The simple threshold technique generally used in micro-CT analysis cannot be used to segment the areas of absorbable implants and bone. Instead, a region-based segmentation method, a region-labeling method, and subsequent morphological operations were successively applied to micro-CT images. The three-dimensional VBIC and VA of the absorbable implant were then calculated over the entire volume of the implant. Two-dimensional (2D) bone-implant contact (BIC) and bone area (BA) were also measured based on the conventional histomorphometric method. VA and VBIC increased significantly with as the healing period increased (pimplants using micro-CT analysis using a region-based segmentation method.

The avian-origin PB1 gene segment facilitated replication and transmissibility of the H3N2/1968 pandemic influenza virus.

Science.gov (United States)

Wendel, Isabel; Rubbenstroth, Dennis; Doedt, Jennifer; Kochs, Georg; Wilhelm, Jochen; Staeheli, Peter; Klenk, Hans-Dieter; Matrosovich, Mikhail

2015-04-01

The H2N2/1957 and H3N2/1968 pandemic influenza viruses emerged via the exchange of genomic RNA segments between human and avian viruses. The avian hemagglutinin (HA) allowed the hybrid viruses to escape preexisting immunity in the human population. Both pandemic viruses further received the PB1 gene segment from the avian parent (Y. Kawaoka, S. Krauss, and R. G. Webster, J Virol 63:4603-4608, 1989), but the biological significance of this observation was not understood. To assess whether the avian-origin PB1 segment provided pandemic viruses with some selective advantage, either on its own or via cooperation with the homologous HA segment, we modeled by reverse genetics the reassortment event that led to the emergence of the H3N2/1968 pandemic virus. Using seasonal H2N2 virus A/California/1/66 (Cal) as a surrogate precursor human virus and pandemic virus A/Hong Kong/1/68 (H3N2) (HK) as a source of avian-derived PB1 and HA gene segments, we generated four reassortant recombinant viruses and compared pairs of viruses which differed solely by the origin of PB1. Replacement of the PB1 segment of Cal by PB1 of HK facilitated viral polymerase activity, replication efficiency in human cells, and contact transmission in guinea pigs. A combination of PB1 and HA segments of HK did not enhance replicative fitness of the reassortant virus compared with the single-gene PB1 reassortant. Our data suggest that the avian PB1 segment of the 1968 pandemic virus served to enhance viral growth and transmissibility, likely by enhancing activity of the viral polymerase complex. Despite the high impact of influenza pandemics on human health, some mechanisms underlying the emergence of pandemic influenza viruses still are poorly understood. Thus, it was unclear why both H2N2/1957 and H3N2/1968 reassortant pandemic viruses contained, in addition to the avian HA, the PB1 gene segment of the avian parent. Here, we addressed this long-standing question by modeling the emergence of the H3N2

Chronic thromboembolic pulmonary hypertension: diagnostic impact of multislice-CT and selective pulmonary-DSA

International Nuclear Information System (INIS)

Pitton, M.B.; Kemmerich, G.; Herber, S.; Schweden, F.; Thelen, M.; Mayer, E.

2002-01-01

Purpose: To evaluate the diagnostic impact of multislice-CT and selective pulmonary DSA in chronic thromboembolic pulmonary hypertension (CTEPH). Methods: 994 vessel segments of 14 consecutive patients with CTEPH were investigated with multislice-CT (slice thickness 3 mm, collimation 2.5 mm, reconstruction intervall 2 mm) and selective pulmonary DSA posterior-anterior, 45 oblique, and lateral projection. Analysis was performed by 2 investigators independently for CT and DSA. Diagnostic criteria were occlusions and non-occlusive changes like webs and bands, irregularities of the vessel wall, diameter reduction and thromboembolic depositions at different levels from central pulmonary arteries to subsegmental arteries. Reference diagnosis was made by synopsis of CT and DSA by consensus. Results: Concerning patency CT and DSA showed concordant findings overall in 88.9%, 92.9% for segmental arteries and 85.4% for subsegmental arteries. Concerning any thromboembolic changes, multislice-CT was significantly inferior to selective DSA (concordance 67.0% overall, 70.4% for segments and 63.6% for subsegments). Non-occlusive changes of the vessels were significantly underdiagnosed by CT (concordance of CT versus DSA: 23.1%). Conclusion: Multislice-CT and selective pulmonary DSA are equivalent for diagnosis of vessel occlusions at the level of segmental and subsegmental arteries. However, for visualisation of the non-occlusive thromboembolic changes of the vessel wall selective pulmonary DSA is still superior compared to multislice-CT. Multislice-CT and selective pulmonary DSA are complementary tools for diagnosis and treatment planning of chronic thromboembolic pulmonary hypertension (CTEPH). (orig.) [de

A multimodality segmentation framework for automatic target delineation in head and neck radiotherapy

Energy Technology Data Exchange (ETDEWEB)

Yang, Jinzhong; Aristophanous, Michalis, E-mail: MAristophanous@mdanderson.org [Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030 (United States); Beadle, Beth M.; Garden, Adam S. [Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030 (United States); Schwartz, David L. [Department of Radiation Oncology, The University of Texas Southwestern Medical Center, Dallas, Texas 75390 (United States)

2015-09-15

Purpose: To develop an automatic segmentation algorithm integrating imaging information from computed tomography (CT), positron emission tomography (PET), and magnetic resonance imaging (MRI) to delineate target volume in head and neck cancer radiotherapy. Methods: Eleven patients with unresectable disease at the tonsil or base of tongue who underwent MRI, CT, and PET/CT within two months before the start of radiotherapy or chemoradiotherapy were recruited for the study. For each patient, PET/CT and T1-weighted contrast MRI scans were first registered to the planning CT using deformable and rigid registration, respectively, to resample the PET and magnetic resonance (MR) images to the planning CT space. A binary mask was manually defined to identify the tumor area. The resampled PET and MR images, the planning CT image, and the binary mask were fed into the automatic segmentation algorithm for target delineation. The algorithm was based on a multichannel Gaussian mixture model and solved using an expectation–maximization algorithm with Markov random fields. To evaluate the algorithm, we compared the multichannel autosegmentation with an autosegmentation method using only PET images. The physician-defined gross tumor volume (GTV) was used as the “ground truth” for quantitative evaluation. Results: The median multichannel segmented GTV of the primary tumor was 15.7 cm{sup 3} (range, 6.6–44.3 cm{sup 3}), while the PET segmented GTV was 10.2 cm{sup 3} (range, 2.8–45.1 cm{sup 3}). The median physician-defined GTV was 22.1 cm{sup 3} (range, 4.2–38.4 cm{sup 3}). The median difference between the multichannel segmented and physician-defined GTVs was −10.7%, not showing a statistically significant difference (p-value = 0.43). However, the median difference between the PET segmented and physician-defined GTVs was −19.2%, showing a statistically significant difference (p-value =0.0037). The median Dice similarity coefficient between the multichannel segmented

A multimodality segmentation framework for automatic target delineation in head and neck radiotherapy.

Science.gov (United States)

Yang, Jinzhong; Beadle, Beth M; Garden, Adam S; Schwartz, David L; Aristophanous, Michalis

2015-09-01

To develop an automatic segmentation algorithm integrating imaging information from computed tomography (CT), positron emission tomography (PET), and magnetic resonance imaging (MRI) to delineate target volume in head and neck cancer radiotherapy. Eleven patients with unresectable disease at the tonsil or base of tongue who underwent MRI, CT, and PET/CT within two months before the start of radiotherapy or chemoradiotherapy were recruited for the study. For each patient, PET/CT and T1-weighted contrast MRI scans were first registered to the planning CT using deformable and rigid registration, respectively, to resample the PET and magnetic resonance (MR) images to the planning CT space. A binary mask was manually defined to identify the tumor area. The resampled PET and MR images, the planning CT image, and the binary mask were fed into the automatic segmentation algorithm for target delineation. The algorithm was based on a multichannel Gaussian mixture model and solved using an expectation-maximization algorithm with Markov random fields. To evaluate the algorithm, we compared the multichannel autosegmentation with an autosegmentation method using only PET images. The physician-defined gross tumor volume (GTV) was used as the "ground truth" for quantitative evaluation. The median multichannel segmented GTV of the primary tumor was 15.7 cm(3) (range, 6.6-44.3 cm(3)), while the PET segmented GTV was 10.2 cm(3) (range, 2.8-45.1 cm(3)). The median physician-defined GTV was 22.1 cm(3) (range, 4.2-38.4 cm(3)). The median difference between the multichannel segmented and physician-defined GTVs was -10.7%, not showing a statistically significant difference (p-value = 0.43). However, the median difference between the PET segmented and physician-defined GTVs was -19.2%, showing a statistically significant difference (p-value =0.0037). The median Dice similarity coefficient between the multichannel segmented and physician-defined GTVs was 0.75 (range, 0.55-0.84), and the

A multimodality segmentation framework for automatic target delineation in head and neck radiotherapy

International Nuclear Information System (INIS)

Yang, Jinzhong; Aristophanous, Michalis; Beadle, Beth M.; Garden, Adam S.; Schwartz, David L.

2015-01-01

Purpose: To develop an automatic segmentation algorithm integrating imaging information from computed tomography (CT), positron emission tomography (PET), and magnetic resonance imaging (MRI) to delineate target volume in head and neck cancer radiotherapy. Methods: Eleven patients with unresectable disease at the tonsil or base of tongue who underwent MRI, CT, and PET/CT within two months before the start of radiotherapy or chemoradiotherapy were recruited for the study. For each patient, PET/CT and T1-weighted contrast MRI scans were first registered to the planning CT using deformable and rigid registration, respectively, to resample the PET and magnetic resonance (MR) images to the planning CT space. A binary mask was manually defined to identify the tumor area. The resampled PET and MR images, the planning CT image, and the binary mask were fed into the automatic segmentation algorithm for target delineation. The algorithm was based on a multichannel Gaussian mixture model and solved using an expectation–maximization algorithm with Markov random fields. To evaluate the algorithm, we compared the multichannel autosegmentation with an autosegmentation method using only PET images. The physician-defined gross tumor volume (GTV) was used as the “ground truth” for quantitative evaluation. Results: The median multichannel segmented GTV of the primary tumor was 15.7 cm"3 (range, 6.6–44.3 cm"3), while the PET segmented GTV was 10.2 cm"3 (range, 2.8–45.1 cm"3). The median physician-defined GTV was 22.1 cm"3 (range, 4.2–38.4 cm"3). The median difference between the multichannel segmented and physician-defined GTVs was −10.7%, not showing a statistically significant difference (p-value = 0.43). However, the median difference between the PET segmented and physician-defined GTVs was −19.2%, showing a statistically significant difference (p-value =0.0037). The median Dice similarity coefficient between the multichannel segmented and physician-defined GTVs was

Discriminative Localization in CNNs for Weakly-Supervised Segmentation of Pulmonary Nodules.

Science.gov (United States)

Feng, Xinyang; Yang, Jie; Laine, Andrew F; Angelini, Elsa D

2017-09-01

Automated detection and segmentation of pulmonary nodules on lung computed tomography (CT) scans can facilitate early lung cancer diagnosis. Existing supervised approaches for automated nodule segmentation on CT scans require voxel-based annotations for training, which are labor- and time-consuming to obtain. In this work, we propose a weakly-supervised method that generates accurate voxel-level nodule segmentation trained with image-level labels only. By adapting a convolutional neural network (CNN) trained for image classification, our proposed method learns discriminative regions from the activation maps of convolution units at different scales, and identifies the true nodule location with a novel candidate-screening framework. Experimental results on the public LIDC-IDRI dataset demonstrate that, our weakly-supervised nodule segmentation framework achieves competitive performance compared to a fully-supervised CNN-based segmentation method.

Evaluation of the robustness of the preprocessing technique improving reversible compressibility of CT images: Tested on various CT examinations

Energy Technology Data Exchange (ETDEWEB)

Jeon, Chang Ho; Kim, Bohyoung; Gu, Bon Seung; Lee, Jong Min [Department of Radiology, Seoul National University Bundang Hospital, Seoul National University College of Medicine, 300 Gumi-ro, Bundang-gu, Seongnam-si, Gyeonggi-do 463-707 (Korea, Republic of); Kim, Kil Joong [Department of Radiology, Seoul National University Bundang Hospital, Seoul National University College of Medicine, 300 Gumi-ro, Bundang-gu, Seongnam-si, Gyeonggi-do 463-707, South Korea and Department of Radiation Applied Life Science, Seoul National University College of Medicine, 103 Daehak-ro, Jongno-gu, Seoul 110-799 (Korea, Republic of); Lee, Kyoung Ho [Department of Radiology, Seoul National University Bundang Hospital, Seoul National University College of Medicine, 300 Gumi-ro, Bundang-gu, Seongnam-si, Gyeonggi-do 463-707, South Korea and Institute of Radiation Medicine, Seoul National University Medical Research Center, and Clinical Research Institute, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul 110-744 (Korea, Republic of); Kim, Tae Ki [Medical Information Center, Seoul National University Bundang Hospital, Seoul National University College of Medicine, 300 Gumi-ro, Bundang-gu, Seongnam-si, Gyeonggi-do 463-707 (Korea, Republic of)

2013-10-15

Purpose: To modify the preprocessing technique, which was previously proposed, improving compressibility of computed tomography (CT) images to cover the diversity of three dimensional configurations of different body parts and to evaluate the robustness of the technique in terms of segmentation correctness and increase in reversible compression ratio (CR) for various CT examinations.Methods: This study had institutional review board approval with waiver of informed patient consent. A preprocessing technique was previously proposed to improve the compressibility of CT images by replacing pixel values outside the body region with a constant value resulting in maximizing data redundancy. Since the technique was developed aiming at only chest CT images, the authors modified the segmentation method to cover the diversity of three dimensional configurations of different body parts. The modified version was evaluated as follows. In randomly selected 368 CT examinations (352 787 images), each image was preprocessed by using the modified preprocessing technique. Radiologists visually confirmed whether the segmented region covers the body region or not. The images with and without the preprocessing were reversibly compressed using Joint Photographic Experts Group (JPEG), JPEG2000 two-dimensional (2D), and JPEG2000 three-dimensional (3D) compressions. The percentage increase in CR per examination (CR{sub I}) was measured.Results: The rate of correct segmentation was 100.0% (95% CI: 99.9%, 100.0%) for all the examinations. The median of CR{sub I} were 26.1% (95% CI: 24.9%, 27.1%), 40.2% (38.5%, 41.1%), and 34.5% (32.7%, 36.2%) in JPEG, JPEG2000 2D, and JPEG2000 3D, respectively.Conclusions: In various CT examinations, the modified preprocessing technique can increase in the CR by 25% or more without concerning about degradation of diagnostic information.

Effect of image scaling and segmentation in digital rock characterisation

Science.gov (United States)

Jones, B. D.; Feng, Y. T.

2016-04-01

Digital material characterisation from microstructural geometry is an emerging field in computer simulation. For permeability characterisation, a variety of studies exist where the lattice Boltzmann method (LBM) has been used in conjunction with computed tomography (CT) imaging to simulate fluid flow through microscopic rock pores. While these previous works show that the technique is applicable, the use of binary image segmentation and the bounceback boundary condition results in a loss of grain surface definition when the modelled geometry is compared to the original CT image. We apply the immersed moving boundary (IMB) condition of Noble and Torczynski as a partial bounceback boundary condition which may be used to better represent the geometric definition provided by a CT image. The IMB condition is validated against published work on idealised porous geometries in both 2D and 3D. Following this, greyscale image segmentation is applied to a CT image of Diemelstadt sandstone. By varying the mapping of CT voxel densities to lattice sites, it is shown that binary image segmentation may underestimate the true permeability of the sample. A CUDA-C-based code, LBM-C, was developed specifically for this work and leverages GPU hardware in order to carry out computations.

MRI Brain Tumor Segmentation Methods- A Review

OpenAIRE

Gursangeet, Kaur; Jyoti, Rani

2016-01-01

Medical image processing and its segmentation is an active and interesting area for researchers. It has reached at the tremendous place in diagnosing tumors after the discovery of CT and MRI. MRI is an useful tool to detect the brain tumor and segmentation is performed to carry out the useful portion from an image. The purpose of this paper is to provide an overview of different image segmentation methods like watershed algorithm, morphological operations, neutrosophic sets, thresholding, K-...

Segmentation of urinary bladder in CT Urography (CTU) using CLASS

Science.gov (United States)

Hadjiiski, Lubomir; Chan, Heang-Ping; Law, Yuen; Cohan, Richard H.; Caoili, Elaine M.; Cho, Hyun-Chong; Zhou, Chuan; Wei, Jun

2012-03-01

We are developing a computerized system for bladder segmentation on CTU, as a critical component for computer aided diagnosis of bladder cancer. A challenge for bladder segmentation is the presence of regions without contrast (NC) and filled with IV contrast (C). We are developing a Conjoint Level set Analysis and Segmentation System (CLASS) specifically for this application. CLASS performs a series of image processing tasks: preprocessing, initial segmentation, and 3D and 2D level set segmentation and post-processing, designed according to the characteristics of the bladder in CTU. The NC and the C regions of the bladder were segmented separately in CLASS. The final contour is obtained in the post-processing stage by the union of the NC and C contours. Seventy bladders (31 containing lesions, 24 containing wall thickening, and 15 normal) were segmented. The performance of CLASS was assessed by rating the quality of the contours on a 5-point scale (1= "very poor", 3= "fair", 5 = "excellent"). For the 53 partially contrast-filled bladders, the average quality ratings for the 53 NC and 53 C regions were 4.0+/-0.7 and 4.0+/-1.0, respectively. 46 NC and 41 C regions were given quality ratings of 4 or above. Only 2 NC and 5 C regions had ratings under 3. The average quality ratings for the remaining 12 completely no contrast (NC) and 5 completely contrast-filled (C) bladder contours were 3.3+/-1.0 and 3.4+/-0.5, respectively. After combining the NC and C contours for each of the 70 bladders, 46 had quality ratings of 4 or above. Only 4 had ratings under 3. The average quality rating was 3.8+/-0.7. The results demonstrate the potential of CLASS for automated segmentation of the bladder.

A Method for Extracting Suspected Parotid Lesions in CT Images using Feature-based Segmentation and Active Contours based on Stationary Wavelet Transform

Science.gov (United States)

Wu, T. Y.; Lin, S. F.

2013-10-01

Automatic suspected lesion extraction is an important application in computer-aided diagnosis (CAD). In this paper, we propose a method to automatically extract the suspected parotid regions for clinical evaluation in head and neck CT images. The suspected lesion tissues in low contrast tissue regions can be localized with feature-based segmentation (FBS) based on local texture features, and can be delineated with accuracy by modified active contour models (ACM). At first, stationary wavelet transform (SWT) is introduced. The derived wavelet coefficients are applied to derive the local features for FBS, and to generate enhanced energy maps for ACM computation. Geometric shape features (GSFs) are proposed to analyze each soft tissue region segmented by FBS; the regions with higher similarity GSFs with the lesions are extracted and the information is also applied as the initial conditions for fine delineation computation. Consequently, the suspected lesions can be automatically localized and accurately delineated for aiding clinical diagnosis. The performance of the proposed method is evaluated by comparing with the results outlined by clinical experts. The experiments on 20 pathological CT data sets show that the true-positive (TP) rate on recognizing parotid lesions is about 94%, and the dimension accuracy of delineation results can also approach over 93%.

Feasibility of Single Scan for Simultaneous Evaluation of Regional Krypton and Iodine Concentrations with Dual-Energy CT: An Experimental Study.

Science.gov (United States)

Hong, Sae Rom; Chang, Suyon; Im, Dong Jin; Suh, Young Joo; Hong, Yoo Jin; Hur, Jin; Kim, Young Jin; Choi, Byoung Wook; Lee, Hye-Jeong

2016-11-01

Purpose To evaluate the feasibility of a simultaneous single scan of regional krypton and iodine concentrations by using dual-energy computed tomography (CT). Materials and Methods The study was approved by the institutional animal experimental committee. An airway obstruction model was first made in 10 beagle dogs, and a pulmonary arterial occlusion was induced in each animal after 1 week. For each model, three sessions of dual-energy CT (80% krypton ventilation [krypton CT], 80% krypton ventilation with iodine enhancement [mixed-contrast agent CT], and iodine enhancement [iodine CT]) were performed. Krypton maps were made from krypton and mixed-contrast agent CT, and iodine maps were made from iodine and mixed-contrast agent CT. Observers measured overlay Hounsfield units of the diseased and contralateral segments on each map. Values were compared by using the Wilcoxon signed-rank test. Results In krypton maps of airway obstruction, overlay Hounsfield units of diseased segments were significantly decreased compared with those of contralateral segments in both krypton and mixed-contrast agent CT (P = .005 for both). However, the values of mixed-contrast agent CT were significantly higher than those of krypton CT for both segments (P = .005 and .007, respectively). In iodine maps of pulmonary arterial occlusion, values were significantly lower in diseased segments than in contralateral segments for both iodine and mixed-contrast agent CT (P = .005 for both), without significant difference between iodine and mixed-contrast agent CT for both segments (P = .126 and .307, respectively). Conclusion Although some limitations may exist, it might be feasible to analyze regional krypton and iodine concentrations simultaneously by using dual-energy CT. © RSNA, 2016.

Electron absorbed fractions in skeletal soft tissues based on red bone marrow segmentation at runtime in muCT images of human trabecular bone;Fracoes absorvidas de eletrons em tecidos moles do esqueleto avaliadas com base na segmentacao em tempo de execucao da medula ossea vermelha contida em imagens muCT do osso trabecular humano

Energy Technology Data Exchange (ETDEWEB)

Vieira, J.W. [Instituto Federal de Educacao, Ciencia e Tecnologia de Pernambuco, Recife, PE (Brazil); Kramer, R. [Universidade de Pernambuco (UPE), Recife, PE (Brazil). Escola Politecnica; Khoury, H.J. [Universidade Federal de Pernambuco (UFPE), Recife, PE (Brazil). Dept. de Energia Nuclear; Robson-Brown, K. [University of Bristol, Bristol (United Kingdom). Dept. of Archaeology and Anthropology

2009-07-01

Skeletal dosimetry determines equivalent dose or absorbed fractions in the red bone marrow (RBM) and the osteogenic cells on bone surfaces (BSC). Following a method used earlier for the BSC, RBM and yellow bone marrow (YBM) have been segmented in the marrow cavities of muCT images of human spongiosa at runtime, i.e. during the execution of the Monte Carlo calculation, which avoids the necessity to segment RBM and YBM externally in muCT images for many different cellularities and to store the data. Using this internal RBM/YBM segmentation, this study presents electron absorbed fractions for the RBM and the BSC as a function of the voxel resolution and also compares the results with data from other investigations. (author)

Computer-aided diagnosis of pulmonary nodules on CT scans: Segmentation and classification using 3D active contours

International Nuclear Information System (INIS)

Way, Ted W.; Hadjiiski, Lubomir M.; Sahiner, Berkman; Chan, H.-P.; Cascade, Philip N.; Kazerooni, Ella A.; Bogot, Naama; Zhou Chuan

2006-01-01

We are developing a computer-aided diagnosis (CAD) system to classify malignant and benign lung nodules found on CT scans. A fully automated system was designed to segment the nodule from its surrounding structured background in a local volume of interest (VOI) and to extract image features for classification. Image segmentation was performed with a three-dimensional (3D) active contour (AC) method. A data set of 96 lung nodules (44 malignant, 52 benign) from 58 patients was used in this study. The 3D AC model is based on two-dimensional AC with the addition of three new energy components to take advantage of 3D information: (1) 3D gradient, which guides the active contour to seek the object surface (2) 3D curvature, which imposes a smoothness constraint in the z direction, and (3) mask energy, which penalizes contours that grow beyond the pleura or thoracic wall. The search for the best energy weights in the 3D AC model was guided by a simplex optimization method. Morphological and gray-level features were extracted from the segmented nodule. The rubber band straightening transform (RBST) was applied to the shell of voxels surrounding the nodule. Texture features based on run-length statistics were extracted from the RBST image. A linear discriminant analysis classifier with stepwise feature selection was designed using a second simplex optimization to select the most effective features. Leave-one-case-out resampling was used to train and test the CAD system. The system achieved a test area under the receiver operating characteristic curve (A z ) of 0.83±0.04. Our preliminary results indicate that use of the 3D AC model and the 3D texture features surrounding the nodule is a promising approach to the segmentation and classification of lung nodules with CAD. The segmentation performance of the 3D AC model trained with our data set was evaluated with 23 nodules available in the Lung Image Database Consortium (LIDC). The lung nodule volumes segmented by the 3D AC

Quantitative hepatic CT perfusion measurement: Comparison of Couinaud's hepatic segments with dual-source 128-slice CT

Energy Technology Data Exchange (ETDEWEB)

Wang, Xuan [The Department of Radiology, Peking Union Medical College Hospital, Dongcheng District, Beijing, 100730 (China); Xue, Hua-dan, E-mail: bjdanna95@hotmail.com [The Department of Radiology, Peking Union Medical College Hospital, Dongcheng District, Beijing, 100730 (China); Jin, Zheng-yu, E-mail: jin_zhengyu@163.com [The Department of Radiology, Peking Union Medical College Hospital, Dongcheng District, Beijing, 100730 (China); Su, Bai-yan; Li, Zhuo; Sun, Hao; Chen, Yu; Liu, Wei [The Department of Radiology, Peking Union Medical College Hospital, Dongcheng District, Beijing, 100730 (China)

2013-02-15

Purpose: To compare the quantitative liver computed tomography perfusion (CTP) differences among eight hepatic segments. Materials and methods: This retrospective study was based on 72 acquired upper abdomen CTP scans for detecting suspected pancreas tumor. Patients with primary or metastatic liver tumor, any focal liver lesions except simple cyst (<3 cm in diameter), history of liver operation or splenectomy, evidence of liver cirrhosis or invasion of portal vein were excluded. The final analysis included 50 patients (M:F = 21:29, mean age = 43.2 years, 15–76 years). Arterial liver perfusion (ALP), portal-venous perfusion (PVP), total hepatic perfusion (THP = ALP + PVP), and hepatic perfusion index (HPI) of each hepatic segment were calculated and compared by means of one-way analysis of variance (ANOVA) and the Bonferonni correction method. Results: Compared to hepatic segments 5, 6, 7 and 8, segments 2 and 3 showed a tendency of higher ALPs, lower PVPs, and higher HPIs, most of which were statistically significant (p < 0.05). Hepatic segments 1 and 4 had higher mean values of ALP and HPI and lower mean values of PVP than segments 5, 6, 7 and 8 as well, although no significant differences were detected except for ALP and HPI for liver segments 1 and 7 (p = 0.001 and 0.035 respectively), and ALP for liver segments 1 and 5 (p = 0.039). Higher ALP and HPI were showed in hepatic segment 3 compared to segment 4 (p = 0.000 and 0.000 respectively). No significant differences were found for THP among eight segments. Conclusions: Intra-hepatic perfusion differences exist in normal hepatic parenchyma especially between lateral sector (segments 2 and 3) and right lobe (segments 5, 6, 7 and 8). This might have potential clinical significance in liver-perfusion-related protocol design and result analysis.

A Semiautomatic Segmentation Algorithm for Extracting the Complete Structure of Acini from Synchrotron Micro-CT Images

Directory of Open Access Journals (Sweden)

Luosha Xiao

2013-01-01

Full Text Available Pulmonary acinus is the largest airway unit provided with alveoli where blood/gas exchange takes place. Understanding the complete structure of acinus is necessary to measure the pathway of gas exchange and to simulate various mechanical phenomena in the lungs. The usual manual segmentation of a complete acinus structure from their experimentally obtained images is difficult and extremely time-consuming, which hampers the statistical analysis. In this study, we develop a semiautomatic segmentation algorithm for extracting the complete structure of acinus from synchrotron micro-CT images of the closed chest of mouse lungs. The algorithm uses a combination of conventional binary image processing techniques based on the multiscale and hierarchical nature of lung structures. Specifically, larger structures are removed, while smaller structures are isolated from the image by repeatedly applying erosion and dilation operators in order, adjusting the parameter referencing to previously obtained morphometric data. A cluster of isolated acini belonging to the same terminal bronchiole is obtained without floating voxels. The extracted acinar models above 98% agree well with those extracted manually. The run time is drastically shortened compared with manual methods. These findings suggest that our method may be useful for taking samples used in the statistical analysis of acinus.

An improved method for pancreas segmentation using SLIC and interactive region merging

Science.gov (United States)

Zhang, Liyuan; Yang, Huamin; Shi, Weili; Miao, Yu; Li, Qingliang; He, Fei; He, Wei; Li, Yanfang; Zhang, Huimao; Mori, Kensaku; Jiang, Zhengang

2017-03-01

Considering the weak edges in pancreas segmentation, this paper proposes a new solution which integrates more features of CT images by combining SLIC superpixels and interactive region merging. In the proposed method, Mahalanobis distance is first utilized in SLIC method to generate better superpixel images. By extracting five texture features and one gray feature, the similarity measure between two superpixels becomes more reliable in interactive region merging. Furthermore, object edge blocks are accurately addressed by re-segmentation merging process. Applying the proposed method to four cases of abdominal CT images, we segment pancreatic tissues to verify the feasibility and effectiveness. The experimental results show that the proposed method can make segmentation accuracy increase to 92% on average. This study will boost the application process of pancreas segmentation for computer-aided diagnosis system.

Segmentation of nodules on chest computed tomography for growth assessment

International Nuclear Information System (INIS)

Mullally, William; Betke, Margrit; Wang Jingbin; Ko, Jane P.

2004-01-01

Several segmentation methods to evaluate growth of small isolated pulmonary nodules on chest computed tomography (CT) are presented. The segmentation methods are based on adaptively thresholding attenuation levels and use measures of nodule shape. The segmentation methods were first tested on a realistic chest phantom to evaluate their performance with respect to specific nodule characteristics. The segmentation methods were also tested on sequential CT scans of patients. The methods' estimation of nodule growth were compared to the volume change calculated by a chest radiologist. The best method segmented nodules on average 43% smaller or larger than the actual nodule when errors were computed across all nodule variations on the phantom. Some methods achieved smaller errors when examined with respect to certain nodule properties. In particular, on the phantom individual methods segmented solid nodules to within 23% of their actual size and nodules with 60.7 mm3 volumes to within 14%. On the clinical data, none of the methods examined showed a statistically significant difference in growth estimation from the radiologist

Morphometric Atlas Selection for Automatic Brachial Plexus Segmentation

International Nuclear Information System (INIS)

Van de Velde, Joris; Wouters, Johan; Vercauteren, Tom; De Gersem, Werner; Duprez, Fréderic; De Neve, Wilfried; Van Hoof, Tom

2015-01-01

Purpose: The purpose of this study was to determine the effects of atlas selection based on different morphometric parameters, on the accuracy of automatic brachial plexus (BP) segmentation for radiation therapy planning. The segmentation accuracy was measured by comparing all of the generated automatic segmentations with anatomically validated gold standard atlases developed using cadavers. Methods and Materials: Twelve cadaver computed tomography (CT) atlases (3 males, 9 females; mean age: 73 years) were included in the study. One atlas was selected to serve as a patient, and the other 11 atlases were registered separately onto this “patient” using deformable image registration. This procedure was repeated for every atlas as a patient. Next, the Dice and Jaccard similarity indices and inclusion index were calculated for every registered BP with the original gold standard BP. In parallel, differences in several morphometric parameters that may influence the BP segmentation accuracy were measured for the different atlases. Specific brachial plexus-related CT-visible bony points were used to define the morphometric parameters. Subsequently, correlations between the similarity indices and morphometric parameters were calculated. Results: A clear negative correlation between difference in protraction-retraction distance and the similarity indices was observed (mean Pearson correlation coefficient = −0.546). All of the other investigated Pearson correlation coefficients were weak. Conclusions: Differences in the shoulder protraction-retraction position between the atlas and the patient during planning CT influence the BP autosegmentation accuracy. A greater difference in the protraction-retraction distance between the atlas and the patient reduces the accuracy of the BP automatic segmentation result

Volumetric quantification of bone-implant contact using micro-computed tomography analysis based on region-based segmentation

Energy Technology Data Exchange (ETDEWEB)

Kang, Sung Won; Lee, Woo Jin; Choi, Soon Chul; Lee, Sam Sun; Heo, Min Suk; Huh, Kyung Hoe; Kim, Tae Il; Yi, Won Ji [Dental Research Institute, School of Dentistry, Seoul National University, Seoul (Korea, Republic of)

2015-03-15

We have developed a new method of segmenting the areas of absorbable implants and bone using region-based segmentation of micro-computed tomography (micro-CT) images, which allowed us to quantify volumetric bone-implant contact (VBIC) and volumetric absorption (VA). The simple threshold technique generally used in micro-CT analysis cannot be used to segment the areas of absorbable implants and bone. Instead, a region-based segmentation method, a region-labeling method, and subsequent morphological operations were successively applied to micro-CT images. The three-dimensional VBIC and VA of the absorbable implant were then calculated over the entire volume of the implant. Two-dimensional (2D) bone-implant contact (BIC) and bone area (BA) were also measured based on the conventional histomorphometric method. VA and VBIC increased significantly with as the healing period increased (p<0.05). VBIC values were significantly correlated with VA values (p<0.05) and with 2D BIC values (p<0.05). It is possible to quantify VBIC and VA for absorbable implants using micro-CT analysis using a region-based segmentation method.

Volumetric quantification of bone-implant contact using micro-computed tomography analysis based on region-based segmentation

International Nuclear Information System (INIS)

Kang, Sung Won; Lee, Woo Jin; Choi, Soon Chul; Lee, Sam Sun; Heo, Min Suk; Huh, Kyung Hoe; Kim, Tae Il; Yi, Won Ji

2015-01-01

We have developed a new method of segmenting the areas of absorbable implants and bone using region-based segmentation of micro-computed tomography (micro-CT) images, which allowed us to quantify volumetric bone-implant contact (VBIC) and volumetric absorption (VA). The simple threshold technique generally used in micro-CT analysis cannot be used to segment the areas of absorbable implants and bone. Instead, a region-based segmentation method, a region-labeling method, and subsequent morphological operations were successively applied to micro-CT images. The three-dimensional VBIC and VA of the absorbable implant were then calculated over the entire volume of the implant. Two-dimensional (2D) bone-implant contact (BIC) and bone area (BA) were also measured based on the conventional histomorphometric method. VA and VBIC increased significantly with as the healing period increased (p<0.05). VBIC values were significantly correlated with VA values (p<0.05) and with 2D BIC values (p<0.05). It is possible to quantify VBIC and VA for absorbable implants using micro-CT analysis using a region-based segmentation method.

Improving vertebra segmentation through joint vertebra-rib atlases

Science.gov (United States)

Wang, Yinong; Yao, Jianhua; Roth, Holger R.; Burns, Joseph E.; Summers, Ronald M.

2016-03-01

Accurate spine segmentation allows for improved identification and quantitative characterization of abnormalities of the vertebra, such as vertebral fractures. However, in existing automated vertebra segmentation methods on computed tomography (CT) images, leakage into nearby bones such as ribs occurs due to the close proximity of these visibly intense structures in a 3D CT volume. To reduce this error, we propose the use of joint vertebra-rib atlases to improve the segmentation of vertebrae via multi-atlas joint label fusion. Segmentation was performed and evaluated on CTs containing 106 thoracic and lumbar vertebrae from 10 pathological and traumatic spine patients on an individual vertebra level basis. Vertebra atlases produced errors where the segmentation leaked into the ribs. The use of joint vertebra-rib atlases produced a statistically significant increase in the Dice coefficient from 92.5 +/- 3.1% to 93.8 +/- 2.1% for the left and right transverse processes and a decrease in the mean and max surface distance from 0.75 +/- 0.60mm and 8.63 +/- 4.44mm to 0.30 +/- 0.27mm and 3.65 +/- 2.87mm, respectively.

Segmentation of Synchrotron Radiation micro-Computed Tomography Images using Energy Minimization via Graph Cuts

International Nuclear Information System (INIS)

Meneses, Anderson A.M.; Giusti, Alessandro; Almeida, André P. de; Nogueira, Liebert; Braz, Delson; Almeida, Carlos E. de; Barroso, Regina C.

2012-01-01

The research on applications of segmentation algorithms to Synchrotron Radiation X-Ray micro-Computed Tomography (SR-μCT) is an open problem, due to the interesting and well-known characteristics of SR images, such as the phase contrast effect. The Energy Minimization via Graph Cuts (EMvGC) algorithm represents state-of-art segmentation algorithm, presenting an enormous potential of application in SR-μCT imaging. We describe the application of the algorithm EMvGC with swap move for the segmentation of bone images acquired at the ELETTRA Laboratory (Trieste, Italy). - Highlights: ► Microstructures of Wistar rats' ribs are investigated with Synchrotron Radiation μCT imaging. ► The present work is part of a research on the effects of radiotherapy on the thoracic region. ► Application of the Energy Minimization via Graph Cuts algorithm for segmentation is described.

Characteristics of images of angiographically proven normal coronary arteries acquired by adenosine-stress thallium-201 myocardial perfusion SPECT/CT-IQ[Symbol: see text]SPECT with CT attenuation correction changed stepwise.

Science.gov (United States)

Takahashi, Teruyuki; Tanaka, Haruki; Kozono, Nami; Tanakamaru, Yoshiki; Idei, Naomi; Ohashi, Norihiko; Ohtsubo, Hideki; Okada, Takenori; Yasunobu, Yuji; Kaseda, Shunichi

2015-04-01

Although several studies have shown the diagnostic and prognostic value of CT-based attenuation correction (AC) of single photon emission computed tomography (SPECT) images for diagnosing coronary artery disease (CAD), this issue remains a matter of debate. To clarify the characteristics of CT-AC SPECT images that might potentially improve diagnostic performance, we analyzed images acquired using adenosine-stress thallium-201 myocardial perfusion SPECT/CT equipped with IQ[Symbol: see text]SPECT (SPECT/CT-IQ[Symbol: see text]SPECT) from patients with angiographically proven normal coronary arteries after changing the CT attenuation correction (CT-AC) in a stepwise manner. We enrolled 72 patients (Male 36, Female 36) with normal coronary arteries according to findings of invasive coronary angiography or CT-angiography within three months after a SPECT/CT study. Projection images were reconstructed at CT-AC values of (-), 40, 60, 80 and 100 % using a CT number conversion program according to our definition and analyzed using polar maps according to sex. CT attenuation corrected segments were located from the mid- and apical-inferior spread through the mid- and apical-septal regions and finally to the basal-anterior and basal- and mid-lateral regions in males, and from the mid-inferior region through the mid-septal and mid-anterior, and mid-lateral regions in females as the CT-AC values increased. Segments with maximal mean counts shifted from the apical-anterior to mid-anterolateral region under both stress and rest conditions in males, whereas such segments shifted from the apical-septal to the mid-anteroseptal region under both stress and rest conditions in females. We clarified which part of the myocardium and to which degree CT-AC affects it in adenosine-stress thallium-201 myocardial perfusion SPECT/CT-IQ[Symbol: see text]SPECT images by changing the CT-AC value stepwise. We also identified sex-specific shifts of segments with maximal mean counts that changed as

An Accurate liver segmentation method using parallel computing algorithm

International Nuclear Information System (INIS)

Elbasher, Eiman Mohammed Khalied

2014-12-01

Computed Tomography (CT or CAT scan) is a noninvasive diagnostic imaging procedure that uses a combination of X-rays and computer technology to produce horizontal, or axial, images (often called slices) of the body. A CT scan shows detailed images of any part of the body, including the bones muscles, fat and organs CT scans are more detailed than standard x-rays. CT scans may be done with or without "contrast Contrast refers to a substance taken by mouth and/ or injected into an intravenous (IV) line that causes the particular organ or tissue under study to be seen more clearly. CT scan of the liver and biliary tract are used in the diagnosis of many diseases in the abdomen structures, particularly when another type of examination, such as X-rays, physical examination, and ultra sound is not conclusive. Unfortunately, the presence of noise and artifact in the edges and fine details in the CT images limit the contrast resolution and make diagnostic procedure more difficult. This experimental study was conducted at the College of Medical Radiological Science, Sudan University of Science and Technology and Fidel Specialist Hospital. The sample of study was included 50 patients. The main objective of this research was to study an accurate liver segmentation method using a parallel computing algorithm, and to segment liver and adjacent organs using image processing technique. The main technique of segmentation used in this study was watershed transform. The scope of image processing and analysis applied to medical application is to improve the quality of the acquired image and extract quantitative information from medical image data in an efficient and accurate way. The results of this technique agreed wit the results of Jarritt et al, (2010), Kratchwil et al, (2010), Jover et al, (2011), Yomamoto et al, (1996), Cai et al (1999), Saudha and Jayashree (2010) who used different segmentation filtering based on the methods of enhancing the computed tomography images. Anther

Automatic abdominal multi-organ segmentation using deep convolutional neural network and time-implicit level sets.

Science.gov (United States)

Hu, Peijun; Wu, Fa; Peng, Jialin; Bao, Yuanyuan; Chen, Feng; Kong, Dexing

2017-03-01

Multi-organ segmentation from CT images is an essential step for computer-aided diagnosis and surgery planning. However, manual delineation of the organs by radiologists is tedious, time-consuming and poorly reproducible. Therefore, we propose a fully automatic method for the segmentation of multiple organs from three-dimensional abdominal CT images. The proposed method employs deep fully convolutional neural networks (CNNs) for organ detection and segmentation, which is further refined by a time-implicit multi-phase evolution method. Firstly, a 3D CNN is trained to automatically localize and delineate the organs of interest with a probability prediction map. The learned probability map provides both subject-specific spatial priors and initialization for subsequent fine segmentation. Then, for the refinement of the multi-organ segmentation, image intensity models, probability priors as well as a disjoint region constraint are incorporated into an unified energy functional. Finally, a novel time-implicit multi-phase level-set algorithm is utilized to efficiently optimize the proposed energy functional model. Our method has been evaluated on 140 abdominal CT scans for the segmentation of four organs (liver, spleen and both kidneys). With respect to the ground truth, average Dice overlap ratios for the liver, spleen and both kidneys are 96.0, 94.2 and 95.4%, respectively, and average symmetric surface distance is less than 1.3 mm for all the segmented organs. The computation time for a CT volume is 125 s in average. The achieved accuracy compares well to state-of-the-art methods with much higher efficiency. A fully automatic method for multi-organ segmentation from abdominal CT images was developed and evaluated. The results demonstrated its potential in clinical usage with high effectiveness, robustness and efficiency.

Thoracic cavity definition for 3D PET/CT analysis and visualization.

Science.gov (United States)

Cheirsilp, Ronnarit; Bascom, Rebecca; Allen, Thomas W; Higgins, William E

2015-07-01

X-ray computed tomography (CT) and positron emission tomography (PET) serve as the standard imaging modalities for lung-cancer management. CT gives anatomical details on diagnostic regions of interest (ROIs), while PET gives highly specific functional information. During the lung-cancer management process, a patient receives a co-registered whole-body PET/CT scan pair and a dedicated high-resolution chest CT scan. With these data, multimodal PET/CT ROI information can be gleaned to facilitate disease management. Effective image segmentation of the thoracic cavity, however, is needed to focus attention on the central chest. We present an automatic method for thoracic cavity segmentation from 3D CT scans. We then demonstrate how the method facilitates 3D ROI localization and visualization in patient multimodal imaging studies. Our segmentation method draws upon digital topological and morphological operations, active-contour analysis, and key organ landmarks. Using a large patient database, the method showed high agreement to ground-truth regions, with a mean coverage=99.2% and leakage=0.52%. Furthermore, it enabled extremely fast computation. For PET/CT lesion analysis, the segmentation method reduced ROI search space by 97.7% for a whole-body scan, or nearly 3 times greater than that achieved by a lung mask. Despite this reduction, we achieved 100% true-positive ROI detection, while also reducing the false-positive (FP) detection rate by >5 times over that achieved with a lung mask. Finally, the method greatly improved PET/CT visualization by eliminating false PET-avid obscurations arising from the heart, bones, and liver. In particular, PET MIP views and fused PET/CT renderings depicted unprecedented clarity of the lesions and neighboring anatomical structures truly relevant to lung-cancer assessment. Copyright © 2015 Elsevier Ltd. All rights reserved.

Defining the lung outline from a gamma camera transmission attenuation map

International Nuclear Information System (INIS)

Fleming, John S; Pitcairn, Gary; Newman, Stephen

2006-01-01

Segmentation of the lung outline from gamma camera transmission images of the thorax is useful in attenuation correction and quantitative image analysis. This paper describes and compares two threshold-based methods of segmentation. Simulated gamma camera transmission images of test objects were used to produce a knowledge base of the variation of threshold defining the lung outline with image resolution and chest wall thickness. Two segmentation techniques based on global (GT) and context-sensitive (CST) thresholds were developed and evaluated in simulated transmission images of realistic thoraces. The segmented lung volumes were compared to the true values used in the simulation. The mean distances between segmented and true lung surface were calculated. The techniques were also applied to three real human subject transmission images. The lung volumes were estimated and the segmentations were compared visually. The CST segmentation produced significantly superior segmentations than the GT technique in the simulated data. In human subjects, the GT technique underestimated volumes by 13% compared to the CST technique. It missed areas that clearly belonged to the lungs. In conclusion, both techniques segmented the lungs with reasonable accuracy and precision. The CST approach was superior, particularly in real human subject images

A proposed framework for consensus-based lung tumour volume auto-segmentation in 4D computed tomography imaging

International Nuclear Information System (INIS)

Martin, Spencer; Rodrigues, George; Gaede, Stewart; Brophy, Mark; Barron, John L; Beauchemin, Steven S; Palma, David; Louie, Alexander V; Yu, Edward; Yaremko, Brian; Ahmad, Belal

2015-01-01

This work aims to propose and validate a framework for tumour volume auto-segmentation based on ground-truth estimates derived from multi-physician input contours to expedite 4D-CT based lung tumour volume delineation. 4D-CT datasets of ten non-small cell lung cancer (NSCLC) patients were manually segmented by 6 physicians. Multi-expert ground truth (GT) estimates were constructed using the STAPLE algorithm for the gross tumour volume (GTV) on all respiratory phases. Next, using a deformable model-based method, multi-expert GT on each individual phase of the 4D-CT dataset was propagated to all other phases providing auto-segmented GTVs and motion encompassing internal gross target volumes (IGTVs) based on GT estimates (STAPLE) from each respiratory phase of the 4D-CT dataset. Accuracy assessment of auto-segmentation employed graph cuts for 3D-shape reconstruction and point-set registration-based analysis yielding volumetric and distance-based measures. STAPLE-based auto-segmented GTV accuracy ranged from (81.51  ±  1.92) to (97.27  ±  0.28)% volumetric overlap of the estimated ground truth. IGTV auto-segmentation showed significantly improved accuracies with reduced variance for all patients ranging from 90.87 to 98.57% volumetric overlap of the ground truth volume. Additional metrics supported these observations with statistical significance. Accuracy of auto-segmentation was shown to be largely independent of selection of the initial propagation phase. IGTV construction based on auto-segmented GTVs within the 4D-CT dataset provided accurate and reliable target volumes compared to manual segmentation-based GT estimates. While inter-/intra-observer effects were largely mitigated, the proposed segmentation workflow is more complex than that of current clinical practice and requires further development. (paper)

A proposed framework for consensus-based lung tumour volume auto-segmentation in 4D computed tomography imaging

Science.gov (United States)

Martin, Spencer; Brophy, Mark; Palma, David; Louie, Alexander V.; Yu, Edward; Yaremko, Brian; Ahmad, Belal; Barron, John L.; Beauchemin, Steven S.; Rodrigues, George; Gaede, Stewart

2015-02-01

This work aims to propose and validate a framework for tumour volume auto-segmentation based on ground-truth estimates derived from multi-physician input contours to expedite 4D-CT based lung tumour volume delineation. 4D-CT datasets of ten non-small cell lung cancer (NSCLC) patients were manually segmented by 6 physicians. Multi-expert ground truth (GT) estimates were constructed using the STAPLE algorithm for the gross tumour volume (GTV) on all respiratory phases. Next, using a deformable model-based method, multi-expert GT on each individual phase of the 4D-CT dataset was propagated to all other phases providing auto-segmented GTVs and motion encompassing internal gross target volumes (IGTVs) based on GT estimates (STAPLE) from each respiratory phase of the 4D-CT dataset. Accuracy assessment of auto-segmentation employed graph cuts for 3D-shape reconstruction and point-set registration-based analysis yielding volumetric and distance-based measures. STAPLE-based auto-segmented GTV accuracy ranged from (81.51  ±  1.92) to (97.27  ±  0.28)% volumetric overlap of the estimated ground truth. IGTV auto-segmentation showed significantly improved accuracies with reduced variance for all patients ranging from 90.87 to 98.57% volumetric overlap of the ground truth volume. Additional metrics supported these observations with statistical significance. Accuracy of auto-segmentation was shown to be largely independent of selection of the initial propagation phase. IGTV construction based on auto-segmented GTVs within the 4D-CT dataset provided accurate and reliable target volumes compared to manual segmentation-based GT estimates. While inter-/intra-observer effects were largely mitigated, the proposed segmentation workflow is more complex than that of current clinical practice and requires further development.

Cochlea Segmentation using Iterated Random Walks with Shape Prior

DEFF Research Database (Denmark)

Ruiz Pujadas, Esmeralda; Kjer, Hans Martin; Vera, Sergio

2016-01-01

Cochlear implants can restore hearing to deaf or partially deaf patients. In order to plan the intervention, a model from high resolution μCT images is to be built from accurate cochlea segmentations and then, adapted to a patient-specific model. Thus, a precise segmentation is required to build...

Adenosine-stress dynamic real-time myocardial perfusion CT and adenosine-stress first-pass dual-energy myocardial perfusion CT for the assessment of acute chest pain: Initial results

Energy Technology Data Exchange (ETDEWEB)

Weininger, Markus [Medical University of South Carolina, Department of Radiology and Radiological Science, Charleston, SC (United States); Schoepf, U. Joseph, E-mail: schoepf@musc.edu [Medical University of South Carolina, Department of Radiology and Radiological Science, Charleston, SC (United States); Department of Medicine, Division of Cardiology, Medical University of South Carolina, Charleston, SC (United States); Ramachandra, Ashok [Medical University of South Carolina, Department of Radiology and Radiological Science, Charleston, SC (United States); Fink, Christian [Institute of Clinical Radiology and Nuclear Medicine, University Medical Center Mannheim, Medical Faculty Mannheim, Heidelberg University (Germany); Rowe, Garrett W.; Costello, Philip [Medical University of South Carolina, Department of Radiology and Radiological Science, Charleston, SC (United States); Henzler, Thomas [Medical University of South Carolina, Department of Radiology and Radiological Science, Charleston, SC (United States); Institute of Clinical Radiology and Nuclear Medicine, University Medical Center Mannheim, Medical Faculty Mannheim, Heidelberg University (Germany)

2012-12-15

Purpose: Recent innovations in CT enable the evolution from mere morphologic imaging to dynamic and functional testing. We describe our initial experience performing myocardial stress perfusion CT in a clinical population with acute chest pain. Methods and materials: Myocardial stress perfusion CT was performed on twenty consecutive patients (15 men, 5 women; mean age 65 ± 8 years) who presented with acute chest pain and were clinically referred for stress/rest SPECT and cardiac MRI. Prior to CT each patient was randomly assigned either to Group A or to Group B in a consecutive order (10 patients per group). Group A underwent adenosine-stress dynamic real-time myocardial perfusion CT using a novel “shuttle” mode on a 2nd generation dual-source CT. Group B underwent adenosine-stress first-pass dual-energy myocardial perfusion CT using the same CT scanner in dual-energy mode. Two experienced observers visually analyzed all CT perfusion studies. CT findings were compared with MRI and SPECT. Results: In Group A 149/170 myocardial segments (88%) could be evaluated. Real-time perfusion CT (versus SPECT) had 86% (84%) sensitivity, 98% (92%) specificity, 94% (88%) positive predictive value, and 96% (92%) negative predictive value in comparison with perfusion MRI for the detection of myocardial perfusion defects. In Group B all myocardial segments were available for analysis. Compared with MRI, dual-energy myocardial perfusion CT (versus SPECT) had 93% (94%) sensitivity, 99% (98%) specificity, 92% (88%) positive predictive value, and 96% (94%) negative predictive value for detecting hypoperfused myocardial segments. Conclusion: Our results suggest the clinical feasibility of myocardial perfusion CT imaging in patients with acute chest pain. Compared to MRI and SPECT both, dynamic real-time perfusion CT and first-pass dual-energy perfusion CT showed good agreement for the detection of myocardial perfusion defects.

Adenosine-stress dynamic real-time myocardial perfusion CT and adenosine-stress first-pass dual-energy myocardial perfusion CT for the assessment of acute chest pain: Initial results

International Nuclear Information System (INIS)

Weininger, Markus; Schoepf, U. Joseph; Ramachandra, Ashok; Fink, Christian; Rowe, Garrett W.; Costello, Philip; Henzler, Thomas

2012-01-01

Purpose: Recent innovations in CT enable the evolution from mere morphologic imaging to dynamic and functional testing. We describe our initial experience performing myocardial stress perfusion CT in a clinical population with acute chest pain. Methods and materials: Myocardial stress perfusion CT was performed on twenty consecutive patients (15 men, 5 women; mean age 65 ± 8 years) who presented with acute chest pain and were clinically referred for stress/rest SPECT and cardiac MRI. Prior to CT each patient was randomly assigned either to Group A or to Group B in a consecutive order (10 patients per group). Group A underwent adenosine-stress dynamic real-time myocardial perfusion CT using a novel “shuttle” mode on a 2nd generation dual-source CT. Group B underwent adenosine-stress first-pass dual-energy myocardial perfusion CT using the same CT scanner in dual-energy mode. Two experienced observers visually analyzed all CT perfusion studies. CT findings were compared with MRI and SPECT. Results: In Group A 149/170 myocardial segments (88%) could be evaluated. Real-time perfusion CT (versus SPECT) had 86% (84%) sensitivity, 98% (92%) specificity, 94% (88%) positive predictive value, and 96% (92%) negative predictive value in comparison with perfusion MRI for the detection of myocardial perfusion defects. In Group B all myocardial segments were available for analysis. Compared with MRI, dual-energy myocardial perfusion CT (versus SPECT) had 93% (94%) sensitivity, 99% (98%) specificity, 92% (88%) positive predictive value, and 96% (94%) negative predictive value for detecting hypoperfused myocardial segments. Conclusion: Our results suggest the clinical feasibility of myocardial perfusion CT imaging in patients with acute chest pain. Compared to MRI and SPECT both, dynamic real-time perfusion CT and first-pass dual-energy perfusion CT showed good agreement for the detection of myocardial perfusion defects.

Segmentation of Synchrotron Radiation micro-Computed Tomography Images using Energy Minimization via Graph Cuts

Energy Technology Data Exchange (ETDEWEB)

Meneses, Anderson A.M. [Federal University of Western Para (Brazil); Physics Institute, Rio de Janeiro State University (Brazil); Giusti, Alessandro [IDSIA (Dalle Molle Institute for Artificial Intelligence), University of Lugano (Switzerland); Almeida, Andre P. de, E-mail: apalmeid@gmail.com [Physics Institute, Rio de Janeiro State University (Brazil); Nuclear Engineering Program, Federal University of Rio de Janeiro (Brazil); Nogueira, Liebert; Braz, Delson [Nuclear Engineering Program, Federal University of Rio de Janeiro (Brazil); Almeida, Carlos E. de [Radiological Sciences Laboratory, Rio de Janeiro State University (Brazil); Barroso, Regina C. [Physics Institute, Rio de Janeiro State University (Brazil)

2012-07-15

The research on applications of segmentation algorithms to Synchrotron Radiation X-Ray micro-Computed Tomography (SR-{mu}CT) is an open problem, due to the interesting and well-known characteristics of SR images, such as the phase contrast effect. The Energy Minimization via Graph Cuts (EMvGC) algorithm represents state-of-art segmentation algorithm, presenting an enormous potential of application in SR-{mu}CT imaging. We describe the application of the algorithm EMvGC with swap move for the segmentation of bone images acquired at the ELETTRA Laboratory (Trieste, Italy). - Highlights: Black-Right-Pointing-Pointer Microstructures of Wistar rats' ribs are investigated with Synchrotron Radiation {mu}CT imaging. Black-Right-Pointing-Pointer The present work is part of a research on the effects of radiotherapy on the thoracic region. Black-Right-Pointing-Pointer Application of the Energy Minimization via Graph Cuts algorithm for segmentation is described.

Functional CT imaging: load-dependent visualization of the subchondral mineralization by means of CT osteoabsorptionmetry (CT-OAM)

International Nuclear Information System (INIS)

Linsenmaier, U.; Schlichtenhorst, K.; Pfeifer, K.J.; Reiser, M.; Kersting, S.; Putz, R.; Mueller-Gerbl, M.

2003-01-01

Purpose: Functional computed tomography for visualization and quantification of subchondral bone mineralization using CT osteoabsorptiometry (CT-OAM). Materials and Methods: Tarsometatarsal (TMT) and metatarsophalangeal (MTP) joints of 46 human hallux valgus (HV) specimens were examined (sagittal 1/1/1 mm) on a single slice CT scanner SCT (Somatom Plus 4, Siemens AG). Subchondral bone pixels were segmented and assigned to 10 density value groups (triangle 100 HU, range 200 - 1200 HU) the pixels using volume rendering technique (VRT). The data analysis considered the severity of HV as determined by the radiographically measured HV-angle (a.p. projection). Results: CT-OAM could generate reproducible densitograms of the distribution pattern of the subchondral bone density for all four joint surfaces (TMT and MTP joints). The bone density localization enables the assignment to different groups, showing a characteristic HV-angle-dependent distribution of the maximum bone mineralization of the load-dependent densitogram (p [de

Fluence map segmentation

International Nuclear Information System (INIS)

Rosenwald, J.-C.

2008-01-01

The lecture addressed the following topics: 'Interpreting' the fluence map; The sequencer; Reasons for difference between desired and actual fluence map; Principle of 'Step and Shoot' segmentation; Large number of solutions for given fluence map; Optimizing 'step and shoot' segmentation; The interdigitation constraint; Main algorithms; Conclusions on segmentation algorithms (static mode); Optimizing intensity levels and monitor units; Sliding window sequencing; Synchronization to avoid the tongue-and-groove effect; Accounting for physical characteristics of MLC; Importance of corrections for leaf transmission and offset; Accounting for MLC mechanical constraints; The 'complexity' factor; Incorporating the sequencing into optimization algorithm; Data transfer to the treatment machine; Interface between R and V and accelerator; and Conclusions on fluence map segmentation (Segmentation is part of the overall inverse planning procedure; 'Step and Shoot' and 'Dynamic' options are available for most TPS (depending on accelerator model; The segmentation phase tends to come into the optimization loop; The physical characteristics of the MLC have a large influence on final dose distribution; The IMRT plans (MU and relative dose distribution) must be carefully validated). (P.A.)

CT image segmentation methods for bone used in medical additive manufacturing.

Science.gov (United States)

van Eijnatten, Maureen; van Dijk, Roelof; Dobbe, Johannes; Streekstra, Geert; Koivisto, Juha; Wolff, Jan

2018-01-01

The accuracy of additive manufactured medical constructs is limited by errors introduced during image segmentation. The aim of this study was to review the existing literature on different image segmentation methods used in medical additive manufacturing. Thirty-two publications that reported on the accuracy of bone segmentation based on computed tomography images were identified using PubMed, ScienceDirect, Scopus, and Google Scholar. The advantages and disadvantages of the different segmentation methods used in these studies were evaluated and reported accuracies were compared. The spread between the reported accuracies was large (0.04 mm - 1.9 mm). Global thresholding was the most commonly used segmentation method with accuracies under 0.6 mm. The disadvantage of this method is the extensive manual post-processing required. Advanced thresholding methods could improve the accuracy to under 0.38 mm. However, such methods are currently not included in commercial software packages. Statistical shape model methods resulted in accuracies from 0.25 mm to 1.9 mm but are only suitable for anatomical structures with moderate anatomical variations. Thresholding remains the most widely used segmentation method in medical additive manufacturing. To improve the accuracy and reduce the costs of patient-specific additive manufactured constructs, more advanced segmentation methods are required. Copyright © 2017 IPEM. Published by Elsevier Ltd. All rights reserved.

Auto-Segmentation of Head and Neck Cancer using Textural features

DEFF Research Database (Denmark)

Hollensen, Christian; Jørgensen, Peter Stanley; Højgaard, Liselotte

- and intra observer variability. Several automatic segmentation methods have been developed using positron emission tomography (PET) and/or computerised tomography (CT). The aim of the present study is to develop a model for 3-dimensional auto-segmentation, the level set method, to contour gross tumour...

Efficacy of IV Buscopan as a muscle relaxant in CT colonography

International Nuclear Information System (INIS)

Bruzzi, John F.; Brennan, Darren D.; Fenlon, Helen M.; Moss, Alan C.; MacMathuna, Padraic

2003-01-01

The aim of this study was to examine the efficacy of IV Buscopan as a muscle relaxant in CT colonography in terms of colonic distension and polyp detection, and to determine its particular efficacy in patients with diverticular disease. Seventy-three consecutive patients were randomised to receive IV Buscopan or no muscle relaxant prior to CT colonography. CT colonography was performed using a Siemens Somatom 4-detector multislice CT scanner. The following parameters were recorded: degree of colonic distension using a 4-point scale; diagnostic adequacy of colonic distension; presence or absence of diverticular disease; and presence of colonic polyps. Accuracy of polyp detection was assessed using subsequent conventional colonoscopy as a gold standard. There was no significant difference between the two groups in the number of segments that were deemed to be optimally or adequately distended (p=0.37). Although IV Buscopan did improve distension of certain segments, this effect was not sufficient to improve the number of diagnostically adequate studies in the Buscopan group (p=0.14). In patients with diverticular disease, IV Buscopan did not have any significant effect on segments affected by diverticulosis but was associated with an improvement in distension of more proximal segments. There was no significant difference between the two groups in terms of polyp detection (p=0.34). The addition of prone scanning to supine scanning was found to be the most useful technique for maximising colonic distension. Intravenous Buscopan at CT colonography does not improve the overall adequacy of colonic distension nor the accuracy of polyp detection. In patients with sigmoid diverticular disease IV Buscopan improves distension of more proximal colonic segments and may be useful in selected cases, but our results do not support its routine use for CT colonography. (orig.)

Automatic 2D segmentation of airways in thorax computed tomography images

International Nuclear Information System (INIS)

Cavalcante, Tarique da Silveira; Cortez, Paulo Cesar; Almeida, Thomaz Maia de; Felix, John Hebert da Silva; Holanda, Marcelo Alcantara

2013-01-01

Introduction: much of the world population is affected by pulmonary diseases, such as the bronchial asthma, bronchitis and bronchiectasis. The bronchial diagnosis is based on the airways state. In this sense, the automatic segmentation of the airways in Computed Tomography (CT) scans is a critical step in the aid to diagnosis of these diseases. Methods: this paper evaluates algorithms for airway automatic segmentation, using Neural Network Multilayer Perceptron (MLP) and Lung Densities Analysis (LDA) for detecting airways, along with Region Growing (RG), Active Contour Method (ACM) Balloon and Topology Adaptive to segment them. Results: we obtained results in three stages: comparative analysis of the detection algorithms MLP and LDA, with a gold standard acquired by three physicians with expertise in CT imaging of the chest; comparative analysis of segmentation algorithms ACM Balloon, ACM Topology Adaptive, MLP and RG; and evaluation of possible combinations between segmentation and detection algorithms, resulting in the complete method for automatic segmentation of the airways in 2D. Conclusion: the low incidence of false negative and the significant reduction of false positive, results in similarity coefficient and sensitivity exceeding 91% and 87% respectively, for a combination of algorithms with satisfactory segmentation quality. (author)

Mycoplasma pneumoniae pneumonia: CT features in 16 patients

Energy Technology Data Exchange (ETDEWEB)

Lee, Inho; Kim, Tae Sung; Yoon, Hye-Kyung [Sungkyunkwan University School of Medicine, Department of Radiology and Center for Imaging Science, Samsung Medical Center, Seoul (Korea)

2006-03-15

The objective of this study was to assess the computed tomography (CT) features of Mycoplasma pneumoniae pneumonia. We retrospectively reviewed CT findings of 16 patients (M:F=9:7, age range 1-74 years, median 9 years) with serologically proven Mycoplasma pneumoniae pneumonia and with chest CT scan available. Two distinctive patterns of CT features of M. pneumoniae pneumonia were noted between the paediatric (age <18 years) and the adult (age {>=}18 years) groups. The pediatric group (n=11) showed lobar or segmental consolidation (100%) with frequent pleural effusion (82%) and regional lymphadenopathy (82%) and mild volume decrease of the involved lobe (73%), while four of the five adult patients showed diffuse and/or multifocal, centrilobular or peribronchovascular areas of ground-glass attenuation (80%) with a lobular distribution, and frequent thickening of interlobular septa (60%) and the bronchial walls (40%) were also detected at high-resolution CT. The CT finding of a lobar or segmental consolidation with a parapneumonic effusion seen in our children with M. pneumoniae pneumonia was similar to that of bacterial lobar pneumonia. In contrast, the CT findings noted in our adult patients consisted of a mixture of a bacterial bronchopneumonia pattern and a viral interstitial pneumonia pattern. (orig.)

Mycoplasma pneumoniae pneumonia: CT features in 16 patients

International Nuclear Information System (INIS)

Lee, Inho; Kim, Tae Sung; Yoon, Hye-Kyung

2006-01-01

The objective of this study was to assess the computed tomography (CT) features of Mycoplasma pneumoniae pneumonia. We retrospectively reviewed CT findings of 16 patients (M:F=9:7, age range 1-74 years, median 9 years) with serologically proven Mycoplasma pneumoniae pneumonia and with chest CT scan available. Two distinctive patterns of CT features of M. pneumoniae pneumonia were noted between the paediatric (age <18 years) and the adult (age ≥18 years) groups. The pediatric group (n=11) showed lobar or segmental consolidation (100%) with frequent pleural effusion (82%) and regional lymphadenopathy (82%) and mild volume decrease of the involved lobe (73%), while four of the five adult patients showed diffuse and/or multifocal, centrilobular or peribronchovascular areas of ground-glass attenuation (80%) with a lobular distribution, and frequent thickening of interlobular septa (60%) and the bronchial walls (40%) were also detected at high-resolution CT. The CT finding of a lobar or segmental consolidation with a parapneumonic effusion seen in our children with M. pneumoniae pneumonia was similar to that of bacterial lobar pneumonia. In contrast, the CT findings noted in our adult patients consisted of a mixture of a bacterial bronchopneumonia pattern and a viral interstitial pneumonia pattern. (orig.)

Fuzzy clustering-based segmented attenuation correction in whole-body PET

CERN Document Server

Zaidi, H; Boudraa, A; Slosman, DO

2001-01-01

Segmented-based attenuation correction is now a widely accepted technique to reduce noise contribution of measured attenuation correction. In this paper, we present a new method for segmenting transmission images in positron emission tomography. This reduces the noise on the correction maps while still correcting for differing attenuation coefficients of specific tissues. Based on the Fuzzy C-Means (FCM) algorithm, the method segments the PET transmission images into a given number of clusters to extract specific areas of differing attenuation such as air, the lungs and soft tissue, preceded by a median filtering procedure. The reconstructed transmission image voxels are therefore segmented into populations of uniform attenuation based on the human anatomy. The clustering procedure starts with an over-specified number of clusters followed by a merging process to group clusters with similar properties and remove some undesired substructures using anatomical knowledge. The method is unsupervised, adaptive and a...

Novel approach for identification of influenza virus host range and zoonotic transmissible sequences by determination of host-related associative positions in viral genome segments.

Science.gov (United States)

Kargarfard, Fatemeh; Sami, Ashkan; Mohammadi-Dehcheshmeh, Manijeh; Ebrahimie, Esmaeil

2016-11-16

Recent (2013 and 2009) zoonotic transmission of avian or porcine influenza to humans highlights an increase in host range by evading species barriers. Gene reassortment or antigenic shift between viruses from two or more hosts can generate a new life-threatening virus when the new shuffled virus is no longer recognized by antibodies existing within human populations. There is no large scale study to help understand the underlying mechanisms of host transmission. Furthermore, there is no clear understanding of how different segments of the influenza genome contribute in the final determination of host range. To obtain insight into the rules underpinning host range determination, various supervised machine learning algorithms were employed to mine reassortment changes in different viral segments in a range of hosts. Our multi-host dataset contained whole segments of 674 influenza strains organized into three host categories: avian, human, and swine. Some of the sequences were assigned to multiple hosts. In point of fact, the datasets are a form of multi-labeled dataset and we utilized a multi-label learning method to identify discriminative sequence sites. Then algorithms such as CBA, Ripper, and decision tree were applied to extract informative and descriptive association rules for each viral protein segment. We found informative rules in all segments that are common within the same host class but varied between different hosts. For example, for infection of an avian host, HA14V and NS1230S were the most important discriminative and combinatorial positions. Host range identification is facilitated by high support combined rules in this study. Our major goal was to detect discriminative genomic positions that were able to identify multi host viruses, because such viruses are likely to cause pandemic or disastrous epidemics.

Coronary artery imaging with 64-slice spiral CT in atrial fibrillation patients: initial experience

International Nuclear Information System (INIS)

Zhou Xuhui; Yan Chaogui; Xie Hongbo; Li Xiangmin; Li Ziping; Meng Quanfei; Chen Xing

2008-01-01

Objective: To discuss the clinical value of coronary artery imaging using 64-slice spiral CT in patient with atrial fibrillation. Methods: The images of 31 patients with atrial fibrillation who underwent contrast-enhanced CT coronary angiography were evaluated. The presence of stenosis on each segment of coronary arteries was recorded and their degree of stenosis was measured using the vessel analysis software. Ten patients additionally underwent conventional coronary angiography. The results of conventional coronary angiography were compared with CT coronary angiography of the 10 patients. Results: Image reconstruction was based on absolute timing. The image quality of 364 coronary vessel segments on the images from 31 patients was evaluated and defined as excellent, fine, moderate or poor. The image quality was excellent, fine, moderate and poor in 85, 41, 5, and 8 vessel segments respectively in patient group with heart rate between 47 beat per minent (bpm) and 69 bpm; and in 63, 16, 13, and 15 vessel segments respectively in patent group with heart rate between 70 bpm and 79 bpm;and in 46, 25, 23, and 24 vessel segments in patient group with heart rate between 80 bpm and 105 bpm. There was significant difference among the three patient groups (H=22.08, P<0.01). Comparison was carried out between CT angiographic findings and conventional angiographic findings of the 125 segments of the coronary arteries in the 10 patients who underwent conventional coronary angiography. The sensitivity and specificity of CT angiography for diagnosing vessel with significant coronary stenosis (≥50% narrowing) was 85.0% (17/20) and 95.2% (100/105), respectively. Positive predictive value was 77.3% (17/22), and negative predictive value was 97.1% (100/103). Coronary CTA underestimated the lesions of 3 vessel segments and overestimated the lesions of 5 vessel segments. Conclusion: Coronary artery imaging with 64-slice row CT had clinical value for patients with atrial fibrillation

Classication Methods for CT-Scanned Carcass Midsections

DEFF Research Database (Denmark)

Skytte, Jacob Lercke; Dahl, Anders Lindbjerg; Larsen, Rasmus

2011-01-01

Computed tomography (CT) has successfully been applied in medical environments for decades. In recent years CT has also made its entry to the industrial environments, including the slaughterhouses. In this paper we investigate classication methods for an online CT system, in order to assist...... in the segmentation of the outer fat layer in the mid- section of CT-scanned pig carcasses. Prior information about the carcass composition can potentially be applied for a fully automated solution, in order to optimize the slaughter line. The methods comprise Markov Random Field and contextual Bayesian classication...

MO-F-CAMPUS-J-04: Tissue Segmentation-Based MR Electron Density Mapping Method for MR-Only Radiation Treatment Planning of Brain

Energy Technology Data Exchange (ETDEWEB)

Yu, H [Sunnybrook Health Sciences Centre, Toronto, Ontario (Canada); Lee, Y [Sunnybrook Odette Cancer Centre, Toronto, Ontario (Canada); Ruschin, M [Odette Cancer Centre, Toronto, ON (Canada); Karam, I [Sunnybrook Odette Cancer Center, Toronto, Ontario (Canada); Sahgal, A [University of Toronto, Toronto, ON (Canada)

2015-06-15

Purpose: Automatically derive electron density of tissues using MR images and generate a pseudo-CT for MR-only treatment planning of brain tumours. Methods: 20 stereotactic radiosurgery (SRS) patients’ T1-weighted MR images and CT images were retrospectively acquired. First, a semi-automated tissue segmentation algorithm was developed to differentiate tissues with similar MR intensities and large differences in electron densities. The method started with approximately 12 slices of manually contoured spatial regions containing sinuses and airways, then air, bone, brain, cerebrospinal fluid (CSF) and eyes were automatically segmented using edge detection and anatomical information including location, shape, tissue uniformity and relative intensity distribution. Next, soft tissues - muscle and fat were segmented based on their relative intensity histogram. Finally, intensities of voxels in each segmented tissue were mapped into their electron density range to generate pseudo-CT by linearly fitting their relative intensity histograms. Co-registered CT was used as a ground truth. The bone segmentations of pseudo-CT were compared with those of co-registered CT obtained by using a 300HU threshold. The average distances between voxels on external edges of the skull of pseudo-CT and CT in three axial, coronal and sagittal slices with the largest width of skull were calculated. The mean absolute electron density (in Hounsfield unit) difference of voxels in each segmented tissues was calculated. Results: The average of distances between voxels on external skull from pseudo-CT and CT were 0.6±1.1mm (mean±1SD). The mean absolute electron density differences for bone, brain, CSF, muscle and fat are 78±114 HU, and 21±8 HU, 14±29 HU, 57±37 HU, and 31±63 HU, respectively. Conclusion: The semi-automated MR electron density mapping technique was developed using T1-weighted MR images. The generated pseudo-CT is comparable to that of CT in terms of anatomical position of

MIA-Clustering: a novel method for segmentation of paleontological material

Directory of Open Access Journals (Sweden)

Christopher J. Dunmore

2018-02-01

Full Text Available Paleontological research increasingly uses high-resolution micro-computed tomography (μCT to study the inner architecture of modern and fossil bone material to answer important questions regarding vertebrate evolution. This non-destructive method allows for the measurement of otherwise inaccessible morphology. Digital measurement is predicated on the accurate segmentation of modern or fossilized bone from other structures imaged in μCT scans, as errors in segmentation can result in inaccurate calculations of structural parameters. Several approaches to image segmentation have been proposed with varying degrees of automation, ranging from completely manual segmentation, to the selection of input parameters required for computational algorithms. Many of these segmentation algorithms provide speed and reproducibility at the cost of flexibility that manual segmentation provides. In particular, the segmentation of modern and fossil bone in the presence of materials such as desiccated soft tissue, soil matrix or precipitated crystalline material can be difficult. Here we present a free open-source segmentation algorithm application capable of segmenting modern and fossil bone, which also reduces subjective user decisions to a minimum. We compare the effectiveness of this algorithm with another leading method by using both to measure the parameters of a known dimension reference object, as well as to segment an example problematic fossil scan. The results demonstrate that the medical image analysis-clustering method produces accurate segmentations and offers more flexibility than those of equivalent precision. Its free availability, flexibility to deal with non-bone inclusions and limited need for user input give it broad applicability in anthropological, anatomical, and paleontological contexts.

MIA-Clustering: a novel method for segmentation of paleontological material.

Science.gov (United States)

Dunmore, Christopher J; Wollny, Gert; Skinner, Matthew M

2018-01-01

Paleontological research increasingly uses high-resolution micro-computed tomography (μCT) to study the inner architecture of modern and fossil bone material to answer important questions regarding vertebrate evolution. This non-destructive method allows for the measurement of otherwise inaccessible morphology. Digital measurement is predicated on the accurate segmentation of modern or fossilized bone from other structures imaged in μCT scans, as errors in segmentation can result in inaccurate calculations of structural parameters. Several approaches to image segmentation have been proposed with varying degrees of automation, ranging from completely manual segmentation, to the selection of input parameters required for computational algorithms. Many of these segmentation algorithms provide speed and reproducibility at the cost of flexibility that manual segmentation provides. In particular, the segmentation of modern and fossil bone in the presence of materials such as desiccated soft tissue, soil matrix or precipitated crystalline material can be difficult. Here we present a free open-source segmentation algorithm application capable of segmenting modern and fossil bone, which also reduces subjective user decisions to a minimum. We compare the effectiveness of this algorithm with another leading method by using both to measure the parameters of a known dimension reference object, as well as to segment an example problematic fossil scan. The results demonstrate that the medical image analysis-clustering method produces accurate segmentations and offers more flexibility than those of equivalent precision. Its free availability, flexibility to deal with non-bone inclusions and limited need for user input give it broad applicability in anthropological, anatomical, and paleontological contexts.

Intratemporal facial nerve neuromas and their mimics: CT and MR findings

Energy Technology Data Exchange (ETDEWEB)

Han, Moon Hee; Chang, Kee Hyun; Lee, Kyung Hwan; Cha, Sang Hoon; Kim, Chong Sun [Seoul National University College of Medicine, Seoul (Korea, Republic of); Kim, Sang Joon [Chungang Gil General Hospital, Seoul (Korea, Republic of)

1992-05-15

CT and MR findings of nine cases with intra temporal facial nerve neuromas were described and compared with CT findings of 3 cases with facial nerve palsy and facial nerve canal erosion which may mimic facial nerve neuroma. The tympanic segment of the facial nerve was involved in 8 cases, mastoid segment in 7 cases and labyrinthine segment in 5 cases. The lesions were easily diagnosed with high resolution CT with bone algorithms by showing the expansion of bony structures along the course of the facial nerves. In 4 cases with large vertical segment tumors, extensive destruction of mastoid air cells and external auditory canals posed difficulty in making a diagnosis. Two out of 5 cases with labyrinthine segment involvement were presented as middle cranial fossa masses. MRI with enhancement was performed in 4 cases and was useful in characterizing the lesion as a tumor with its superior sensitivity to enhancement. Three cases of facial neuroma-mimicking lesion including post-inflammatory peri neural thickening, peri neural extension from parotid adenoid cystic carcinoma, and congenita; cholesteatoma showed irregular erosion or mild expansion of the facial nerve canal which may be helpful for differential diagnosis from neuromas.

TU-H-207A-03: CT Hounsfield Unit Accuracy: Effect of Beam Hardening On Phantom and Clinical Whole-Body CT Images

Energy Technology Data Exchange (ETDEWEB)

Ai, H; Wendt, R [The University of Texas MD Anderson Cancer Center, Houston, TX (United States)

2016-06-15

Purpose: To assess the effect of beam hardening on measured CT HU values. Methods: An anthropomorphic knee phantom was scanned with the CT component of a GE Discovery 690 PET/CT scanner (120kVp, 300mAs, 40?0.625mm collimation, pitch=0.984, FOV=500mm, matrix=512?512) with four different scan setups, each of which induces different degrees of beam hardening by introducing additional attenuation media into the field of view. Homogeneous voxels representing “soft tissue” and “bone” were segmented by HU thresholding followed by a 3D morphological erosion operation which removes the non-homogenous voxels located on the interface of thresholded tissue mask. HU values of segmented “soft tissue” and “bone” were compared.Additionally, whole-body CT data with coverage from the skull apex to the end of toes were retrospectively retrieved from seven PET/CT exams to evaluate the effect of beam hardening in vivo. Homogeneous bone voxels were segmented with the same method previously described. Total In-Slice Attenuation (TISA) for each CT slice, defined as the summation of HU values over all voxels within a CT slice, was calculated for all slices of the seven whole-body CT datasets and evaluated against the mean HU values of homogeneous bone voxels within that slice. Results: HU values measured from the phantom showed that while “soft tissue” HU values were unaffected, added attenuation within the FOV caused noticeable decreases in the measured HU values of “bone” voxels. A linear relationship was observed between bone HU and TISA for slices of the torso and legs, but not of the skull. Conclusion: Beam hardening effect is not an issue of concern for voxels with HU in the soft tissue range, but should not be neglected for bone voxels. A linear relationship exists between bone HU and the associated TISA in non-skull CT slices, which can be exploited to develop a correction strategy.

Superpixel-based and boundary-sensitive convolutional neural network for automated liver segmentation

Science.gov (United States)

Qin, Wenjian; Wu, Jia; Han, Fei; Yuan, Yixuan; Zhao, Wei; Ibragimov, Bulat; Gu, Jia; Xing, Lei

2018-05-01

Segmentation of liver in abdominal computed tomography (CT) is an important step for radiation therapy planning of hepatocellular carcinoma. Practically, a fully automatic segmentation of liver remains challenging because of low soft tissue contrast between liver and its surrounding organs, and its highly deformable shape. The purpose of this work is to develop a novel superpixel-based and boundary sensitive convolutional neural network (SBBS-CNN) pipeline for automated liver segmentation. The entire CT images were first partitioned into superpixel regions, where nearby pixels with similar CT number were aggregated. Secondly, we converted the conventional binary segmentation into a multinomial classification by labeling the superpixels into three classes: interior liver, liver boundary, and non-liver background. By doing this, the boundary region of the liver was explicitly identified and highlighted for the subsequent classification. Thirdly, we computed an entropy-based saliency map for each CT volume, and leveraged this map to guide the sampling of image patches over the superpixels. In this way, more patches were extracted from informative regions (e.g. the liver boundary with irregular changes) and fewer patches were extracted from homogeneous regions. Finally, deep CNN pipeline was built and trained to predict the probability map of the liver boundary. We tested the proposed algorithm in a cohort of 100 patients. With 10-fold cross validation, the SBBS-CNN achieved mean Dice similarity coefficients of 97.31  ±  0.36% and average symmetric surface distance of 1.77  ±  0.49 mm. Moreover, it showed superior performance in comparison with state-of-art methods, including U-Net, pixel-based CNN, active contour, level-sets and graph-cut algorithms. SBBS-CNN provides an accurate and effective tool for automated liver segmentation. It is also envisioned that the proposed framework is directly applicable in other medical image segmentation scenarios.

CT diagnosis of annular pancreas

International Nuclear Information System (INIS)

Ueno, Eiko; Isobe, Yoshinori; Niimi, Akiko; Shimizu, Yasushi; Yamada, Akiyoshi; Hanyu, Fujio

1987-01-01

CT scan was performed in two cases of annular pancreas which could be found in one case preoperatively and in the other case retrospectively. CT scan demonstrated secondary changes of annular pancreas such as medial displacement and dilatation of the duodenal bulb in the former case and stenosis of the duodenal loop and thickened soft tissue density around the narrow segment of the duodenal loop in the latter case, although it failed to demonstrate the peninsular protrusion of the parenchyma of the pancreas head. These findings suggest high possibility of diagnosing annular pancreas by CT scan. (author)

3D intrathoracic region definition and its application to PET-CT analysis

Science.gov (United States)

Cheirsilp, Ronnarit; Bascom, Rebecca; Allen, Thomas W.; Higgins, William E.

2014-03-01

Recently developed integrated PET-CT scanners give co-registered multimodal data sets that offer complementary three-dimensional (3D) digital images of the chest. PET (positron emission tomography) imaging gives highly specific functional information of suspect cancer sites, while CT (X-ray computed tomography) gives associated anatomical detail. Because the 3D CT and PET scans generally span the body from the eyes to the knees, accurate definition of the intrathoracic region is vital for focusing attention to the central-chest region. In this way, diagnostically important regions of interest (ROIs), such as central-chest lymph nodes and cancer nodules, can be more efficiently isolated. We propose a method for automatic segmentation of the intrathoracic region from a given co-registered 3D PET-CT study. Using the 3D CT scan as input, the method begins by finding an initial intrathoracic region boundary for a given 2D CT section. Next, active contour analysis, driven by a cost function depending on local image gradient, gradient-direction, and contour shape features, iteratively estimates the contours spanning the intrathoracic region on neighboring 2D CT sections. This process continues until the complete region is defined. We next present an interactive system that employs the segmentation method for focused 3D PET-CT chest image analysis. A validation study over a series of PET-CT studies reveals that the segmentation method gives a Dice index accuracy of less than 98%. In addition, further results demonstrate the utility of the method for focused 3D PET-CT chest image analysis, ROI definition, and visualization.

Clinical significance of segmental parenchymal excretion delay on Tc-99m DISIDA hepatobiliary scan

International Nuclear Information System (INIS)

Kang, D. Y.; Ryu, J. S.; Moon, D. H.; Lee, S. K.; Kim, M. H.; Lee, H. K.

1998-01-01

Segmental parenchymal excretion delay on Tc-99m DISIDA scan in caused by intrahepatic bile duct obstruction. However, the diagnostic value for intrahepatic bile duct obstruction is unknown. We conducted this study to assess the positive predictive value of segmental excretion delay for the diagnosis of intrahepatic bile duct obstruction, and additional benefit over other noninvasive radiologic studies. The study population consisted of 43 patients (48 scans) who showed segmental parenchymal excretion delay on Tc-99m DISIDA scan. The results of abdominal CT or ultrasonography, which was done within 1 month of Tc-99m DISIDA scan, were compared with scintigraphic findings. The etiology of segmental parenchymal excretion delay was determined by ERC or PTC in 31 scans, and follow-up studies in 13 scans. No causes were identified in 4 scans. The positive predictive value of segmental parenchymal excretion delay for intrahepatic bile duct obstruction was 92% (44/48). On the other hand, 13% (5/38) of CT and 28% (5/18) of ultrasonography were normal. In 18% *7/38) of CT and 17% (3/18) of ultrasonography, only intrahepatic bile duct dilatation was noted without any diagnostic findings of intrahepatic bile duct obstruction. Segmental parenchymal excretion delay on Tc-99m DISIDA scan had a high positive predictive value for the diagnosis of intrahepatic bile duct obstruction. Tc-99m DISIDA scan may be useful for the diagnosis of intrahepatic bile duct obstruction, especially in patients with nondiagnostic CT or ultrasonography. The diagnostic usefulness need to be confirmed by further prospective studies

Automatic segmentation of rotational x-ray images for anatomic intra-procedural surface generation in atrial fibrillation ablation procedures.

Science.gov (United States)

Manzke, Robert; Meyer, Carsten; Ecabert, Olivier; Peters, Jochen; Noordhoek, Niels J; Thiagalingam, Aravinda; Reddy, Vivek Y; Chan, Raymond C; Weese, Jürgen

2010-02-01

Since the introduction of 3-D rotational X-ray imaging, protocols for 3-D rotational coronary artery imaging have become widely available in routine clinical practice. Intra-procedural cardiac imaging in a computed tomography (CT)-like fashion has been particularly compelling due to the reduction of clinical overhead and ability to characterize anatomy at the time of intervention. We previously introduced a clinically feasible approach for imaging the left atrium and pulmonary veins (LAPVs) with short contrast bolus injections and scan times of approximately 4 -10 s. The resulting data have sufficient image quality for intra-procedural use during electro-anatomic mapping (EAM) and interventional guidance in atrial fibrillation (AF) ablation procedures. In this paper, we present a novel technique to intra-procedural surface generation which integrates fully-automated segmentation of the LAPVs for guidance in AF ablation interventions. Contrast-enhanced rotational X-ray angiography (3-D RA) acquisitions in combination with filtered-back-projection-based reconstruction allows for volumetric interrogation of LAPV anatomy in near-real-time. An automatic model-based segmentation algorithm allows for fast and accurate LAPV mesh generation despite the challenges posed by image quality; relative to pre-procedural cardiac CT/MR, 3-D RA images suffer from more artifacts and reduced signal-to-noise. We validate our integrated method by comparing 1) automatic and manual segmentations of intra-procedural 3-D RA data, 2) automatic segmentations of intra-procedural 3-D RA and pre-procedural CT/MR data, and 3) intra-procedural EAM point cloud data with automatic segmentations of 3-D RA and CT/MR data. Our validation results for automatically segmented intra-procedural 3-D RA data show average segmentation errors of 1) approximately 1.3 mm compared with manual 3-D RA segmentations 2) approximately 2.3 mm compared with automatic segmentation of pre-procedural CT/MR data and 3

SU-C-207B-05: Tissue Segmentation of Computed Tomography Images Using a Random Forest Algorithm: A Feasibility Study

International Nuclear Information System (INIS)

Polan, D; Brady, S; Kaufman, R

2016-01-01

Purpose: Develop an automated Random Forest algorithm for tissue segmentation of CT examinations. Methods: Seven materials were classified for segmentation: background, lung/internal gas, fat, muscle, solid organ parenchyma, blood/contrast, and bone using Matlab and the Trainable Weka Segmentation (TWS) plugin of FIJI. The following classifier feature filters of TWS were investigated: minimum, maximum, mean, and variance each evaluated over a pixel radius of 2n, (n = 0–4). Also noise reduction and edge preserving filters, Gaussian, bilateral, Kuwahara, and anisotropic diffusion, were evaluated. The algorithm used 200 trees with 2 features per node. A training data set was established using an anonymized patient’s (male, 20 yr, 72 kg) chest-abdomen-pelvis CT examination. To establish segmentation ground truth, the training data were manually segmented using Eclipse planning software, and an intra-observer reproducibility test was conducted. Six additional patient data sets were segmented based on classifier data generated from the training data. Accuracy of segmentation was determined by calculating the Dice similarity coefficient (DSC) between manual and auto segmented images. Results: The optimized autosegmentation algorithm resulted in 16 features calculated using maximum, mean, variance, and Gaussian blur filters with kernel radii of 1, 2, and 4 pixels, in addition to the original CT number, and Kuwahara filter (linear kernel of 19 pixels). Ground truth had a DSC of 0.94 (range: 0.90–0.99) for adult and 0.92 (range: 0.85–0.99) for pediatric data sets across all seven segmentation classes. The automated algorithm produced segmentation with an average DSC of 0.85 ± 0.04 (range: 0.81–1.00) for the adult patients, and 0.86 ± 0.03 (range: 0.80–0.99) for the pediatric patients. Conclusion: The TWS Random Forest auto-segmentation algorithm was optimized for CT environment, and able to segment seven material classes over a range of body habitus and CT

SU-C-207B-05: Tissue Segmentation of Computed Tomography Images Using a Random Forest Algorithm: A Feasibility Study

Energy Technology Data Exchange (ETDEWEB)

Polan, D [University of Michigan, Ann Arbor, MI (United States); Brady, S; Kaufman, R [St. Jude Children’s Research Hospital, Memphis, TN (United States)

2016-06-15

Purpose: Develop an automated Random Forest algorithm for tissue segmentation of CT examinations. Methods: Seven materials were classified for segmentation: background, lung/internal gas, fat, muscle, solid organ parenchyma, blood/contrast, and bone using Matlab and the Trainable Weka Segmentation (TWS) plugin of FIJI. The following classifier feature filters of TWS were investigated: minimum, maximum, mean, and variance each evaluated over a pixel radius of 2n, (n = 0–4). Also noise reduction and edge preserving filters, Gaussian, bilateral, Kuwahara, and anisotropic diffusion, were evaluated. The algorithm used 200 trees with 2 features per node. A training data set was established using an anonymized patient’s (male, 20 yr, 72 kg) chest-abdomen-pelvis CT examination. To establish segmentation ground truth, the training data were manually segmented using Eclipse planning software, and an intra-observer reproducibility test was conducted. Six additional patient data sets were segmented based on classifier data generated from the training data. Accuracy of segmentation was determined by calculating the Dice similarity coefficient (DSC) between manual and auto segmented images. Results: The optimized autosegmentation algorithm resulted in 16 features calculated using maximum, mean, variance, and Gaussian blur filters with kernel radii of 1, 2, and 4 pixels, in addition to the original CT number, and Kuwahara filter (linear kernel of 19 pixels). Ground truth had a DSC of 0.94 (range: 0.90–0.99) for adult and 0.92 (range: 0.85–0.99) for pediatric data sets across all seven segmentation classes. The automated algorithm produced segmentation with an average DSC of 0.85 ± 0.04 (range: 0.81–1.00) for the adult patients, and 0.86 ± 0.03 (range: 0.80–0.99) for the pediatric patients. Conclusion: The TWS Random Forest auto-segmentation algorithm was optimized for CT environment, and able to segment seven material classes over a range of body habitus and CT

18F-FDG PET/CT Equivalent of the Hepatic Hot Spot Sign With CT Correlation.

Science.gov (United States)

Jundt, Michael C; Broski, Stephen M; Binkovitz, Larry A

2018-05-01

A 43-year-old woman presented with an FDG-avid mediastinal Ewing sarcoma invading and nearly occluding the superior vena cava. Geographic increased FDG uptake in hepatic segment IVA was the only other site of nonphysiologic FDG activity. This focal activity was without an underlying mass, had atypical morphology for a hepatic metastasis, and correlated well with prior CT findings of abnormal segment IVA enhancement resulting from the recruitment of portocaval collaterals. In the correct setting, the F-FDG hepatic hot spot should be considered in the differential of a focal FDG-avid hepatic lesion in segment IVA.

Segmentation of kidney using C-V model and anatomy priors

Science.gov (United States)

Lu, Jinghua; Chen, Jie; Zhang, Juan; Yang, Wenjia

2007-12-01

This paper presents an approach for kidney segmentation on abdominal CT images as the first step of a virtual reality surgery system. Segmentation for medical images is often challenging because of the objects' complicated anatomical structures, various gray levels, and unclear edges. A coarse to fine approach has been applied in the kidney segmentation using Chan-Vese model (C-V model) and anatomy prior knowledge. In pre-processing stage, the candidate kidney regions are located. Then C-V model formulated by level set method is applied in these smaller ROI, which can reduce the calculation complexity to a certain extent. At last, after some mathematical morphology procedures, the specified kidney structures have been extracted interactively with prior knowledge. The satisfying results on abdominal CT series show that the proposed approach keeps all the advantages of C-V model and overcome its disadvantages.

Segmentation methodology for automated classification and differentiation of soft tissues in multiband images of high-resolution ultrasonic transmission tomography.

Science.gov (United States)

Jeong, Jeong-Won; Shin, Dae C; Do, Synho; Marmarelis, Vasilis Z

2006-08-01

This paper presents a novel segmentation methodology for automated classification and differentiation of soft tissues using multiband data obtained with the newly developed system of high-resolution ultrasonic transmission tomography (HUTT) for imaging biological organs. This methodology extends and combines two existing approaches: the L-level set active contour (AC) segmentation approach and the agglomerative hierarchical kappa-means approach for unsupervised clustering (UC). To prevent the trapping of the current iterative minimization AC algorithm in a local minimum, we introduce a multiresolution approach that applies the level set functions at successively increasing resolutions of the image data. The resulting AC clusters are subsequently rearranged by the UC algorithm that seeks the optimal set of clusters yielding the minimum within-cluster distances in the feature space. The presented results from Monte Carlo simulations and experimental animal-tissue data demonstrate that the proposed methodology outperforms other existing methods without depending on heuristic parameters and provides a reliable means for soft tissue differentiation in HUTT images.

Influence of Co-57 and CT Transmission Measurements on the Quantification Accuracy and Partial Volume Effect of a Small Animal PET Scanner.

Science.gov (United States)

Mannheim, Julia G; Schmid, Andreas M; Pichler, Bernd J

2017-12-01

Non-invasive in vivo positron emission tomography (PET) provides high detection sensitivity in the nano- to picomolar range and in addition to other advantages, the possibility to absolutely quantify the acquired data. The present study focuses on the comparison of transmission data acquired with an X-ray computed tomography (CT) scanner or a Co-57 source for the Inveon small animal PET scanner (Siemens Healthcare, Knoxville, TN, USA), as well as determines their influences on the quantification accuracy and partial volume effect (PVE). A special focus included the impact of the performed calibration on the quantification accuracy. Phantom measurements were carried out to determine the quantification accuracy, the influence of the object size on the quantification, and the PVE for different sphere sizes, along the field of view and for different contrast ratios. An influence of the emission activity on the Co-57 transmission measurements was discovered (deviations up to 24.06 % measured to true activity), whereas no influence of the emission activity on the CT attenuation correction was identified (deviations influenced by the applied calibration factor and by the object size. The PVE demonstrated a dependency on the sphere size, the position within the field of view, the reconstruction and correction algorithms and the count statistics. Depending on the reconstruction algorithm, only ∼30-40 % of the true activity within a small sphere could be resolved. The iterative 3D reconstruction algorithms uncovered substantially increased recovery values compared to the analytical and 2D iterative reconstruction algorithms (up to 70.46 % and 80.82 % recovery for the smallest and largest sphere using iterative 3D reconstruction algorithms). The transmission measurement (CT or Co-57 source) to correct for attenuation did not severely influence the PVE. The analysis of the quantification accuracy and the PVE revealed an influence of the object size, the reconstruction

Fully automatic segmentation of arbitrarily shaped fiducial markers in cone-beam CT projections

DEFF Research Database (Denmark)

Bertholet, Jenny; Wan, Hanlin; Toftegaard, Jakob

2017-01-01

segmentation, the DPTB algorithm generates and uses a 3D marker model to create 2D templates at any projection angle. The 2D templates are used to segment the marker position as the position with highest normalized cross-correlation in a search area centered at the DP segmented position. The accuracy of the DP...... algorithm and the new DPTB algorithm was quantified as the 2D segmentation error (pixels) compared to a manual ground truth segmentation for 97 markers in the projection images of CBCT scans of 40 patients. Also the fraction of wrong segmentations, defined as 2D errors larger than 5 pixels, was calculated...

Active contour segmentation in dynamic medical imaging: application to nuclear cardiology

International Nuclear Information System (INIS)

Debreuve, Eric

2000-01-01

In emission imaging, nuclear medicine provides functional information about the organ of interest. In transmission imaging, it provides anatomical information whose goal may be the correction of physical phenomena that corrupt emission images. With both emission and transmission images, it is useful to know how to extract, either automatically or semi-automatically, the organs of interest and the body outline in the case of a large field of view. This is the aim of segmentation. We developed two active contour segmentation methods. They were implemented using level sets. The key point is the evolution velocity definition. First, we were interested in static transmission imaging of the thorax. The evolution velocity was heuristically defined and depended only on the acquired projections. The segmented transmission map was computed w/o reconstruction and could be advantageously used for attenuation correction. Then, we studied the segmentation of cardiac gated sequences. The developed space-time segmentation method results from the minimization of a variational criterion which takes into account the whole sequence. The computed segmentation could be used for calculating physiological parameters. As an illustration, we computed the ejection fraction. Finally, we exploited some level set properties to develop a non-rigid, non-parametric, and geometric registration method. We applied it for kinetic compensation of cardiac gated sequences. The registered images were then added together providing an image with noise characteristics similar to a cardiac static image but w/o motion-induced blurring. (author)

Hemorrhage Detection and Segmentation in Traumatic Pelvic Injuries

Science.gov (United States)

Davuluri, Pavani; Wu, Jie; Tang, Yang; Cockrell, Charles H.; Ward, Kevin R.; Najarian, Kayvan; Hargraves, Rosalyn H.

2012-01-01

Automated hemorrhage detection and segmentation in traumatic pelvic injuries is vital for fast and accurate treatment decision making. Hemorrhage is the main cause of deaths in patients within first 24 hours after the injury. It is very time consuming for physicians to analyze all Computed Tomography (CT) images manually. As time is crucial in emergence medicine, analyzing medical images manually delays the decision-making process. Automated hemorrhage detection and segmentation can significantly help physicians to analyze these images and make fast and accurate decisions. Hemorrhage segmentation is a crucial step in the accurate diagnosis and treatment decision-making process. This paper presents a novel rule-based hemorrhage segmentation technique that utilizes pelvic anatomical information to segment hemorrhage accurately. An evaluation measure is used to quantify the accuracy of hemorrhage segmentation. The results show that the proposed method is able to segment hemorrhage very well, and the results are promising. PMID:22919433

CT angiography using electron-beam computed tomography (EBCT). A phantom study

International Nuclear Information System (INIS)

Uchino, Akira; Kato, Akira; Kudo, Sho

1997-01-01

The purpose of this study was to evaluate the accuracy of CT angiography in small vessels using electron-beam computed tomography (EBCT). Vessel phantoms with inner diameters of 8 mm, 6 mm, and 4 mm were prepared with segments of 75%, 50%, and 25% stenosis in each vessel. The vessels were filled with contrast medium (Iopamidol 300 at 1/24 dilution, approximately 380 HU). The EBCT apparatus used was an Imatron C-150. The step volume scan mode was used with slice thicknesses of 1.5 mm and 3.0 mm, scan time of 0.3 sec, and 210 mm field of view. Images with a slice thickness of 1.5 mm were definitely better than those with a slice thickness of 3.0 mm. The quality of maximum intensity projection (MIP) images was quite similar to that of three-dimensional (3D) images. Using the 8 mm vessel phantom, all stenotic segments were accurately visualized on CT angiography. The 50% stenotic segments were accurately estimated in all vessels. However, the 75% stenotic segments were slightly overestimated in smaller vessels, and the 25% stenotic segments were slightly underestimated in smaller vessels. We consider CT angiography using EBCT to be a useful, less invasive diagnostic modality for stenoocclusive lesions. (author)

Upper airway segmentation and dimensions estimation from cone-beam CT image datasets

Energy Technology Data Exchange (ETDEWEB)

Shi, Hongjian; Scarfe, W.C. [Louisville Univ., KY (United States). School of Dentistry; Farman, A.G. [Louisville Univ., KY (United States). School of Dentistry; Louisville Univ., KY (United States). Div. of Radiology and Imaging Science

2006-11-15

Objective: To segment and measure the upper airway using cone-beam computed tomography (CBCT). This information may be useful as an imaging biomarker in the diagnostic assessment of patients with obstructive sleep apnea and in the planning of any necessary therapy. Methods: With Institutional Review Board Approval, anonymous CBCT datasets from subjects who had been imaged for a variety of conditions unrelated to the airway were evaluated. DICOM images were available. A segmentation algorithm was developed to separate the bounded upper airway and measurements were performed manually to determine the smallest cross-sectional area and the anteriorposterior distance of the retropalatal space (RP-SCA and RP-AP, respectively) and retroglossal space (RG-SCA and RG-AP, respectively). A segmentation algorithm was developed to separate the bounded upper airway and it was applied to determine RP-AP, RG-AP, the smallest transaxial-sectional area (TSCA) and largest sagittal view airway area (LCSA). A second algorithm was created to evaluate the airway volume within this bounded upper airway. Results: Measurements of the airway segmented automatically by the developed algorithm agreed with those obtained using manual segmentation. The corresponding volumes showed only very small differences considered clinically insignificant. Conclusion: Automatic segmentation of the airway imaged using CBCT is feasible and this method can be used to evaluate airway cross-section and volume comparable to measurements extracted using manual segmentation. (orig.)

Nearest neighbor 3D segmentation with context features

Science.gov (United States)

Hristova, Evelin; Schulz, Heinrich; Brosch, Tom; Heinrich, Mattias P.; Nickisch, Hannes

2018-03-01

Automated and fast multi-label segmentation of medical images is challenging and clinically important. This paper builds upon a supervised machine learning framework that uses training data sets with dense organ annotations and vantage point trees to classify voxels in unseen images based on similarity of binary feature vectors extracted from the data. Without explicit model knowledge, the algorithm is applicable to different modalities and organs, and achieves high accuracy. The method is successfully tested on 70 abdominal CT and 42 pelvic MR images. With respect to ground truth, an average Dice overlap score of 0.76 for the CT segmentation of liver, spleen and kidneys is achieved. The mean score for the MR delineation of bladder, bones, prostate and rectum is 0.65. Additionally, we benchmark several variations of the main components of the method and reduce the computation time by up to 47% without significant loss of accuracy. The segmentation results are - for a nearest neighbor method - surprisingly accurate, robust as well as data and time efficient.

PET attenuation coefficients from CT images: experimental evaluation of the transformation of CT into PET 511-keV attenuation coefficients.

Science.gov (United States)

Burger, C; Goerres, G; Schoenes, S; Buck, A; Lonn, A H R; Von Schulthess, G K

2002-07-01

The CT data acquired in combined PET/CT studies provide a fast and essentially noiseless source for the correction of photon attenuation in PET emission data. To this end, the CT values relating to attenuation of photons in the range of 40-140 keV must be transformed into linear attenuation coefficients at the PET energy of 511 keV. As attenuation depends on photon energy and the absorbing material, an accurate theoretical relation cannot be devised. The transformation implemented in the Discovery LS PET/CT scanner (GE Medical Systems, Milwaukee, Wis.) uses a bilinear function based on the attenuation of water and cortical bone at the CT and PET energies. The purpose of this study was to compare this transformation with experimental CT values and corresponding PET attenuation coefficients. In 14 patients, quantitative PET attenuation maps were calculated from germanium-68 transmission scans, and resolution-matched CT images were generated. A total of 114 volumes of interest were defined and the average PET attenuation coefficients and CT values measured. From the CT values the predicted PET attenuation coefficients were calculated using the bilinear transformation. When the transformation was based on the narrow-beam attenuation coefficient of water at 511 keV (0.096 cm(-1)), the predicted attenuation coefficients were higher in soft tissue than the measured values. This bias was reduced by replacing 0.096 cm(-1) in the transformation by the linear attenuation coefficient of 0.093 cm(-1) obtained from germanium-68 transmission scans. An analysis of the corrected emission activities shows that the resulting transformation is essentially equivalent to the transmission-based attenuation correction for human tissue. For non-human material, however, it may assign inaccurate attenuation coefficients which will also affect the correction in neighbouring tissue.

Magnetic Resonance–Based Automatic Air Segmentation for Generation of Synthetic Computed Tomography Scans in the Head Region

Energy Technology Data Exchange (ETDEWEB)

Zheng, Weili; Kim, Joshua P. [Department of Radiation Oncology, Henry Ford Health Systems, Detroit, Michigan (United States); Kadbi, Mo [Philips Healthcare, Cleveland, Ohio (United States); Movsas, Benjamin; Chetty, Indrin J. [Department of Radiation Oncology, Henry Ford Health Systems, Detroit, Michigan (United States); Glide-Hurst, Carri K., E-mail: churst2@hfhs.org [Department of Radiation Oncology, Henry Ford Health Systems, Detroit, Michigan (United States)

2015-11-01

Purpose: To incorporate a novel imaging sequence for robust air and tissue segmentation using ultrashort echo time (UTE) phase images and to implement an innovative synthetic CT (synCT) solution as a first step toward MR-only radiation therapy treatment planning for brain cancer. Methods and Materials: Ten brain cancer patients were scanned with a UTE/Dixon sequence and other clinical sequences on a 1.0 T open magnet with simulation capabilities. Bone-enhanced images were generated from a weighted combination of water/fat maps derived from Dixon images and inverted UTE images. Automated air segmentation was performed using unwrapped UTE phase maps. Segmentation accuracy was assessed by calculating segmentation errors (true-positive rate, false-positive rate, and Dice similarity indices using CT simulation (CT-SIM) as ground truth. The synCTs were generated using a voxel-based, weighted summation method incorporating T2, fluid attenuated inversion recovery (FLAIR), UTE1, and bone-enhanced images. Mean absolute error (MAE) characterized Hounsfield unit (HU) differences between synCT and CT-SIM. A dosimetry study was conducted, and differences were quantified using γ-analysis and dose-volume histogram analysis. Results: On average, true-positive rate and false-positive rate for the CT and MR-derived air masks were 80.8% ± 5.5% and 25.7% ± 6.9%, respectively. Dice similarity indices values were 0.78 ± 0.04 (range, 0.70-0.83). Full field of view MAE between synCT and CT-SIM was 147.5 ± 8.3 HU (range, 138.3-166.2 HU), with the largest errors occurring at bone–air interfaces (MAE 422.5 ± 33.4 HU for bone and 294.53 ± 90.56 HU for air). Gamma analysis revealed pass rates of 99.4% ± 0.04%, with acceptable treatment plan quality for the cohort. Conclusions: A hybrid MRI phase/magnitude UTE image processing technique was introduced that significantly improved bone and air contrast in MRI. Segmented air masks and bone-enhanced images were integrated

Magnetic Resonance–Based Automatic Air Segmentation for Generation of Synthetic Computed Tomography Scans in the Head Region

International Nuclear Information System (INIS)

Zheng, Weili; Kim, Joshua P.; Kadbi, Mo; Movsas, Benjamin; Chetty, Indrin J.; Glide-Hurst, Carri K.

2015-01-01

Purpose: To incorporate a novel imaging sequence for robust air and tissue segmentation using ultrashort echo time (UTE) phase images and to implement an innovative synthetic CT (synCT) solution as a first step toward MR-only radiation therapy treatment planning for brain cancer. Methods and Materials: Ten brain cancer patients were scanned with a UTE/Dixon sequence and other clinical sequences on a 1.0 T open magnet with simulation capabilities. Bone-enhanced images were generated from a weighted combination of water/fat maps derived from Dixon images and inverted UTE images. Automated air segmentation was performed using unwrapped UTE phase maps. Segmentation accuracy was assessed by calculating segmentation errors (true-positive rate, false-positive rate, and Dice similarity indices using CT simulation (CT-SIM) as ground truth. The synCTs were generated using a voxel-based, weighted summation method incorporating T2, fluid attenuated inversion recovery (FLAIR), UTE1, and bone-enhanced images. Mean absolute error (MAE) characterized Hounsfield unit (HU) differences between synCT and CT-SIM. A dosimetry study was conducted, and differences were quantified using γ-analysis and dose-volume histogram analysis. Results: On average, true-positive rate and false-positive rate for the CT and MR-derived air masks were 80.8% ± 5.5% and 25.7% ± 6.9%, respectively. Dice similarity indices values were 0.78 ± 0.04 (range, 0.70-0.83). Full field of view MAE between synCT and CT-SIM was 147.5 ± 8.3 HU (range, 138.3-166.2 HU), with the largest errors occurring at bone–air interfaces (MAE 422.5 ± 33.4 HU for bone and 294.53 ± 90.56 HU for air). Gamma analysis revealed pass rates of 99.4% ± 0.04%, with acceptable treatment plan quality for the cohort. Conclusions: A hybrid MRI phase/magnitude UTE image processing technique was introduced that significantly improved bone and air contrast in MRI. Segmented air masks and bone-enhanced images were integrated

Design of free-space optical transmission system in computer tomography equipment

Science.gov (United States)

Liu, Min; Fu, Weiwei; Zhang, Tao

2018-04-01

Traditional computer tomography (CT) based on capacitive coupling cannot satisfy the high data rate transmission requirement. We design and experimentally demonstrate a free-space optical transmission system for CT equipment at a data rate of 10 Gb / s. Two interchangeable sections of 12 pieces of fiber with equal length is fabricated and tested by our designed laser phase distance measurement system. By locating the 12 collimators in the edge of the circle wheel evenly, the optical propagation characteristics for the 12 wired and wireless paths are similar, which can satisfy the requirement of high-speed CT transmission system. After bit error rate (BER) measurement in several conditions, the BER performances are below the value of 10 - 11, which has the potential in the future application scenario of CT equipment.

Frameless image registration of X-ray CT and SPECT by volume matching

International Nuclear Information System (INIS)

Tanaka, Yuko; Kihara, Tomohiko; Yui, Nobuharu; Kinoshita, Fujimi; Kamimura, Yoshitsugu; Yamada, Yoshifumi.

1998-01-01

Image registration of functional (SPECT) and morphological (X-ray CT/MRI) images is studied in order to improve the accuracy and the quantity of the image diagnosis. We have developed a new frameless registration method of X-ray CT and SPECT image using transmission CT image acquired for absorption correction of SPECT images. This is the automated registration method and calculates the transformation matrix between the two coordinate systems of image data by the optimization method. This registration method is based on the similar physical property of X-ray CT and transmission CT image. The three-dimensional overlap of the bone region is used for image matching. We verified by a phantom test that it can provide a good result of within two millimeters error. We also evaluated visually the accuracy of the registration method by the application study of SPECT, X-ray CT, and transmission CT head images. This method can be carried out accurately without any frames. We expect this registration method becomes an efficient tool to improve image diagnosis and medical treatment. (author)

First performance evaluation of software for automatic segmentation, labeling and reformation of anatomical aligned axial images of the thoracolumbar spine at CT

Energy Technology Data Exchange (ETDEWEB)

Scholtz, Jan-Erik, E-mail: janerikscholtz@gmail.com; Wichmann, Julian L.; Kaup, Moritz; Fischer, Sebastian; Kerl, J. Matthias; Lehnert, Thomas; Vogl, Thomas J.; Bauer, Ralf W.

2015-03-15

Highlights: •Automatic segmentation and labeling of the thoracolumbar spine. •Automatically generated double-angulated and aligned axial images of spine segments. •High grade of accurateness for the symmetric depiction of anatomical structures. •Time-saving and may improve workflow in daily practice. -- Abstract: Objectives: To evaluate software for automatic segmentation, labeling and reformation of anatomical aligned axial images of the thoracolumbar spine on CT in terms of accuracy, potential for time savings and workflow improvement. Material and methods: 77 patients (28 women, 49 men, mean age 65.3 ± 14.4 years) with known or suspected spinal disorders (degenerative spine disease n = 32; disc herniation n = 36; traumatic vertebral fractures n = 9) underwent 64-slice MDCT with thin-slab reconstruction. Time for automatic labeling of the thoracolumbar spine and reconstruction of double-angulated axial images of the pathological vertebrae was compared with manually performed reconstruction of anatomical aligned axial images. Reformatted images of both reconstruction methods were assessed by two observers regarding accuracy of symmetric depiction of anatomical structures. Results: In 33 cases double-angulated axial images were created in 1 vertebra, in 28 cases in 2 vertebrae and in 16 cases in 3 vertebrae. Correct automatic labeling was achieved in 72 of 77 patients (93.5%). Errors could be manually corrected in 4 cases. Automatic labeling required 1 min in average. In cases where anatomical aligned axial images of 1 vertebra were created, reconstructions made by hand were significantly faster (p < 0.05). Automatic reconstruction was time-saving in cases of 2 and more vertebrae (p < 0.05). Both reconstruction methods revealed good image quality with excellent inter-observer agreement. Conclusion: The evaluated software for automatic labeling and anatomically aligned, double-angulated axial image reconstruction of the thoracolumbar spine on CT is time

Malignant pleural mesothelioma segmentation for photodynamic therapy planning.

Science.gov (United States)

Brahim, Wael; Mestiri, Makram; Betrouni, Nacim; Hamrouni, Kamel

2018-04-01

Medical imaging modalities such as computed tomography (CT) combined with computer-aided diagnostic processing have already become important part of clinical routine specially for pleural diseases. The segmentation of the thoracic cavity represents an extremely important task in medical imaging for different reasons. Multiple features can be extracted by analyzing the thoracic cavity space and these features are signs of pleural diseases including the malignant pleural mesothelioma (MPM) which is the main focus of our research. This paper presents a method that detects the MPM in the thoracic cavity and plans the photodynamic therapy in the preoperative phase. This is achieved by using a texture analysis of the MPM region combined with a thoracic cavity segmentation method. The algorithm to segment the thoracic cavity consists of multiple stages. First, the rib cage structure is segmented using various image processing techniques. We used the segmented rib cage to detect feature points which represent the thoracic cavity boundaries. Next, the proposed method segments the structures of the inner thoracic cage and fits 2D closed curves to the detected pleural cavity features in each slice. The missing bone structures are interpolated using a prior knowledge from manual segmentation performed by an expert. Next, the tumor region is segmented inside the thoracic cavity using a texture analysis approach. Finally, the contact surface between the tumor region and the thoracic cavity curves is reconstructed in order to plan the photodynamic therapy. Using the adjusted output of the thoracic cavity segmentation method and the MPM segmentation method, we evaluated the contact surface generated from these two steps by comparing it to the ground truth. For this evaluation, we used 10 CT scans with pathologically confirmed MPM at stages 1 and 2. We obtained a high similarity rate between the manually planned surface and our proposed method. The average value of Jaccard index

CT findings in recurrent pyogenic cholangitis

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Jung, Seung Hye; Lim, Jae Hoon; Ko, Young Tae; Lee, Dong Ho

1991-01-01

Recurrent pyogenic cholangitis is characterized clinically by recurrent attacks of right upper abdominal pain, fever and jaundice, and pathologically by chronic inflammation of the bile ducts with or without pigment bile duct stones. We analyzed the CT findings of 33 cases with recurrent pyogenic cholangitis. Twenty-four cases were confirmed by operation, and 9 cases were diagnosed clinically and cholangiographically. The CT findings of recurrent pyogenic cholangitis were dilatation of the intrahepatic ducts (n = 30), dilatation of the extrahepatic ducts (n = 24) intrahepatic stones (n = 16), extrahepatic stones (n = 12), stricture of the bile ducts (n = 10), wall enhancement of the bile ducts (n = 8), gallstones (n = 8), segmental atrophy of the liver (n = 7), pneumobilia (n = 4), abscess (n = 3), and segmental enhancement (n = 1) of the liver. A CT is considered helpful when sectional imaging is needed, but sonographic findings are equivocal or not confirmative; space-occupying lesions complicated with recurrent pyogenic cholangitis: hepatic resection is planned; and imaging guidance is needed for complex drainage procedures

Deformable segmentation via sparse shape representation.

Science.gov (United States)

Zhang, Shaoting; Zhan, Yiqiang; Dewan, Maneesh; Huang, Junzhou; Metaxas, Dimitris N; Zhou, Xiang Sean

2011-01-01

Appearance and shape are two key elements exploited in medical image segmentation. However, in some medical image analysis tasks, appearance cues are weak/misleading due to disease/artifacts and often lead to erroneous segmentation. In this paper, a novel deformable model is proposed for robust segmentation in the presence of weak/misleading appearance cues. Owing to the less trustable appearance information, this method focuses on the effective shape modeling with two contributions. First, a shape composition method is designed to incorporate shape prior on-the-fly. Based on two sparsity observations, this method is robust to false appearance information and adaptive to statistically insignificant shape modes. Second, shape priors are modeled and used in a hierarchical fashion. More specifically, by using affinity propagation method, our deformable surface is divided into multiple partitions, on which local shape models are built independently. This scheme facilitates a more compact shape prior modeling and hence a more robust and efficient segmentation. Our deformable model is applied on two very diverse segmentation problems, liver segmentation in PET-CT images and rodent brain segmentation in MR images. Compared to state-of-art methods, our method achieves better performance in both studies.

Adaptive Breast Radiation Therapy Using Modeling of Tissue Mechanics: A Breast Tissue Segmentation Study

International Nuclear Information System (INIS)

Juneja, Prabhjot; Harris, Emma J.; Kirby, Anna M.; Evans, Philip M.

2012-01-01

Purpose: To validate and compare the accuracy of breast tissue segmentation methods applied to computed tomography (CT) scans used for radiation therapy planning and to study the effect of tissue distribution on the segmentation accuracy for the purpose of developing models for use in adaptive breast radiation therapy. Methods and Materials: Twenty-four patients receiving postlumpectomy radiation therapy for breast cancer underwent CT imaging in prone and supine positions. The whole-breast clinical target volume was outlined. Clinical target volumes were segmented into fibroglandular and fatty tissue using the following algorithms: physical density thresholding; interactive thresholding; fuzzy c-means with 3 classes (FCM3) and 4 classes (FCM4); and k-means. The segmentation algorithms were evaluated in 2 stages: first, an approach based on the assumption that the breast composition should be the same in both prone and supine position; and second, comparison of segmentation with tissue outlines from 3 experts using the Dice similarity coefficient (DSC). Breast datasets were grouped into nonsparse and sparse fibroglandular tissue distributions according to expert assessment and used to assess the accuracy of the segmentation methods and the agreement between experts. Results: Prone and supine breast composition analysis showed differences between the methods. Validation against expert outlines found significant differences (P<.001) between FCM3 and FCM4. Fuzzy c-means with 3 classes generated segmentation results (mean DSC = 0.70) closest to the experts' outlines. There was good agreement (mean DSC = 0.85) among experts for breast tissue outlining. Segmentation accuracy and expert agreement was significantly higher (P<.005) in the nonsparse group than in the sparse group. Conclusions: The FCM3 gave the most accurate segmentation of breast tissues on CT data and could therefore be used in adaptive radiation therapy-based on tissue modeling. Breast tissue segmentation

Adaptive Breast Radiation Therapy Using Modeling of Tissue Mechanics: A Breast Tissue Segmentation Study

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Juneja, Prabhjot, E-mail: Prabhjot.Juneja@icr.ac.uk [Joint Department of Physics, Institute of Cancer Research, Sutton (United Kingdom); Harris, Emma J. [Joint Department of Physics, Institute of Cancer Research, Sutton (United Kingdom); Kirby, Anna M. [Department of Academic Radiotherapy, Royal Marsden National Health Service Foundation Trust, Sutton (United Kingdom); Evans, Philip M. [Joint Department of Physics, Institute of Cancer Research, Sutton (United Kingdom)

2012-11-01

Purpose: To validate and compare the accuracy of breast tissue segmentation methods applied to computed tomography (CT) scans used for radiation therapy planning and to study the effect of tissue distribution on the segmentation accuracy for the purpose of developing models for use in adaptive breast radiation therapy. Methods and Materials: Twenty-four patients receiving postlumpectomy radiation therapy for breast cancer underwent CT imaging in prone and supine positions. The whole-breast clinical target volume was outlined. Clinical target volumes were segmented into fibroglandular and fatty tissue using the following algorithms: physical density thresholding; interactive thresholding; fuzzy c-means with 3 classes (FCM3) and 4 classes (FCM4); and k-means. The segmentation algorithms were evaluated in 2 stages: first, an approach based on the assumption that the breast composition should be the same in both prone and supine position; and second, comparison of segmentation with tissue outlines from 3 experts using the Dice similarity coefficient (DSC). Breast datasets were grouped into nonsparse and sparse fibroglandular tissue distributions according to expert assessment and used to assess the accuracy of the segmentation methods and the agreement between experts. Results: Prone and supine breast composition analysis showed differences between the methods. Validation against expert outlines found significant differences (P<.001) between FCM3 and FCM4. Fuzzy c-means with 3 classes generated segmentation results (mean DSC = 0.70) closest to the experts' outlines. There was good agreement (mean DSC = 0.85) among experts for breast tissue outlining. Segmentation accuracy and expert agreement was significantly higher (P<.005) in the nonsparse group than in the sparse group. Conclusions: The FCM3 gave the most accurate segmentation of breast tissues on CT data and could therefore be used in adaptive radiation therapy-based on tissue modeling. Breast tissue

The interpolation method based on endpoint coordinate for CT three-dimensional image

International Nuclear Information System (INIS)

Suto, Yasuzo; Ueno, Shigeru.

1997-01-01

Image interpolation is frequently used to improve slice resolution to reach spatial resolution. Improved quality of reconstructed three-dimensional images can be attained with this technique as a result. Linear interpolation is a well-known and widely used method. The distance-image method, which is a non-linear interpolation technique, is also used to convert CT value images to distance images. This paper describes a newly developed method that makes use of end-point coordinates: CT-value images are initially converted to binary images by thresholding them and then sequences of pixels with 1-value are arranged in vertical or horizontal directions. A sequence of pixels with 1-value is defined as a line segment which has starting and end points. For each pair of adjacent line segments, another line segment was composed by spatial interpolation of the start and end points. Binary slice images are constructed from the composed line segments. Three-dimensional images were reconstructed from clinical X-ray CT images, using three different interpolation methods and their quality and processing speed were evaluated and compared. (author)

Analysis of different power grid segmentation and transmission schemes for power system security improvement

International Nuclear Information System (INIS)

Shami, U.T.; Chaudhary, M.S.

2015-01-01

This paper explores the power grid segmentation concept for power system stability improvement in detail. First, the firewall property of grid segmentation is investigated for a two area network. Then two HVDC technologies, LCC and VSC, are compared for the same network. A two area VSC-AC segmented network is then compared with two area VSC segmented network. Suitable segmentation topology and suitable number of VSC segmented areas are then investigated. Simulation results show that grid segmentation offers network stability during fault conditions and VSC is the most suitable choice for segmentation over LCC. Results further show that having large number of DC segmented areas and using the radial segmentation topology improves the stability of the overall system. All the simulations were carried out in PSS at the rate E software provided by SIEMENS discussed. Section IV discusses the test systems under study in this research. Section V compares and analyzes the simulation results. Section VI contains the conclusion. (author)

Automated segmentation of synchrotron radiation micro-computed tomography biomedical images using Graph Cuts and neural networks

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Alvarenga de Moura Meneses, Anderson, E-mail: ameneses@ieee.org [Radiological Sciences Laboratory, Rio de Janeiro State University, Rua Sao Francisco Xavier 524, CEP 20550-900, RJ (Brazil); Giusti, Alessandro [IDSIA (Dalle Molle Institute for Artificial Intelligence), University of Lugano (Switzerland); Pereira de Almeida, Andre; Parreira Nogueira, Liebert; Braz, Delson [Nuclear Engineering Program, Federal University of Rio de Janeiro, RJ (Brazil); Cely Barroso, Regina [Laboratory of Applied Physics on Biomedical Sciences, Physics Department, Rio de Janeiro State University, RJ (Brazil); Almeida, Carlos Eduardo de [Radiological Sciences Laboratory, Rio de Janeiro State University, Rua Sao Francisco Xavier 524, CEP 20550-900, RJ (Brazil)

2011-12-21

Synchrotron Radiation (SR) X-ray micro-Computed Tomography ({mu}CT) enables magnified images to be used as a non-invasive and non-destructive technique with a high space resolution for the qualitative and quantitative analyses of biomedical samples. The research on applications of segmentation algorithms to SR-{mu}CT is an open problem, due to the interesting and well-known characteristics of SR images for visualization, such as the high resolution and the phase contrast effect. In this article, we describe and assess the application of the Energy Minimization via Graph Cuts (EMvGC) algorithm for the segmentation of SR-{mu}CT biomedical images acquired at the Synchrotron Radiation for MEdical Physics (SYRMEP) beam line at the Elettra Laboratory (Trieste, Italy). We also propose a method using EMvGC with Artificial Neural Networks (EMANNs) for correcting misclassifications due to intensity variation of phase contrast, which are important effects and sometimes indispensable in certain biomedical applications, although they impair the segmentation provided by conventional techniques. Results demonstrate considerable success in the segmentation of SR-{mu}CT biomedical images, with average Dice Similarity Coefficient 99.88% for bony tissue in Wistar Rats rib samples (EMvGC), as well as 98.95% and 98.02% for scans of Rhodnius prolixus insect samples (Chagas's disease vector) with EMANNs, in relation to manual segmentation. The techniques EMvGC and EMANNs cope with the task of performing segmentation in images with the intensity variation due to phase contrast effects, presenting a superior performance in comparison to conventional segmentation techniques based on thresholding and linear/nonlinear image filtering, which is also discussed in the present article.

Automated segmentation of synchrotron radiation micro-computed tomography biomedical images using Graph Cuts and neural networks

International Nuclear Information System (INIS)

Alvarenga de Moura Meneses, Anderson; Giusti, Alessandro; Pereira de Almeida, André; Parreira Nogueira, Liebert; Braz, Delson; Cely Barroso, Regina; Almeida, Carlos Eduardo de

2011-01-01

Synchrotron Radiation (SR) X-ray micro-Computed Tomography (μCT) enables magnified images to be used as a non-invasive and non-destructive technique with a high space resolution for the qualitative and quantitative analyses of biomedical samples. The research on applications of segmentation algorithms to SR-μCT is an open problem, due to the interesting and well-known characteristics of SR images for visualization, such as the high resolution and the phase contrast effect. In this article, we describe and assess the application of the Energy Minimization via Graph Cuts (EMvGC) algorithm for the segmentation of SR-μCT biomedical images acquired at the Synchrotron Radiation for MEdical Physics (SYRMEP) beam line at the Elettra Laboratory (Trieste, Italy). We also propose a method using EMvGC with Artificial Neural Networks (EMANNs) for correcting misclassifications due to intensity variation of phase contrast, which are important effects and sometimes indispensable in certain biomedical applications, although they impair the segmentation provided by conventional techniques. Results demonstrate considerable success in the segmentation of SR-μCT biomedical images, with average Dice Similarity Coefficient 99.88% for bony tissue in Wistar Rats rib samples (EMvGC), as well as 98.95% and 98.02% for scans of Rhodnius prolixus insect samples (Chagas's disease vector) with EMANNs, in relation to manual segmentation. The techniques EMvGC and EMANNs cope with the task of performing segmentation in images with the intensity variation due to phase contrast effects, presenting a superior performance in comparison to conventional segmentation techniques based on thresholding and linear/nonlinear image filtering, which is also discussed in the present article.

Iterative CT shading correction with no prior information

Science.gov (United States)

Wu, Pengwei; Sun, Xiaonan; Hu, Hongjie; Mao, Tingyu; Zhao, Wei; Sheng, Ke; Cheung, Alice A.; Niu, Tianye

2015-11-01

Shading artifacts in CT images are caused by scatter contamination, beam-hardening effect and other non-ideal imaging conditions. The purpose of this study is to propose a novel and general correction framework to eliminate low-frequency shading artifacts in CT images (e.g. cone-beam CT, low-kVp CT) without relying on prior information. The method is based on the general knowledge of the relatively uniform CT number distribution in one tissue component. The CT image is first segmented to construct a template image where each structure is filled with the same CT number of a specific tissue type. Then, by subtracting the ideal template from the CT image, the residual image from various error sources are generated. Since forward projection is an integration process, non-continuous shading artifacts in the image become continuous signals in a line integral. Thus, the residual image is forward projected and its line integral is low-pass filtered in order to estimate the error that causes shading artifacts. A compensation map is reconstructed from the filtered line integral error using a standard FDK algorithm and added back to the original image for shading correction. As the segmented image does not accurately depict a shaded CT image, the proposed scheme is iterated until the variation of the residual image is minimized. The proposed method is evaluated using cone-beam CT images of a Catphan©600 phantom and a pelvis patient, and low-kVp CT angiography images for carotid artery assessment. Compared with the CT image without correction, the proposed method reduces the overall CT number error from over 200 HU to be less than 30 HU and increases the spatial uniformity by a factor of 1.5. Low-contrast object is faithfully retained after the proposed correction. An effective iterative algorithm for shading correction in CT imaging is proposed that is only assisted by general anatomical information without relying on prior knowledge. The proposed method is thus practical

CT and angiographic appearances of hepatocellular carcinoma partially fed by right inferior phrenic artery

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Ohtomo, Kuni; Furui, Shigeru; Yoshikawa, Hiroki; Yashiro, Naofumi; Araki, Tsutomu [Tokyo Univ. (Japan). Faculty of Medicine

1983-04-01

CT and angiographic appearances of 8 hepatocellular carcinomas which were partially fed by right inferior phrenic artery were discussed. CT demonstrated tumor fully occupied posterior segment of right hepatic lobe in 6 cases which were composed of 3 solitary massive, 2 massive nodular and 1 confluent massive angiographically. In the other 2 cases, CT showed encapsulated tumor in posterior inferior portion of posterior segment protruded from the liver. In 6 out of these 8 cases, tumor vessels and tumor stain were chiefly derived from posterior branch of right inferior phrenic artery.

CT and angiographic appearances of hepatocellular carcinoma partially feeded by right inferior phrenic artery

International Nuclear Information System (INIS)

Ohtomo, Kuni; Furui, Shigeru; Yoshikawa, Hiroki; Yashiro, Naofumi; Araki, Tsutomu

1983-01-01

CT and angiographic appearances of 8 hepatocellular carcinomas which were partially feeded by right inferior phrenic artery were discussed. CT demonstrated tumor fully occupied posterior segment of right hepatic lobe in 6 cases which were composed of 3 solitary massive, 2 massive nodular and 1 confluent massive angiographically. In the other 2 cases, CT showed encapsulated tumor in posterior inferior portion of posterior segment protruded from the liver. In 6 out of these 8 cases, tumor vessels and tumor stain were chiefly derived from posterior branch of right inferior phrenic artery. (author)

Comparing 511 keV Attenuation Maps Obtained from Different Energy Mapping Methods for CT Based Attenuation Correction of PET Data

Directory of Open Access Journals (Sweden)

Maryam Shirmohammad

2008-06-01

Full Text Available Introduction:  The  advent  of  dual-modality  PET/CT  scanners  has  revolutionized  clinical  oncology  by  improving lesion localization and facilitating treatment planning for radiotherapy. In addition, the use of  CT images for CT-based attenuation correction (CTAC decreases the overall scanning time and creates  a noise-free  attenuation  map  (6map.  CTAC  methods  include  scaling,  segmentation,  hybrid  scaling/segmentation, bilinear and dual energy methods. All CTAC methods require the transformation  of CT Hounsfield units (HU to linear attenuation coefficients (LAC at 511 keV. The aim of this study is  to compare the results of implementing different methods of energy mapping in PET/CT scanners.   Materials and Methods: This study was conducted in 2 phases, the first phase in a phantom and the  second  one  on  patient  data.  To  perform  the  first  phase,  a  cylindrical  phantom  with  different  concentrations of K2HPO4 inserts was CT scanned and energy mapping methods were implemented on  it. For performing the second phase, different energy  mapping  methods  were implemented on several  clinical studies and compared to the transmission (TX image derived using Ga-68 radionuclide source  acquired on the GE Discovery LS PET/CT scanner.   Results: An ROI analysis was performed on different positions of the resultant 6maps and the average  6value of each ROI was compared to the reference value. The results of the 6maps obtained for 511 keV  compared to the theoretical  values showed that in the phantom for low  concentrations  of K 2 HPO 4 all  these  methods  produce  511  keV  attenuation  maps  with  small  relative  difference  compared  to  gold  standard. The relative difference for scaling, segmentation, hybrid, bilinear and dual energy methods was  4.92,  3.21,  4.43,  2.24  and  2.29%,  respectively.  Although  for  high  concentration �

AISLE: an automatic volumetric segmentation method for the study of lung allometry.

Science.gov (United States)

Ren, Hongliang; Kazanzides, Peter

2011-01-01

We developed a fully automatic segmentation method for volumetric CT (computer tomography) datasets to support construction of a statistical atlas for the study of allometric laws of the lung. The proposed segmentation method, AISLE (Automated ITK-Snap based on Level-set), is based on the level-set implementation from an existing semi-automatic segmentation program, ITK-Snap. AISLE can segment the lung field without human interaction and provide intermediate graphical results as desired. The preliminary experimental results show that the proposed method can achieve accurate segmentation, in terms of volumetric overlap metric, by comparing with the ground-truth segmentation performed by a radiologist.

CT colonography: colonic distention improved by dual positioning but not intravenous glucagon

International Nuclear Information System (INIS)

Morrin, Martina M.; Keogan, Mary T.; Kruskal, Jonathan B.; Yam, Chun-Shan; Raptopoulos, Vassilios; Farrell, Richard J.

2002-01-01

The aim of this study was to determine whether intravenous (IV) glucagon and dual positioning administered prior to CT colonography enhances colonic distention. We assessed the effect of dual positioning and IV glucagon on colonic distention in 96 patients who underwent CT colonography examinations. The CT colonography was performed in both supine and prone positions. Seventy-four patients received glucagon (1 mg i.v.) immediately prior to CT scanning and 22 patients did not. The bowel was divided into ten segments and colonic distention was scored by two radiologists in the supine, prone, and combined supine/prone positions using a five-point scale: 1=collapsed; 2=poorly visualized; ≥3=adequate distention; 4=entire segment visualized and well distended; 5=excellent distention. A combined segmental and overall supine/prone distention score was calculated based on the sum of the mean score for each position. There was no significant difference in the degree of colonic distention between patients who received glucagon and those who did not [supine/prone distention score (mean±SE): 3.63±0.2 vs 3.85±0.2; p=n.s.]. The degree of colonic distention was greater in the prone position in both the glucagon (3.87±0.2 vs 3.38±0.2; p<0.05) and non-glucagon groups (4.01±0.2 vs 3.69±0.2; p=N.S.) particularly in the proximal colon. There was almost perfect agreement between both radiologists in their scoring of colonic distention on a per-patient basis (k=0.9; p<0.001). Of 1480 bowel segments, 1261 (85.2%) were adequately distended in the glucagon group compared with 370 of 440 bowel segments (84%) in the non-glucagon group (p=n.s.) Colonic distention at CT colonography is improved by dual positioning but not by the administration of intravenous glucagon. While our results suggest that other smooth muscle relaxants, including butyl scopolamine, may only have a limited role in improving colonic distention in CT colonography, further studies are required. (orig.)

Computed tomography landmark-based semi-automated mesh morphing and mapping techniques: generation of patient specific models of the human pelvis without segmentation.

Science.gov (United States)

Salo, Zoryana; Beek, Maarten; Wright, David; Whyne, Cari Marisa

2015-04-13

Current methods for the development of pelvic finite element (FE) models generally are based upon specimen specific computed tomography (CT) data. This approach has traditionally required segmentation of CT data sets, which is time consuming and necessitates high levels of user intervention due to the complex pelvic anatomy. The purpose of this research was to develop and assess CT landmark-based semi-automated mesh morphing and mapping techniques to aid the generation and mechanical analysis of specimen-specific FE models of the pelvis without the need for segmentation. A specimen-specific pelvic FE model (source) was created using traditional segmentation methods and morphed onto a CT scan of a different (target) pelvis using a landmark-based method. The morphed model was then refined through mesh mapping by moving the nodes to the bone boundary. A second target model was created using traditional segmentation techniques. CT intensity based material properties were assigned to the morphed/mapped model and to the traditionally segmented target models. Models were analyzed to evaluate their geometric concurrency and strain patterns. Strains generated in a double-leg stance configuration were compared to experimental strain gauge data generated from the same target cadaver pelvis. CT landmark-based morphing and mapping techniques were efficiently applied to create a geometrically multifaceted specimen-specific pelvic FE model, which was similar to the traditionally segmented target model and better replicated the experimental strain results (R(2)=0.873). This study has shown that mesh morphing and mapping represents an efficient validated approach for pelvic FE model generation without the need for segmentation. Copyright © 2015 Elsevier Ltd. All rights reserved.

Accuracy of liver lesion assessment using automated measurement and segmentation software in biphasic multislice CT (MSCT)

International Nuclear Information System (INIS)

Puesken, M.; Juergens, K.U.; Edenfeld, A.; Buerke, B.; Seifarth, H.; Beyer, F.; Heindel, W.; Wessling, J.; Suehling, M.; Osada, N.

2009-01-01

Purpose: To assess the accuracy of liver lesion measurement using automated measurement and segmentation software depending on the vascularization level. Materials and Methods: Arterial and portal venous phase multislice CT (MSCT) was performed for 58 patients. 94 liver lesions were evaluated and classified according to vascularity (hypervascular: 13 hepatocellular carcinomas, 20 hemangiomas; hypovascular: 31 metastases, 3 lymphomas, 4 abscesses; liquid: 23 cysts). The RECIST diameter and volume were obtained using automated measurement and segmentation software and compared to corresponding measurements derived visually by two experienced radiologists as a reference standard. Statistical analysis was performed using the Wilcoxon test and concordance correlation coefficients. Results: Automated measurements revealed no significant difference between the arterial and portal venous phase in hypovascular (mean RECIST diameter: 31.4 vs. 30.2 mm; p = 0.65; κ = 0.875) and liquid lesions (20.4 vs. 20.1 mm; p = 0.1; κ = 0.996). The RECIST diameter and volume of hypervascular lesions were significantly underestimated in the portal venous phase as compared to the arterial phase (30.3 vs. 26.9 mm, p = 0.007, κ 0.834; 10.7 vs. 7.9 ml, p = 0.0045, κ = 0.752). Automated measurements for hypovascular and liquid lesions in the arterial and portal venous phase were concordant to the reference standard. Hypervascular lesion measurements were in line with the reference standard for the arterial phase (30.3 vs. 32.2 mm, p 0.66, κ = 0.754), but revealed a significant difference for the portal venous phase (26.9 vs. 32.1 mm; p = 0.041; κ = 0.606). (orig.)

Metal artefact reduction for a dental cone beam CT image using image segmentation and backprojection filters

International Nuclear Information System (INIS)

Mohammadi, Mahdi; Khotanlou, Hassan; Mohammadi, Mohammad

2011-01-01

Full text: Due to low dose delivery and fast scanning, the dental Cone Beam CT (CBCT) is the latest technology being implanted for a range of dental imaging. The presence of metallic objects including amalgam or gold fillings in the mouth produces an intuitive image for human jaws. The feasibility of a fast and accurate approach for metal artefact reduction for dental CBCT is investigated. The current study investigates the metal artefact reduction using image segmentation and modification of several sinigrams. In order to reduce metal effects such as beam hardening, streak artefact and intense noises, the application of several algorithms is evaluated. The proposed method includes three stages: preprocessing, reconstruction and post-processing. In the pre-processing stage, in order to reduce the noise level, several phase and frequency filters were applied. At the second stage, based on the specific sinogram achieved for each segment, spline interpolation and weighting backprojection filters were applied to reconstruct the original image. A three-dimensional filter was then applied on reconstructed images, to improve the image quality. Results showed that compared to other available filters, standard frequency filters have a significant influence in the preprocessing stage (ΔHU = 48 ± 6). In addition, with the streak artefact, the probability of beam hardening artefact increases. t e post-processing stage, the application of three-dimensional filters improves the quality of reconstructed images (See Fig. I). Conclusion The proposed method reduces metal artefacts especially where there are more than one metal implanted in the region of interest.

Automatic path proposal computation for CT-guided percutaneous liver biopsy.

Science.gov (United States)

Helck, A; Schumann, C; Aumann, J; Thierfelder, K; Strobl, F F; Braunagel, M; Niethammer, M; Clevert, D A; Hoffmann, R T; Reiser, M; Sandner, T; Trumm, C

2016-12-01

To evaluate feasibility of automatic software-based path proposals for CT-guided percutaneous biopsies. Thirty-three patients (60 [Formula: see text] 12 years) referred for CT-guided biopsy of focal liver lesions were consecutively included. Pre-interventional CT and dedicated software (FraunhoferMeVis Pathfinder) were used for (semi)automatic segmentation of relevant structures. The software subsequently generated three path proposals in downward quality for CT-guided biopsy. Proposed needle paths were compared with consensus proposal of two experts (comparable, less suitable, not feasible). In case of comparable results, equivalent approach to software-based path proposal was used. Quality of segmentation process was evaluated (Likert scale, 1 [Formula: see text] best, 6 [Formula: see text] worst), and time for processing was registered. All biopsies were performed successfully without complications. In 91 % one of the three automatic path proposals was rated comparable to experts' proposal. None of the first proposals was rated not feasible, and 76 % were rated comparable to the experts' proposal. 7 % automatic path proposals were rated not feasible, all being second choice ([Formula: see text]) or third choice ([Formula: see text]). In 79 %, segmentation at least was good. Average total time for establishing automatic path proposal was 42 [Formula: see text] 9 s. Automatic software-based path proposal for CT-guided liver biopsies in the majority provides path proposals that are easy to establish and comparable to experts' insertion trajectories.

Segmentation Toolbox for Tomographic Image Data

DEFF Research Database (Denmark)

Einarsdottir, Hildur

, techniques to automatically analyze such data becomes ever more important. Most segmentation methods for large datasets, such as CT images, deal with simple thresholding techniques, where intensity values cut offs are predetermined and hard coded. For data where the intensity difference is not sufficient......Motivation: Image acquisition has vastly improved over the past years, introducing techniques such as X-ray computed tomography (CT). CT images provide the means to probe a sample non-invasively to investigate its inner structure. Given the wide usage of this technique and massive data amounts......, and partial volume voxels occur frequently, thresholding methods do not suffice and more advanced methods are required. Contribution: To meet these requirements a toolbox has been developed, combining well known methods within the image analysis field. The toolbox includes cluster-based methods...

Intratemporal and extratemporal facial nerve schwannoma: CT and MRI findings

Energy Technology Data Exchange (ETDEWEB)

Kim, Keum Won [Pohang Medical Center, Pohang (Korea, Republic of); Lee, Ho Kyu; Shin, Ji Hoon; Choi, Choong Gon; Suh, Dae Chul [Asan Medical Center, Ulsan Univ. College of Medicine, Seoul (Korea, Republic of); Cheong, Hae Kwan [Dongguk Univ. College of Medicine, Seoul (Korea, Republic of)

2001-05-01

To analyze the characteristics of CT and MRI findings of facial nerve schwannoma in ten patients. Ten patients with pathologically confirmed facial nerve schwannoma, underwent physical and radilolgic examination. The latter involved MRI in all ten and CT scanning in six. We analyzed the location (epicenter), extent and number of involved segments of tumors, tuumor morphology, and changes in adjacent bony structures. The major symptoms of facial nerve schwannoma were facial nerve paralysis in seven cases and hearing loss in six. Epicenters were detected at the intraparotid portion in five cases, the intracanalicular portion in two, the cisternal portion in one, and the intratemporal portion in two. The segment most frequently involved was the mastoid (n=6), followed by the parotid (n=5), intracanalicular (n=4), cisternal (n=2), the labyrinthine/geniculate ganglion (n=2) and the tympanic segment (n=1). Tumors affected two segments of the facial nerve in eight cases, only one segment in one, and four continuous segments in one. Morphologically, tumors were ice-cream cone shaped in the cisternal segment tumor (1/1), cone shaped in intracanalicular tumors (2/2), oval shaped in geniculate ganglion tumors (1/1), club shaped in intraparotid tumors (5/5) and bead shaped in the diffuse-type tumor (1/1). Changes in adjacent bony structures involved widening of the stylomastoid foramen in intraparotid tumors (5/5), widening of the internal auditary canal in intracanalicular and cisternal tumors (3/3), bony erosion of the geniculate fossa in geniculate ganglion tumors (2/2), and widening of the facial nerve canal in intratemporal and intraparotid tumors (6/6). The characteristic location, shape and change in adjacent bony structures revealed by facial schwannomas on CT and MR examination lead to correct diagnosis.

Intratemporal and extratemporal facial nerve schwannoma: CT and MRI findings

International Nuclear Information System (INIS)

Kim, Keum Won; Lee, Ho Kyu; Shin, Ji Hoon; Choi, Choong Gon; Suh, Dae Chul; Cheong, Hae Kwan

2001-01-01

To analyze the characteristics of CT and MRI findings of facial nerve schwannoma in ten patients. Ten patients with pathologically confirmed facial nerve schwannoma, underwent physical and radilolgic examination. The latter involved MRI in all ten and CT scanning in six. We analyzed the location (epicenter), extent and number of involved segments of tumors, tuumor morphology, and changes in adjacent bony structures. The major symptoms of facial nerve schwannoma were facial nerve paralysis in seven cases and hearing loss in six. Epicenters were detected at the intraparotid portion in five cases, the intracanalicular portion in two, the cisternal portion in one, and the intratemporal portion in two. The segment most frequently involved was the mastoid (n=6), followed by the parotid (n=5), intracanalicular (n=4), cisternal (n=2), the labyrinthine/geniculate ganglion (n=2) and the tympanic segment (n=1). Tumors affected two segments of the facial nerve in eight cases, only one segment in one, and four continuous segments in one. Morphologically, tumors were ice-cream cone shaped in the cisternal segment tumor (1/1), cone shaped in intracanalicular tumors (2/2), oval shaped in geniculate ganglion tumors (1/1), club shaped in intraparotid tumors (5/5) and bead shaped in the diffuse-type tumor (1/1). Changes in adjacent bony structures involved widening of the stylomastoid foramen in intraparotid tumors (5/5), widening of the internal auditary canal in intracanalicular and cisternal tumors (3/3), bony erosion of the geniculate fossa in geniculate ganglion tumors (2/2), and widening of the facial nerve canal in intratemporal and intraparotid tumors (6/6). The characteristic location, shape and change in adjacent bony structures revealed by facial schwannomas on CT and MR examination lead to correct diagnosis

Spiral CT colonography in inflammatory bowel disease

International Nuclear Information System (INIS)

Tarjan, Zsolt; Zagoni, Tamas; Gyoerke, Tamas; Mester, Adam; Karlinger, Kinga; Mako, Erno K.

2000-01-01

Objective: Most of the studies on virtual colonoscopy are dealing with the role of detecting colorectal polyps or neoplasms. We have undertaken this study to evaluate the value of CT colonography in patients with colonic Crohn's disease. Methods and material: Five patients (three males, two females, 23-51 years, mean age 42 years) with known (4) or suspected (1) Crohn's disease of the colon underwent fiberoptic colonoscopy and CT colonography in the same day or during a 1-week period. The images were evaluated with the so called zoomed axial slice movie technique and in some regions intra- and extraluminal surface shaded and volume rendered images were generated on a separate workstation. The results were compared to those of a colonoscopy. Results: The final diagnosis was Crohn's disease in four patients and colitis ulcerosa in one. Total examination was possible by colonoscopy in two cases, and with CT colonography in all five cases. The wall of those segments severely affected by the disease were depicted by the axial CT scans to be thickened. The thick walled, segments with narrow lumen seen on CT colonography corresponded to the regions where colonoscopy was failed to pass. Air filled sinus tracts, thickening of the wall of the terminal ileum, loss of haustration pseudopolyps and deep ulcers were seen in CT colonography. Three dimensional (3D) endoluminal views demonstrated pseudopolyps similar to endoscopic images None of the colonoscopically reported shallow ulcerations or aphtoid ulcerations or granular mucosal surface were observed on 2- or 3D CT colonographic images. Conclusion: CT colonography by depicting colonic wall thickening seems to be a useful tool in the diagnosis of Crohn's colitis, which could be a single examination depicting the intraluminal, and transmural extent of the disease

Segmented attenuation correction using artificial neural networks in positron tomography

International Nuclear Information System (INIS)

Yu, S.K.; Nahmias, C.

1996-01-01

The measured attenuation correction technique is widely used in cardiac positron tomographic studies. However, the success of this technique is limited because of insufficient counting statistics achievable in practical transmission scan times, and of the scattered radiation in transmission measurement which leads to an underestimation of the attenuation coefficients. In this work, a segmented attenuation correction technique has been developed that uses artificial neural networks. The technique has been validated in phantoms and verified in human studies. The results indicate that attenuation coefficients measured in the segmented transmission image are accurate and reproducible. Activity concentrations measured in the reconstructed emission image can also be recovered accurately using this new technique. The accuracy of the technique is subject independent and insensitive to scatter contamination in the transmission data. This technique has the potential of reducing the transmission scan time, and satisfactory results are obtained if the transmission data contain about 400 000 true counts per plane. It can predict accurately the value of any attenuation coefficient in the range from air to water in a transmission image with or without scatter correction. (author)

Simultaneous emission transmission tomography using technetium-99m for both emission and transmission

International Nuclear Information System (INIS)

Barnden, L.R.; Ong, P.L.; Rowe, C.C.

1997-01-01

This phantom study investigates whether attenuation maps from transmission data degraded by increased noise from subtraction of emission counts can still provide useful attenuation correction in the regular and obese chest. Technetium-99m was used for both emission and transmission on a triple head simultaneous emission transmission tomography (Tc-Tc SETT) system. Fanbeam transmission counts were computed by subtracting emission counts estimated from the two parallel collimator heads. Radioactive decay was used to simulate organ counts from injections of 900 and 400 MBq sestamibi for regular and obese chest sizes. Line source activity was 350 MBq. Control attenuation maps were obtained with no emission activity. Noise control included catering for negative and zero transmission counts, pre-filtering and segmentation of mu maps. Pre-filtering was tried before and after subtraction and before and after setting negative pixels to zero. Mean±SD count/pixel at the heart in anterior transmission projections was typically 33±18 for the regular and 1±7 for the obese chest. For the obese chest, pre-filtering before resetting negative counts best preserved mean mu in soft tissue and lung. Tc-Tc SETT mu mean±SD for the regular chest were 0.144±0.012 and 0.058±0.004 for soft tissue and lung and for the obese chest, 0.152±0.075 and 0.059±0.017. The accuracy of the Tc-Tc SETT bullseye plots for the regular chest was the same as with control map attenuation correction and 3 times better than with no correction. For the obese chest it was as good as with control map correction only if mu map segmentation was applied. Tc-Tc SETT soft tissue and lung mu in 28 patient studies indicated that segmentation is practical for a wide range of chest sizes. Tc-Tc SETT on a triple-head system offers an accurate, inexpensive method of attenuation correction for the majority of chest sizes. (orig.)

Creation of voxel-based models for paediatric dosimetry from automatic segmentation methods

International Nuclear Information System (INIS)

Acosta, O.; Li, R.; Ourselin, S.; Caon, M.

2006-01-01

Full text: The first computational models representing human anatomy were mathematical phantoms, but still far from accurate representations of human body. These models have been used with radiation transport codes (Monte Carlo) to estimate organ doses from radiological procedures. Although new medical imaging techniques have recently allowed the construction of voxel-based models based on the real anatomy, few children models from individual CT or MRI data have been reported [1,3]. For pediatric dosimetry purposes, a large range of voxel models by ages is required since scaling the anatomy from existing models is not sufficiently accurate. The small number of models available arises from the small number of CT or MRI data sets of children available and the long amount of time required to segment the data sets. The existing models have been constructed by manual segmentation slice by slice and using simple thresholding techniques. In medical image segmentation, considerable difficulties appear when applying classical techniques like thresholding or simple edge detection. Until now, any evidence of more accurate or near-automatic methods used in construction of child voxel models exists. We aim to construct a range of pediatric voxel models, integrating automatic or semi-automatic 3D segmentation techniques. In this paper we present the first stage of this work using pediatric CT data.

Segmental dilatation of the ileum in a healthy adolescent

International Nuclear Information System (INIS)

Shah, Akash D.; Kovanlikaya, Arzu; Brill, Paula W.; Beneck, Debra; Spigland, Nitsana

2009-01-01

Segmental intestinal dilatation is a rare entity presenting overwhelmingly in infants and young children with congenital malformations, anemia, or history of gastrointestinal pathology, characterized by a focally distended segment of bowel with abrupt transition points without an obstructing barrier. We present a 16-year-old girl with no significant medical history who presented with bowel obstruction clinically. Segmental dilatation of the ileum was evident on a CT scan and small bowel series. Following surgical resection, pathologic examination of the segment revealed the presence of heterotopic gastric mucosa. The girl's symptoms resolved after surgery. Awareness of the imaging presentation of this entity can inform the evaluation of older children with nonspecific symptoms mimicking bowel obstruction. (orig.)

3-D CT for cardiovascular treatment planning

International Nuclear Information System (INIS)

Wildermuth, S.; Leschka, S.; Duru, F.; Alkadhi, H.

2005-01-01

The recently developed 64-slice CT scanner together with the use of 2-D and 3-D reconstructions can aid the cardiovascular surgeon and interventional radiologist in visualizing exact geometric relationships to plan and execute complex procedures via minimally invasive or standard approaches.Cardiac 64-slice CT considerably benefits from the high temporal and spatial resolution allowing the reliable depiction of small coronary segments. Similarly, abdominal vascular 64-slice CT became possible within short examination times and allowing an optimal arterial contrast bolus exploitation. We demonstrate four representative cardiac and abdominal examples using the new 64-slice CT technology which reveal the impact of the new scanner generation for cardiovascular treatment planning. (orig.)

Using a method based on Potts Model to segment a micro-CT image stack of trabecular bones of femoral region

Energy Technology Data Exchange (ETDEWEB)

Andrade, Pedro H.A. de; Cabral, Manuela O.M., E-mail: andrade.pha@gmail.com [Universidade Federal de Pernambuco (DEN/UFPE), Recife, PE (Brazil). Departamento de Engenharia Nuclear; Vieira, Jose W.; Correia, Filipe L. de B., E-mail: jose.wilson59@uol.com.br [Instituto Federal de Educacao, Ciencia e Tecnologia de Pernambuco (IFPE), Recife, PE (Brazil); Lima, Fernando R. De A., E-mail: falima@cnen.gov.br [Centro Regional de Ciencias Nucleares do Nordeste (CRCN-NE/CNEN-PE), Recife, PE (brazil)

2015-07-01

Exposure Computational Models are composed basically of an anthropomorphic phantom, a Monte Carlo (MC) code, and an algorithm simulator of the radioactive source. Tomographic phantoms are developed from medical images and must be pre-processed and segmented before being coupled to a MC code (which simulates the interaction of radiation with matter). This work presents a methodology used for treatment of micro-CT images stack of a femur, obtained from a 30 year old female skeleton provided by the Imaging Laboratory for Anthropology of the University of Bristol, UK. These images contain resolution of 60 micrometers and from these a block containing only 160 x 60 x 160 pixels of trabecular tissues and bone marrow was cut and saved as ⁎.sgi file (header + ⁎.raw file). The Grupo de Dosimetria Numerica (Recife-PE, Brazil) developed a software named Digital Image Processing (DIP), in which a method for segmentation based on a physical model for particle interaction known as Potts Model (or q-Ising) was implemented. This model analyzes the statistical dependence between sites in a network. In Potts Model, when the values of spin variables at neighboring sites are identical, it is assigned an 'energy of interaction' between them. Otherwise, it is said that the sites do not interact. Making an analogy between network sites and the pixels of a digital image and, moreover, between the spins variables and the intensity of the gray scale, it was possible to apply this model to obtain texture descriptors and segment the image. (author)

Using a method based on Potts Model to segment a micro-CT image stack of trabecular bones of femoral region

International Nuclear Information System (INIS)

Andrade, Pedro H.A. de; Cabral, Manuela O.M.; Lima, Fernando R. De A.

2015-01-01

Exposure Computational Models are composed basically of an anthropomorphic phantom, a Monte Carlo (MC) code, and an algorithm simulator of the radioactive source. Tomographic phantoms are developed from medical images and must be pre-processed and segmented before being coupled to a MC code (which simulates the interaction of radiation with matter). This work presents a methodology used for treatment of micro-CT images stack of a femur, obtained from a 30 year old female skeleton provided by the Imaging Laboratory for Anthropology of the University of Bristol, UK. These images contain resolution of 60 micrometers and from these a block containing only 160 x 60 x 160 pixels of trabecular tissues and bone marrow was cut and saved as ⁎.sgi file (header + ⁎.raw file). The Grupo de Dosimetria Numerica (Recife-PE, Brazil) developed a software named Digital Image Processing (DIP), in which a method for segmentation based on a physical model for particle interaction known as Potts Model (or q-Ising) was implemented. This model analyzes the statistical dependence between sites in a network. In Potts Model, when the values of spin variables at neighboring sites are identical, it is assigned an 'energy of interaction' between them. Otherwise, it is said that the sites do not interact. Making an analogy between network sites and the pixels of a digital image and, moreover, between the spins variables and the intensity of the gray scale, it was possible to apply this model to obtain texture descriptors and segment the image. (author)

Thoracic manifestation of Wegener's granulomatosis: CT findings in 30 patients

International Nuclear Information System (INIS)

Lee, Kyung Soo; Kim, Tae Sung; Kim, Eun A.; Fujimoto, Kiminori; Moriya, Hiroshi; Watanabe, Hideyuki; Tateishi, Ukihide; Ashizawa, Kazuoto; Johkoh, Takeshi; Kwon, O. Jung

2003-01-01

Our objective was to describe the CT findings of thoracic Wegener's granulomatosis. At presentation, both conventional and thin-section CT scans were available in 30 patients with Wegener's granulomatosis. Serial CT scans (range of intervals: 1-25 months, mean 4.5 months) were available in 20 patients. The initial and follow-up CT scans were analyzed retrospectively by two observers in terms of pattern and extent of parenchymal and airway lesions. Positive CT findings were seen in 29 of 30 (97%) patients at initial presentation. The most common pattern was nodules or masses seen in 27 of 30 (90%) patients. They were multiple in 23 of 27 (85%) patients, bilateral in 18 (67%), subpleural in 24 (89%), and peribronchovascular in 11 (41%) in distribution. Bronchial wall thickening in the segmental or subsegmental bronchi was seen in 22 (73%) patients. Large airways were also abnormal in 9 (30%) patients. Patchy areas of consolidation and ground-glass opacity were seen in 7 (23%) patients, respectively. In 17 of 20 (85%) patients in whom follow-up CT scans were available, the parenchymal or airway lesion showed complete or partial improvement with treatment. The CT findings of Wegener's granulomatosis, although multiple and variable, consist mainly of bilateral subpleural or peribronchovascular nodules or masses and bronchial wall thickening in the segmental or subsegmental bronchi. Parenchymal and airway lesions improve with treatment in most patients. (orig.)

Identification of dental root canals and their medial line from micro-CT and cone-beam CT records

Directory of Open Access Journals (Sweden)

Benyó Balázs

2012-10-01

Full Text Available Abstract Background Shape of the dental root canal is highly patient specific. Automated identification methods of the medial line of dental root canals and the reproduction of their 3D shape can be beneficial for planning endodontic interventions as severely curved root canals or multi-rooted teeth may pose treatment challenges. Accurate shape information of the root canals may also be used by manufacturers of endodontic instruments in order to make more efficient clinical tools. Method Novel image processing procedures dedicated to the automated detection of the medial axis of the root canal from dental micro-CT and cone-beam CT records are developed. For micro-CT, the 3D model of the root canal is built up from several hundred parallel cross sections, using image enhancement, histogram based fuzzy c-means clustering, center point detection in the segmented slice, three dimensional inner surface reconstruction, and potential field driven curve skeleton extraction in three dimensions. Cone-beam CT records are processed with image enhancement filters and fuzzy chain based regional segmentation, followed by the reconstruction of the root canal surface and detecting its skeleton via a mesh contraction algorithm. Results The proposed medial line identification and root canal detection algorithms are validated on clinical data sets. 25 micro-CT and 36 cone-beam-CT records are used in the validation procedure. The overall success rate of the automatic dental root canal identification was about 92% in both procedures. The algorithms proved to be accurate enough for endodontic therapy planning. Conclusions Accurate medial line identification and shape detection algorithms of dental root canal have been developed. Different procedures are defined for micro-CT and cone-beam CT records. The automated execution of the subsequent processing steps allows easy application of the algorithms in the dental care. The output data of the image processing procedures

SU-F-J-113: Multi-Atlas Based Automatic Organ Segmentation for Lung Radiotherapy Planning

International Nuclear Information System (INIS)

Kim, J; Han, J; Ailawadi, S; Baker, J; Hsia, A; Xu, Z; Ryu, S

2016-01-01

Purpose: Normal organ segmentation is one time-consuming and labor-intensive step for lung radiotherapy treatment planning. The aim of this study is to evaluate the performance of a multi-atlas based segmentation approach for automatic organs at risk (OAR) delineation. Methods: Fifteen Lung stereotactic body radiation therapy patients were randomly selected. Planning CT images and OAR contours of the heart - HT, aorta - AO, vena cava - VC, pulmonary trunk - PT, and esophagus – ES were exported and used as reference and atlas sets. For automatic organ delineation for a given target CT, 1) all atlas sets were deformably warped to the target CT, 2) the deformed sets were accumulated and normalized to produce organ probability density (OPD) maps, and 3) the OPD maps were converted to contours via image thresholding. Optimal threshold for each organ was empirically determined by comparing the auto-segmented contours against their respective reference contours. The delineated results were evaluated by measuring contour similarity metrics: DICE, mean distance (MD), and true detection rate (TD), where DICE=(intersection volume/sum of two volumes) and TD = {1.0 - (false positive + false negative)/2.0}. Diffeomorphic Demons algorithm was employed for CT-CT deformable image registrations. Results: Optimal thresholds were determined to be 0.53 for HT, 0.38 for AO, 0.28 for PT, 0.43 for VC, and 0.31 for ES. The mean similarity metrics (DICE[%], MD[mm], TD[%]) were (88, 3.2, 89) for HT, (79, 3.2, 82) for AO, (75, 2.7, 77) for PT, (68, 3.4, 73) for VC, and (51,2.7, 60) for ES. Conclusion: The investigated multi-atlas based approach produced reliable segmentations for the organs with large and relatively clear boundaries (HT and AO). However, the detection of small and narrow organs with diffused boundaries (ES) were challenging. Sophisticated atlas selection and multi-atlas fusion algorithms may further improve the quality of segmentations.

SU-F-J-113: Multi-Atlas Based Automatic Organ Segmentation for Lung Radiotherapy Planning

Energy Technology Data Exchange (ETDEWEB)

Kim, J; Han, J; Ailawadi, S; Baker, J; Hsia, A; Xu, Z; Ryu, S [Stony Brook University Hospital, Stony Brook, NY (United States)

2016-06-15

Purpose: Normal organ segmentation is one time-consuming and labor-intensive step for lung radiotherapy treatment planning. The aim of this study is to evaluate the performance of a multi-atlas based segmentation approach for automatic organs at risk (OAR) delineation. Methods: Fifteen Lung stereotactic body radiation therapy patients were randomly selected. Planning CT images and OAR contours of the heart - HT, aorta - AO, vena cava - VC, pulmonary trunk - PT, and esophagus – ES were exported and used as reference and atlas sets. For automatic organ delineation for a given target CT, 1) all atlas sets were deformably warped to the target CT, 2) the deformed sets were accumulated and normalized to produce organ probability density (OPD) maps, and 3) the OPD maps were converted to contours via image thresholding. Optimal threshold for each organ was empirically determined by comparing the auto-segmented contours against their respective reference contours. The delineated results were evaluated by measuring contour similarity metrics: DICE, mean distance (MD), and true detection rate (TD), where DICE=(intersection volume/sum of two volumes) and TD = {1.0 - (false positive + false negative)/2.0}. Diffeomorphic Demons algorithm was employed for CT-CT deformable image registrations. Results: Optimal thresholds were determined to be 0.53 for HT, 0.38 for AO, 0.28 for PT, 0.43 for VC, and 0.31 for ES. The mean similarity metrics (DICE[%], MD[mm], TD[%]) were (88, 3.2, 89) for HT, (79, 3.2, 82) for AO, (75, 2.7, 77) for PT, (68, 3.4, 73) for VC, and (51,2.7, 60) for ES. Conclusion: The investigated multi-atlas based approach produced reliable segmentations for the organs with large and relatively clear boundaries (HT and AO). However, the detection of small and narrow organs with diffused boundaries (ES) were challenging. Sophisticated atlas selection and multi-atlas fusion algorithms may further improve the quality of segmentations.

Segmentace cév mozku v CT angiografii

OpenAIRE

Tůma, Tomáš

2006-01-01

One of the key tasks involved in processing of CT angiography image data is the vascular system segmentation. The aim is to provide an objective vizualization of acquired data and thus make the diagnostics easier. This work aims at studying the methods used for segmentation of neurocranium vessels and implementing one of the existing algorithms in co-operation with radiodiagnostic department of Na Bulovce faculty hospital.

Toward accurate tooth segmentation from computed tomography images using a hybrid level set model

Energy Technology Data Exchange (ETDEWEB)

Gan, Yangzhou; Zhao, Qunfei [Department of Automation, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240 (China); Xia, Zeyang, E-mail: zy.xia@siat.ac.cn, E-mail: jing.xiong@siat.ac.cn; Hu, Ying [Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, and The Chinese University of Hong Kong, Shenzhen 518055 (China); Xiong, Jing, E-mail: zy.xia@siat.ac.cn, E-mail: jing.xiong@siat.ac.cn [Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 510855 (China); Zhang, Jianwei [TAMS, Department of Informatics, University of Hamburg, Hamburg 22527 (Germany)

2015-01-15

Purpose: A three-dimensional (3D) model of the teeth provides important information for orthodontic diagnosis and treatment planning. Tooth segmentation is an essential step in generating the 3D digital model from computed tomography (CT) images. The aim of this study is to develop an accurate and efficient tooth segmentation method from CT images. Methods: The 3D dental CT volumetric images are segmented slice by slice in a two-dimensional (2D) transverse plane. The 2D segmentation is composed of a manual initialization step and an automatic slice by slice segmentation step. In the manual initialization step, the user manually picks a starting slice and selects a seed point for each tooth in this slice. In the automatic slice segmentation step, a developed hybrid level set model is applied to segment tooth contours from each slice. Tooth contour propagation strategy is employed to initialize the level set function automatically. Cone beam CT (CBCT) images of two subjects were used to tune the parameters. Images of 16 additional subjects were used to validate the performance of the method. Volume overlap metrics and surface distance metrics were adopted to assess the segmentation accuracy quantitatively. The volume overlap metrics were volume difference (VD, mm{sup 3}) and Dice similarity coefficient (DSC, %). The surface distance metrics were average symmetric surface distance (ASSD, mm), RMS (root mean square) symmetric surface distance (RMSSSD, mm), and maximum symmetric surface distance (MSSD, mm). Computation time was recorded to assess the efficiency. The performance of the proposed method has been compared with two state-of-the-art methods. Results: For the tested CBCT images, the VD, DSC, ASSD, RMSSSD, and MSSD for the incisor were 38.16 ± 12.94 mm{sup 3}, 88.82 ± 2.14%, 0.29 ± 0.03 mm, 0.32 ± 0.08 mm, and 1.25 ± 0.58 mm, respectively; the VD, DSC, ASSD, RMSSSD, and MSSD for the canine were 49.12 ± 9.33 mm{sup 3}, 91.57 ± 0.82%, 0.27 ± 0.02 mm, 0

Toward accurate tooth segmentation from computed tomography images using a hybrid level set model

International Nuclear Information System (INIS)

Gan, Yangzhou; Zhao, Qunfei; Xia, Zeyang; Hu, Ying; Xiong, Jing; Zhang, Jianwei

2015-01-01

Purpose: A three-dimensional (3D) model of the teeth provides important information for orthodontic diagnosis and treatment planning. Tooth segmentation is an essential step in generating the 3D digital model from computed tomography (CT) images. The aim of this study is to develop an accurate and efficient tooth segmentation method from CT images. Methods: The 3D dental CT volumetric images are segmented slice by slice in a two-dimensional (2D) transverse plane. The 2D segmentation is composed of a manual initialization step and an automatic slice by slice segmentation step. In the manual initialization step, the user manually picks a starting slice and selects a seed point for each tooth in this slice. In the automatic slice segmentation step, a developed hybrid level set model is applied to segment tooth contours from each slice. Tooth contour propagation strategy is employed to initialize the level set function automatically. Cone beam CT (CBCT) images of two subjects were used to tune the parameters. Images of 16 additional subjects were used to validate the performance of the method. Volume overlap metrics and surface distance metrics were adopted to assess the segmentation accuracy quantitatively. The volume overlap metrics were volume difference (VD, mm 3 ) and Dice similarity coefficient (DSC, %). The surface distance metrics were average symmetric surface distance (ASSD, mm), RMS (root mean square) symmetric surface distance (RMSSSD, mm), and maximum symmetric surface distance (MSSD, mm). Computation time was recorded to assess the efficiency. The performance of the proposed method has been compared with two state-of-the-art methods. Results: For the tested CBCT images, the VD, DSC, ASSD, RMSSSD, and MSSD for the incisor were 38.16 ± 12.94 mm 3 , 88.82 ± 2.14%, 0.29 ± 0.03 mm, 0.32 ± 0.08 mm, and 1.25 ± 0.58 mm, respectively; the VD, DSC, ASSD, RMSSSD, and MSSD for the canine were 49.12 ± 9.33 mm 3 , 91.57 ± 0.82%, 0.27 ± 0.02 mm, 0.28 ± 0.03 mm

320-detector row CT coronary angiography in patients with arrhythmia

International Nuclear Information System (INIS)

Lu Li; Zhang Zhaoqi; Xu Lei; Yang Lin

2011-01-01

Objective: To evaluate the feasibility of CT coronary angiography (CTCA) in patients with arrhythmia using 320-detector row CT. Methods: Thirty-one patients with persistent atrial fibrillation and 8 patients with premature ventricular contraction were enrolled in this study. All patients underwent 320- detector row CTCA. CT image quality was evaluated with 4-point grading scale by two radiologists. Inter- observer agreement was evaluated by Kappa statistics. The radiation dose was calculated. Results: In total 510 coronary segments, 496 (97.2%) segments met diagnostic standard. The mean effective dose was (12.7±4.8) mSv in this study. There was a good agreement in image quality scoring between the two reviewers (Kappa = 0.72). Conclusion: 320-detector row CTCA is feasible in patients with atrial fibrillation and premature ventricular contraction. Arrhythmia may not be considered as a contraindication to CTCA. (authors)

CT findings of the pulmonary tuberculosis in patients with diabetes mellitus

Energy Technology Data Exchange (ETDEWEB)

Yang, Chang Kyu; Hong, Deok Hwa; Kim, Yeong Tong; Kim, Hyung Lyul; Lee, Jong Myeong; Kim, Jong Kun; Lee, So Hyun; Jeong, Gun Young [Taejon Sun General Hospital, Taejon (Korea, Republic of)

1998-07-01

To evaluate the CT findings of pulmonary tuberculosis in patients with diabetes mellitus (MD),according to the diabetic control state. Materials and Methods: We retrospectively studied 34 cases of pulmonary tuberculosis accompanied by DM. We divided the right lung three lobes and ten segments and the left into two lobes and eight segments and analyzed CT findings of bronchogenic spread, cavitary lesion, ill-defined nodule, lobular consolidation, lobar and segmental consolidation, atelectasis, interlobular septal thickening, fibrotic band, and associated findings such as lymph node enlargement, pleural effusion and empyema. We also tried to determine the typical CT findings of pulmonary tuberculosis according to diabetic duration and controlled state of DM focusing by FBS 160 and HbA1C 8.0. Results: Among 34 CT scans, bronchogenic spread was seen on 29 (85.3%), cavitary lesion on 26 (76.5%), ill-defined nodules on 11 (32.4%), lobular consolidation on 14 (41.2%), lobar and segmental consolidation on 12 (35.3%), atelectasis on four (14.7%), and fibrotic band on eight (23.5%). Multiple cavities were present in 76.9% of total cavitary lesions, and consolidation with bronchogenic spread in 75%; associated findings were as follows: lymph node enlargement (n=1), pleural effusion (n=10), empyema (n=2), and pericardial effusion (n=2). In 46.7% of cases, general tubercular lesions were in an unusual location, but among cases of secondary pulmonary tuberculosis, 73.9% of lesions were in the usual location. More lobular consolidation was seen in patients with less than FBS 160 on admission, and this result was statistically significant (p<0.05); CT findings did not, however, differ according to diabetic duration and HbA1C. Conclusion: In patients with DM,general fubercular lesions were found infrequently, but in secondary tubereulosis, multiple cavitary lesions-in the usual location-were very frequent. In patients with DM, CT findings of pulmonary tuberculosis did not vary

CT findings of the pulmonary tuberculosis in patients with diabetes mellitus

International Nuclear Information System (INIS)

Yang, Chang Kyu; Hong, Deok Hwa; Kim, Yeong Tong; Kim, Hyung Lyul; Lee, Jong Myeong; Kim, Jong Kun; Lee, So Hyun; Jeong, Gun Young

1998-01-01

To evaluate the CT findings of pulmonary tuberculosis in patients with diabetes mellitus (MD),according to the diabetic control state. Materials and Methods: We retrospectively studied 34 cases of pulmonary tuberculosis accompanied by DM. We divided the right lung three lobes and ten segments and the left into two lobes and eight segments and analyzed CT findings of bronchogenic spread, cavitary lesion, ill-defined nodule, lobular consolidation, lobar and segmental consolidation, atelectasis, interlobular septal thickening, fibrotic band, and associated findings such as lymph node enlargement, pleural effusion and empyema. We also tried to determine the typical CT findings of pulmonary tuberculosis according to diabetic duration and controlled state of DM focusing by FBS 160 and HbA1C 8.0. Results: Among 34 CT scans, bronchogenic spread was seen on 29 (85.3%), cavitary lesion on 26 (76.5%), ill-defined nodules on 11 (32.4%), lobular consolidation on 14 (41.2%), lobar and segmental consolidation on 12 (35.3%), atelectasis on four (14.7%), and fibrotic band on eight (23.5%). Multiple cavities were present in 76.9% of total cavitary lesions, and consolidation with bronchogenic spread in 75%; associated findings were as follows: lymph node enlargement (n=1), pleural effusion (n=10), empyema (n=2), and pericardial effusion (n=2). In 46.7% of cases, general tubercular lesions were in an unusual location, but among cases of secondary pulmonary tuberculosis, 73.9% of lesions were in the usual location. More lobular consolidation was seen in patients with less than FBS 160 on admission, and this result was statistically significant (p<0.05); CT findings did not, however, differ according to diabetic duration and HbA1C. Conclusion: In patients with DM,general fubercular lesions were found infrequently, but in secondary tubereulosis, multiple cavitary lesions-in the usual location-were very frequent. In patients with DM, CT findings of pulmonary tuberculosis did not vary

Fully convolutional neural networks improve abdominal organ segmentation

Science.gov (United States)

Bobo, Meg F.; Bao, Shunxing; Huo, Yuankai; Yao, Yuang; Virostko, Jack; Plassard, Andrew J.; Lyu, Ilwoo; Assad, Albert; Abramson, Richard G.; Hilmes, Melissa A.; Landman, Bennett A.

2018-03-01

Abdominal image segmentation is a challenging, yet important clinical problem. Variations in body size, position, and relative organ positions greatly complicate the segmentation process. Historically, multi-atlas methods have achieved leading results across imaging modalities and anatomical targets. However, deep learning is rapidly overtaking classical approaches for image segmentation. Recently, Zhou et al. showed that fully convolutional networks produce excellent results in abdominal organ segmentation of computed tomography (CT) scans. Yet, deep learning approaches have not been applied to whole abdomen magnetic resonance imaging (MRI) segmentation. Herein, we evaluate the applicability of an existing fully convolutional neural network (FCNN) designed for CT imaging to segment abdominal organs on T2 weighted (T2w) MRI's with two examples. In the primary example, we compare a classical multi-atlas approach with FCNN on forty-five T2w MRI's acquired from splenomegaly patients with five organs labeled (liver, spleen, left kidney, right kidney, and stomach). Thirty-six images were used for training while nine were used for testing. The FCNN resulted in a Dice similarity coefficient (DSC) of 0.930 in spleens, 0.730 in left kidneys, 0.780 in right kidneys, 0.913 in livers, and 0.556 in stomachs. The performance measures for livers, spleens, right kidneys, and stomachs were significantly better than multi-atlas (p < 0.05, Wilcoxon rank-sum test). In a secondary example, we compare the multi-atlas approach with FCNN on 138 distinct T2w MRI's with manually labeled pancreases (one label). On the pancreas dataset, the FCNN resulted in a median DSC of 0.691 in pancreases versus 0.287 for multi-atlas. The results are highly promising given relatively limited training data and without specific training of the FCNN model and illustrate the potential of deep learning approaches to transcend imaging modalities. 1