Millisecond single-molecule localization microscopy combined with convolution analysis and automated image segmentation to determine protein concentrations in complexly structured, functional cells, one cell at a time.

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Wollman, Adam J M; Leake, Mark C

2015-01-01

We present a single-molecule tool called the CoPro (concentration of proteins) method that uses millisecond imaging with convolution analysis, automated image segmentation and super-resolution localization microscopy to generate robust estimates for protein concentration in different compartments of single living cells, validated using realistic simulations of complex multiple compartment cell types. We demonstrate its utility experimentally on model Escherichia coli bacteria and Saccharomyces cerevisiae budding yeast cells, and use it to address the biological question of how signals are transduced in cells. Cells in all domains of life dynamically sense their environment through signal transduction mechanisms, many involving gene regulation. The glucose sensing mechanism of S. cerevisiae is a model system for studying gene regulatory signal transduction. It uses the multi-copy expression inhibitor of the GAL gene family, Mig1, to repress unwanted genes in the presence of elevated extracellular glucose concentrations. We fluorescently labelled Mig1 molecules with green fluorescent protein (GFP) via chromosomal integration at physiological expression levels in living S. cerevisiae cells, in addition to the RNA polymerase protein Nrd1 with the fluorescent protein reporter mCherry. Using CoPro we make quantitative estimates of Mig1 and Nrd1 protein concentrations in the cytoplasm and nucleus compartments on a cell-by-cell basis under physiological conditions. These estimates indicate a ∼4-fold shift towards higher values in the concentration of diffusive Mig1 in the nucleus if the external glucose concentration is raised, whereas equivalent levels in the cytoplasm shift to smaller values with a relative change an order of magnitude smaller. This compares with Nrd1 which is not involved directly in glucose sensing, and which is almost exclusively localized in the nucleus under high and low external glucose levels. CoPro facilitates time-resolved quantification of

Automated recognition of cell phenotypes in histology images based on membrane- and nuclei-targeting biomarkers

International Nuclear Information System (INIS)

Karaçalı, Bilge; Vamvakidou, Alexandra P; Tözeren, Aydın

2007-01-01

Three-dimensional in vitro culture of cancer cells are used to predict the effects of prospective anti-cancer drugs in vivo. In this study, we present an automated image analysis protocol for detailed morphological protein marker profiling of tumoroid cross section images. Histologic cross sections of breast tumoroids developed in co-culture suspensions of breast cancer cell lines, stained for E-cadherin and progesterone receptor, were digitized and pixels in these images were classified into five categories using k-means clustering. Automated segmentation was used to identify image regions composed of cells expressing a given biomarker. Synthesized images were created to check the accuracy of the image processing system. Accuracy of automated segmentation was over 95% in identifying regions of interest in synthesized images. Image analysis of adjacent histology slides stained, respectively, for Ecad and PR, accurately predicted regions of different cell phenotypes. Image analysis of tumoroid cross sections from different tumoroids obtained under the same co-culture conditions indicated the variation of cellular composition from one tumoroid to another. Variations in the compositions of cross sections obtained from the same tumoroid were established by parallel analysis of Ecad and PR-stained cross section images. Proposed image analysis methods offer standardized high throughput profiling of molecular anatomy of tumoroids based on both membrane and nuclei markers that is suitable to rapid large scale investigations of anti-cancer compounds for drug development

Extraction of the number of peroxisomes in yeast cells by automated image analysis.

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Niemistö, Antti; Selinummi, Jyrki; Saleem, Ramsey; Shmulevich, Ilya; Aitchison, John; Yli-Harja, Olli

2006-01-01

An automated image analysis method for extracting the number of peroxisomes in yeast cells is presented. Two images of the cell population are required for the method: a bright field microscope image from which the yeast cells are detected and the respective fluorescent image from which the number of peroxisomes in each cell is found. The segmentation of the cells is based on clustering the local mean-variance space. The watershed transformation is thereafter employed to separate cells that are clustered together. The peroxisomes are detected by thresholding the fluorescent image. The method is tested with several images of a budding yeast Saccharomyces cerevisiae population, and the results are compared with manually obtained results.

Fluorescence In Situ Hybridization (FISH Signal Analysis Using Automated Generated Projection Images

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Xingwei Wang

2012-01-01

Full Text Available Fluorescence in situ hybridization (FISH tests provide promising molecular imaging biomarkers to more accurately and reliably detect and diagnose cancers and genetic disorders. Since current manual FISH signal analysis is low-efficient and inconsistent, which limits its clinical utility, developing automated FISH image scanning systems and computer-aided detection (CAD schemes has been attracting research interests. To acquire high-resolution FISH images in a multi-spectral scanning mode, a huge amount of image data with the stack of the multiple three-dimensional (3-D image slices is generated from a single specimen. Automated preprocessing these scanned images to eliminate the non-useful and redundant data is important to make the automated FISH tests acceptable in clinical applications. In this study, a dual-detector fluorescence image scanning system was applied to scan four specimen slides with FISH-probed chromosome X. A CAD scheme was developed to detect analyzable interphase cells and map the multiple imaging slices recorded FISH-probed signals into the 2-D projection images. CAD scheme was then applied to each projection image to detect analyzable interphase cells using an adaptive multiple-threshold algorithm, identify FISH-probed signals using a top-hat transform, and compute the ratios between the normal and abnormal cells. To assess CAD performance, the FISH-probed signals were also independently visually detected by an observer. The Kappa coefficients for agreement between CAD and observer ranged from 0.69 to 1.0 in detecting/counting FISH signal spots in four testing samples. The study demonstrated the feasibility of automated FISH signal analysis that applying a CAD scheme to the automated generated 2-D projection images.

Automated processing of label-free Raman microscope images of macrophage cells with standardized regression for high-throughput analysis.

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Milewski, Robert J; Kumagai, Yutaro; Fujita, Katsumasa; Standley, Daron M; Smith, Nicholas I

2010-11-19

Macrophages represent the front lines of our immune system; they recognize and engulf pathogens or foreign particles thus initiating the immune response. Imaging macrophages presents unique challenges, as most optical techniques require labeling or staining of the cellular compartments in order to resolve organelles, and such stains or labels have the potential to perturb the cell, particularly in cases where incomplete information exists regarding the precise cellular reaction under observation. Label-free imaging techniques such as Raman microscopy are thus valuable tools for studying the transformations that occur in immune cells upon activation, both on the molecular and organelle levels. Due to extremely low signal levels, however, Raman microscopy requires sophisticated image processing techniques for noise reduction and signal extraction. To date, efficient, automated algorithms for resolving sub-cellular features in noisy, multi-dimensional image sets have not been explored extensively. We show that hybrid z-score normalization and standard regression (Z-LSR) can highlight the spectral differences within the cell and provide image contrast dependent on spectral content. In contrast to typical Raman imaging processing methods using multivariate analysis, such as single value decomposition (SVD), our implementation of the Z-LSR method can operate nearly in real-time. In spite of its computational simplicity, Z-LSR can automatically remove background and bias in the signal, improve the resolution of spatially distributed spectral differences and enable sub-cellular features to be resolved in Raman microscopy images of mouse macrophage cells. Significantly, the Z-LSR processed images automatically exhibited subcellular architectures whereas SVD, in general, requires human assistance in selecting the components of interest. The computational efficiency of Z-LSR enables automated resolution of sub-cellular features in large Raman microscopy data sets without

An automated approach for single-cell tracking in epifluorescence microscopy applied to E. coli growth analysis on microfluidics biochips

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Fetita, Catalin; Kirov, Boris; Jaramillo, Alfonso; Lefevre, Christophe

2012-03-01

With the accumulation of knowledge for the intimate molecular mechanisms governing the processes inside the living cells in the later years, the ability to characterize the performance of elementary genetic circuits and parts at the single-cell level is becoming of crucial importance. Biological science is arriving to the point where it can develop hypothesis for the action of each molecule participating in the biochemical reactions and need proper techniques to test those hypothesis. Microfluidics is emerging as the technology that combined with high-magnification microscopy will allow for the long-term single-cell level observation of bacterial physiology. In this study we design, build and characterize the gene dynamics of genetic circuits as one of the basic parts governing programmed cell behavior. We use E. coli as model organism and grow it in microfluidics chips, which we observe with epifluorescence microscopy. One of the most invaluable segments of this technology is the consequent image processing, since it allows for the automated analysis of vast amount of single-cell observation and the fast and easy derivation of conclusions based on that data. Specifically, we are interested in promoter activity as function of time. We expect it to be oscillatory and for that we use GFP (green fluorescent protein) as a reporter in our genetic circuits. In this paper, an automated framework for single-cell tracking in phase-contrast microscopy is developed, combining 2D segmentation of cell time frames and graph-based reconstruction of their spatiotemporal evolution with fast tracking of the associated fluorescence signal. The results obtained on the investigated biological database are presented and discussed.

Cryo-imaging of fluorescently labeled single cells in a mouse

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Steyer, Grant J.; Roy, Debashish; Salvado, Olivier; Stone, Meredith E.; Wilson, David L.

2009-02-01

We developed a cryo-imaging system to provide single-cell detection of fluorescently labeled cells in mouse, with particular applicability to stem cells and metastatic cancer. The Case cryoimaging system consists of a fluorescence microscope, robotic imaging positioner, customized cryostat, PC-based control system, and visualization/analysis software. The system alternates between sectioning (10-40 μm) and imaging, collecting color brightfield and fluorescent blockface image volumes >60GB. In mouse experiments, we imaged quantum-dot labeled stem cells, GFP-labeled cancer and stem cells, and cell-size fluorescent microspheres. To remove subsurface fluorescence, we used a simplified model of light-tissue interaction whereby the next image was scaled, blurred, and subtracted from the current image. We estimated scaling and blurring parameters by minimizing entropy of subtracted images. Tissue specific attenuation parameters were found [uT : heart (267 +/- 47.6 μm), liver (218 +/- 27.1 μm), brain (161 +/- 27.4 μm)] to be within the range of estimates in the literature. "Next image" processing removed subsurface fluorescence equally well across multiple tissues (brain, kidney, liver, adipose tissue, etc.), and analysis of 200 microsphere images in the brain gave 97+/-2% reduction of subsurface fluorescence. Fluorescent signals were determined to arise from single cells based upon geometric and integrated intensity measurements. Next image processing greatly improved axial resolution, enabled high quality 3D volume renderings, and improved enumeration of single cells with connected component analysis by up to 24%. Analysis of image volumes identified metastatic cancer sites, found homing of stem cells to injury sites, and showed microsphere distribution correlated with blood flow patterns. We developed and evaluated cryo-imaging to provide single-cell detection of fluorescently labeled cells in mouse. Our cryo-imaging system provides extreme (>60GB), micron

OpenComet: An automated tool for comet assay image analysis

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Benjamin M. Gyori

2014-01-01

Full Text Available Reactive species such as free radicals are constantly generated in vivo and DNA is the most important target of oxidative stress. Oxidative DNA damage is used as a predictive biomarker to monitor the risk of development of many diseases. The comet assay is widely used for measuring oxidative DNA damage at a single cell level. The analysis of comet assay output images, however, poses considerable challenges. Commercial software is costly and restrictive, while free software generally requires laborious manual tagging of cells. This paper presents OpenComet, an open-source software tool providing automated analysis of comet assay images. It uses a novel and robust method for finding comets based on geometric shape attributes and segmenting the comet heads through image intensity profile analysis. Due to automation, OpenComet is more accurate, less prone to human bias, and faster than manual analysis. A live analysis functionality also allows users to analyze images captured directly from a microscope. We have validated OpenComet on both alkaline and neutral comet assay images as well as sample images from existing software packages. Our results show that OpenComet achieves high accuracy with significantly reduced analysis time.

Automated Photoreceptor Cell Identification on Nonconfocal Adaptive Optics Images Using Multiscale Circular Voting.

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Liu, Jianfei; Jung, HaeWon; Dubra, Alfredo; Tam, Johnny

2017-09-01

Adaptive optics scanning light ophthalmoscopy (AOSLO) has enabled quantification of the photoreceptor mosaic in the living human eye using metrics such as cell density and average spacing. These rely on the identification of individual cells. Here, we demonstrate a novel approach for computer-aided identification of cone photoreceptors on nonconfocal split detection AOSLO images. Algorithms for identification of cone photoreceptors were developed, based on multiscale circular voting (MSCV) in combination with a priori knowledge that split detection images resemble Nomarski differential interference contrast images, in which dark and bright regions are present on the two sides of each cell. The proposed algorithm locates dark and bright region pairs, iteratively refining the identification across multiple scales. Identification accuracy was assessed in data from 10 subjects by comparing automated identifications with manual labeling, followed by computation of density and spacing metrics for comparison to histology and published data. There was good agreement between manual and automated cone identifications with overall recall, precision, and F1 score of 92.9%, 90.8%, and 91.8%, respectively. On average, computed density and spacing values using automated identification were within 10.7% and 11.2% of the expected histology values across eccentricities ranging from 0.5 to 6.2 mm. There was no statistically significant difference between MSCV-based and histology-based density measurements (P = 0.96, Kolmogorov-Smirnov 2-sample test). MSCV can accurately detect cone photoreceptors on split detection images across a range of eccentricities, enabling quick, objective estimation of photoreceptor mosaic metrics, which will be important for future clinical trials utilizing adaptive optics.

SuperSegger: robust image segmentation, analysis and lineage tracking of bacterial cells.

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Stylianidou, Stella; Brennan, Connor; Nissen, Silas B; Kuwada, Nathan J; Wiggins, Paul A

2016-11-01

Many quantitative cell biology questions require fast yet reliable automated image segmentation to identify and link cells from frame-to-frame, and characterize the cell morphology and fluorescence. We present SuperSegger, an automated MATLAB-based image processing package well-suited to quantitative analysis of high-throughput live-cell fluorescence microscopy of bacterial cells. SuperSegger incorporates machine-learning algorithms to optimize cellular boundaries and automated error resolution to reliably link cells from frame-to-frame. Unlike existing packages, it can reliably segment microcolonies with many cells, facilitating the analysis of cell-cycle dynamics in bacteria as well as cell-contact mediated phenomena. This package has a range of built-in capabilities for characterizing bacterial cells, including the identification of cell division events, mother, daughter and neighbouring cells, and computing statistics on cellular fluorescence, the location and intensity of fluorescent foci. SuperSegger provides a variety of postprocessing data visualization tools for single cell and population level analysis, such as histograms, kymographs, frame mosaics, movies and consensus images. Finally, we demonstrate the power of the package by analyzing lag phase growth with single cell resolution. © 2016 John Wiley & Sons Ltd.

Precision automation of cell type classification and sub-cellular fluorescence quantification from laser scanning confocal images

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Hardy Craig Hall

2016-02-01

Full Text Available While novel whole-plant phenotyping technologies have been successfully implemented into functional genomics and breeding programs, the potential of automated phenotyping with cellular resolution is largely unexploited. Laser scanning confocal microscopy has the potential to close this gap by providing spatially highly resolved images containing anatomic as well as chemical information on a subcellular basis. However, in the absence of automated methods, the assessment of the spatial patterns and abundance of fluorescent markers with subcellular resolution is still largely qualitative and time-consuming. Recent advances in image acquisition and analysis, coupled with improvements in microprocessor performance, have brought such automated methods within reach, so that information from thousands of cells per image for hundreds of images may be derived in an experimentally convenient time-frame. Here, we present a MATLAB-based analytical pipeline to 1 segment radial plant organs into individual cells, 2 classify cells into cell type categories based upon random forest classification, 3 divide each cell into sub-regions, and 4 quantify fluorescence intensity to a subcellular degree of precision for a separate fluorescence channel. In this research advance, we demonstrate the precision of this analytical process for the relatively complex tissues of Arabidopsis hypocotyls at various stages of development. High speed and robustness make our approach suitable for phenotyping of large collections of stem-like material and other tissue types.

Single-Molecule Light-Sheet Imaging of Suspended T Cells.

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Ponjavic, Aleks; McColl, James; Carr, Alexander R; Santos, Ana Mafalda; Kulenkampff, Klara; Lippert, Anna; Davis, Simon J; Klenerman, David; Lee, Steven F

2018-05-08

Adaptive immune responses are initiated by triggering of the T cell receptor. Single-molecule imaging based on total internal reflection fluorescence microscopy at coverslip/basal cell interfaces is commonly used to study this process. These experiments have suggested, unexpectedly, that the diffusional behavior and organization of signaling proteins and receptors may be constrained before activation. However, it is unclear to what extent the molecular behavior and cell state is affected by the imaging conditions, i.e., by the presence of a supporting surface. In this study, we implemented single-molecule light-sheet microscopy, which enables single receptors to be directly visualized at any plane in a cell to study protein dynamics and organization in live, resting T cells. The light sheet enabled the acquisition of high-quality single-molecule fluorescence images that were comparable to those of total internal reflection fluorescence microscopy. By comparing the apical and basal surfaces of surface-contacting T cells using single-molecule light-sheet microscopy, we found that most coated-glass surfaces and supported lipid bilayers profoundly affected the diffusion of membrane proteins (T cell receptor and CD45) and that all the surfaces induced calcium influx to various degrees. Our results suggest that, when studying resting T cells, surfaces are best avoided, which we achieve here by suspending cells in agarose. Copyright © 2018. Published by Elsevier Inc.

Large-scale image-based profiling of single-cell phenotypes in arrayed CRISPR-Cas9 gene perturbation screens.

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de Groot, Reinoud; Lüthi, Joel; Lindsay, Helen; Holtackers, René; Pelkmans, Lucas

2018-01-23

High-content imaging using automated microscopy and computer vision allows multivariate profiling of single-cell phenotypes. Here, we present methods for the application of the CISPR-Cas9 system in large-scale, image-based, gene perturbation experiments. We show that CRISPR-Cas9-mediated gene perturbation can be achieved in human tissue culture cells in a timeframe that is compatible with image-based phenotyping. We developed a pipeline to construct a large-scale arrayed library of 2,281 sequence-verified CRISPR-Cas9 targeting plasmids and profiled this library for genes affecting cellular morphology and the subcellular localization of components of the nuclear pore complex (NPC). We conceived a machine-learning method that harnesses genetic heterogeneity to score gene perturbations and identify phenotypically perturbed cells for in-depth characterization of gene perturbation effects. This approach enables genome-scale image-based multivariate gene perturbation profiling using CRISPR-Cas9. © 2018 The Authors. Published under the terms of the CC BY 4.0 license.

High-performance imaging of stem cells using single-photon emissions

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Wagenaar, Douglas J.; Moats, Rex A.; Hartsough, Neal E.; Meier, Dirk; Hugg, James W.; Yang, Tang; Gazit, Dan; Pelled, Gadi; Patt, Bradley E.

2011-10-01

Radiolabeled cells have been imaged for decades in the field of autoradiography. Recent advances in detector and microelectronics technologies have enabled the new field of "digital autoradiography" which remains limited to ex vivo specimens of thin tissue slices. The 3D field-of-view (FOV) of single cell imaging can be extended to millimeters if the low energy (10-30 keV) photon emissions of radionuclides are used for single-photon nuclear imaging. This new microscope uses a coded aperture foil made of highly attenuating elements such as gold or platinum to form the image as a kind of "lens". The detectors used for single-photon emission microscopy are typically silicon detectors with a pixel pitch less than 60 μm. The goal of this work is to image radiolabeled mesenchymal stem cells in vivo in an animal model of tendon repair processes. Single-photon nuclear imaging is an attractive modality for translational medicine since the labeled cells can be imaged simultaneously with the reparative processes by using the dual-isotope imaging technique. The details our microscope's two-layer gold aperture and the operation of the energy-dispersive, pixellated silicon detector are presented along with the first demonstration of energy discrimination with a 57Co source. Cell labeling techniques have been augmented by genetic engineering with the sodium-iodide symporter, a type of reporter gene imaging method that enables in vivo uptake of free 99mTc or an iodine isotope at a time point days or weeks after the insertion of the genetically modified stem cells into the animal model. This microscopy work in animal research may expand to the imaging of reporter-enabled stem cells simultaneously with the expected biological repair process in human clinical trials of stem cell therapies.

Automated analysis of invadopodia dynamics in live cells

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Matthew E. Berginski

2014-07-01

Full Text Available Multiple cell types form specialized protein complexes that are used by the cell to actively degrade the surrounding extracellular matrix. These structures are called podosomes or invadopodia and collectively referred to as invadosomes. Due to their potential importance in both healthy physiology as well as in pathological conditions such as cancer, the characterization of these structures has been of increasing interest. Following early descriptions of invadopodia, assays were developed which labelled the matrix underneath metastatic cancer cells allowing for the assessment of invadopodia activity in motile cells. However, characterization of invadopodia using these methods has traditionally been done manually with time-consuming and potentially biased quantification methods, limiting the number of experiments and the quantity of data that can be analysed. We have developed a system to automate the segmentation, tracking and quantification of invadopodia in time-lapse fluorescence image sets at both the single invadopodia level and whole cell level. We rigorously tested the ability of the method to detect changes in invadopodia formation and dynamics through the use of well-characterized small molecule inhibitors, with known effects on invadopodia. Our results demonstrate the ability of this analysis method to quantify changes in invadopodia formation from live cell imaging data in a high throughput, automated manner.

Cell biochemistry studied by single-molecule imaging.

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Mashanov, G I; Nenasheva, T A; Peckham, M; Molloy, J E

2006-11-01

Over the last decade, there have been remarkable developments in live-cell imaging. We can now readily observe individual protein molecules within living cells and this should contribute to a systems level understanding of biological pathways. Direct observation of single fluorophores enables several types of molecular information to be gathered. Temporal and spatial trajectories enable diffusion constants and binding kinetics to be deduced, while analyses of fluorescence lifetime, intensity, polarization or spectra give chemical and conformational information about molecules in their cellular context. By recording the spatial trajectories of pairs of interacting molecules, formation of larger molecular complexes can be studied. In the future, multicolour and multiparameter imaging of single molecules in live cells will be a powerful analytical tool for systems biology. Here, we discuss measurements of single-molecule mobility and residency at the plasma membrane of live cells. Analysis of diffusional paths at the plasma membrane gives information about its physical properties and measurement of temporal trajectories enables rates of binding and dissociation to be derived. Meanwhile, close scrutiny of individual fluorophore trajectories enables ideas about molecular dimerization and oligomerization related to function to be tested directly.

Single-cell magnetic imaging using a quantum diamond microscope.

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Glenn, D R; Lee, K; Park, H; Weissleder, R; Yacoby, A; Lukin, M D; Lee, H; Walsworth, R L; Connolly, C B

2015-08-01

We apply a quantum diamond microscope for detection and imaging of immunomagnetically labeled cells. This instrument uses nitrogen-vacancy (NV) centers in diamond for correlated magnetic and fluorescence imaging. Our device provides single-cell resolution and a field of view (∼1 mm(2)) two orders of magnitude larger than that of previous NV imaging technologies, enabling practical applications. To illustrate, we quantified cancer biomarkers expressed by rare tumor cells in a large population of healthy cells.

Development of Raman microspectroscopy for automated detection and imaging of basal cell carcinoma

Science.gov (United States)

Larraona-Puy, Marta; Ghita, Adrian; Zoladek, Alina; Perkins, William; Varma, Sandeep; Leach, Iain H.; Koloydenko, Alexey A.; Williams, Hywel; Notingher, Ioan

2009-09-01

We investigate the potential of Raman microspectroscopy (RMS) for automated evaluation of excised skin tissue during Mohs micrographic surgery (MMS). The main aim is to develop an automated method for imaging and diagnosis of basal cell carcinoma (BCC) regions. Selected Raman bands responsible for the largest spectral differences between BCC and normal skin regions and linear discriminant analysis (LDA) are used to build a multivariate supervised classification model. The model is based on 329 Raman spectra measured on skin tissue obtained from 20 patients. BCC is discriminated from healthy tissue with 90+/-9% sensitivity and 85+/-9% specificity in a 70% to 30% split cross-validation algorithm. This multivariate model is then applied on tissue sections from new patients to image tumor regions. The RMS images show excellent correlation with the gold standard of histopathology sections, BCC being detected in all positive sections. We demonstrate the potential of RMS as an automated objective method for tumor evaluation during MMS. The replacement of current histopathology during MMS by a ``generalization'' of the proposed technique may improve the feasibility and efficacy of MMS, leading to a wider use according to clinical need.

Extended Field Laser Confocal Microscopy (EFLCM): Combining automated Gigapixel image capture with in silico virtual microscopy

International Nuclear Information System (INIS)

Flaberg, Emilie; Sabelström, Per; Strandh, Christer; Szekely, Laszlo

2008-01-01

Confocal laser scanning microscopy has revolutionized cell biology. However, the technique has major limitations in speed and sensitivity due to the fact that a single laser beam scans the sample, allowing only a few microseconds signal collection for each pixel. This limitation has been overcome by the introduction of parallel beam illumination techniques in combination with cold CCD camera based image capture. Using the combination of microlens enhanced Nipkow spinning disc confocal illumination together with fully automated image capture and large scale in silico image processing we have developed a system allowing the acquisition, presentation and analysis of maximum resolution confocal panorama images of several Gigapixel size. We call the method Extended Field Laser Confocal Microscopy (EFLCM). We show using the EFLCM technique that it is possible to create a continuous confocal multi-colour mosaic from thousands of individually captured images. EFLCM can digitize and analyze histological slides, sections of entire rodent organ and full size embryos. It can also record hundreds of thousands cultured cells at multiple wavelength in single event or time-lapse fashion on fixed slides, in live cell imaging chambers or microtiter plates. The observer independent image capture of EFLCM allows quantitative measurements of fluorescence intensities and morphological parameters on a large number of cells. EFLCM therefore bridges the gap between the mainly illustrative fluorescence microscopy and purely quantitative flow cytometry. EFLCM can also be used as high content analysis (HCA) instrument for automated screening processes

Preparation of Single Cells for Imaging Mass Spectrometry

Energy Technology Data Exchange (ETDEWEB)

Berman, E S; Fortson, S L; Kulp, K S; Checchi, K D; Wu, L; Felton, J S; Wu, K J

2007-10-24

Characterizing chemical changes within single cells is important for determining fundamental mechanisms of biological processes that will lead to new biological insights and improved disease understanding. Imaging biological systems with mass spectrometry (MS) has gained popularity in recent years as a method for creating precise chemical maps of biological samples. In order to obtain high-quality mass spectral images that provide relevant molecular information about individual cells, samples must be prepared so that salts and other cell-culture components are removed from the cell surface and the cell contents are rendered accessible to the desorption beam. We have designed a cellular preparation protocol for imaging MS that preserves the cellular contents for investigation and removes the majority of the interfering species from the extracellular matrix. Using this method, we obtain excellent imaging results and reproducibility in three diverse cell types: MCF7 human breast cancer cells, Madin-Darby canine kidney (MDCK) cells, and NIH/3T3 mouse fibroblasts. This preparation technique allows routine imaging MS analysis of cultured cells, allowing for any number of experiments aimed at furthering scientific understanding of molecular processes within individual cells.

High resolution ultrasound and photoacoustic imaging of single cells

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Eric M. Strohm

2016-03-01

Full Text Available High resolution ultrasound and photoacoustic images of stained neutrophils, lymphocytes and monocytes from a blood smear were acquired using a combined acoustic/photoacoustic microscope. Photoacoustic images were created using a pulsed 532 nm laser that was coupled to a single mode fiber to produce output wavelengths from 532 nm to 620 nm via stimulated Raman scattering. The excitation wavelength was selected using optical filters and focused onto the sample using a 20× objective. A 1000 MHz transducer was co-aligned with the laser spot and used for ultrasound and photoacoustic images, enabling micrometer resolution with both modalities. The different cell types could be easily identified due to variations in contrast within the acoustic and photoacoustic images. This technique provides a new way of probing leukocyte structure with potential applications towards detecting cellular abnormalities and diseased cells at the single cell level.

Automated migration analysis based on cell texture: method & reliability

Directory of Open Access Journals (Sweden)

Chittenden Thomas W

2005-03-01

Full Text Available Abstract Background In this paper, we present and validate a way to measure automatically the extent of cell migration based on automated examination of a series of digital photographs. It was designed specifically to identify the impact of Second Hand Smoke (SHS on endothelial cell migration but has broader applications. The analysis has two stages: (1 preprocessing of image texture, and (2 migration analysis. Results The output is a graphic overlay that indicates the front lines of cell migration superimposed on each original image, with automated reporting of the distance traversed vs. time. Expert preference compares to manual placement of leading edge shows complete equivalence of automated vs. manual leading edge definition for cell migration measurement. Conclusion Our method is indistinguishable from careful manual determinations of cell front lines, with the advantages of full automation, objectivity, and speed.

Bacterial growth on surfaces: Automated image analysis for quantification of growth rate-related parameters

DEFF Research Database (Denmark)

Møller, S.; Sternberg, Claus; Poulsen, L. K.

1995-01-01

species-specific hybridizations with fluorescence-labelled ribosomal probes to estimate the single-cell concentration of RNA. By automated analysis of digitized images of stained cells, we determined four independent growth rate-related parameters: cellular RNA and DNA contents, cell volume......, and the frequency of dividing cells in a cell population. These parameters were used to compare physiological states of liquid-suspended and surfacegrowing Pseudomonas putida KT2442 in chemostat cultures. The major finding is that the correlation between substrate availability and cellular growth rate found...

Advances in Automated Plankton Imaging: Enhanced Throughput, Automated Staining, and Extended Deployment Modes for Imaging FlowCytobot

Science.gov (United States)

Sosik, H. M.; Olson, R. J.; Brownlee, E.; Brosnahan, M.; Crockford, E. T.; Peacock, E.; Shalapyonok, A.

2016-12-01

Imaging FlowCytobot (IFCB) was developed to fill a need for automated identification and monitoring of nano- and microplankton, especially phytoplankton in the size range 10 200 micrometer, which are important in coastal blooms (including harmful algal blooms). IFCB uses a combination of flow cytometric and video technology to capture high resolution (1 micrometer) images of suspended particles. This proven, now commercially available, submersible instrument technology has been deployed in fixed time series locations for extended periods (months to years) and in shipboard laboratories where underway water is automatically analyzed during surveys. Building from these successes, we have now constructed and evaluated three new prototype IFCB designs that extend measurement and deployment capabilities. To improve cell counting statistics without degrading image quality, a high throughput version (IFCB-HT) incorporates in-flow acoustic focusing to non-disruptively pre-concentrate cells before the measurement area of the flow cell. To extend imaging to all heterotrophic cells (even those that do not exhibit chlorophyll fluorescence), Staining IFCB (IFCB-S) incorporates automated addition of a live-cell fluorescent stain (fluorescein diacetate) to samples before analysis. A horizontally-oriented IFCB-AV design addresses the need for spatial surveying from surface autonomous vehicles, including design features that reliably eliminate air bubbles and mitigate wave motion impacts. Laboratory evaluation and test deployments in waters near Woods Hole show the efficacy of each of these enhanced IFCB designs.

In situ single molecule imaging of cell membranes: linking basic nanotechniques to cell biology, immunology and medicine

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Pi, Jiang; Jin, Hua; Yang, Fen; Chen, Zheng W.; Cai, Jiye

2014-10-01

The cell membrane, which consists of a viscous phospholipid bilayer, different kinds of proteins and various nano/micrometer-sized domains, plays a very important role in ensuring the stability of the intracellular environment and the order of cellular signal transductions. Exploring the precise cell membrane structure and detailed functions of the biomolecules in a cell membrane would be helpful to understand the underlying mechanisms involved in cell membrane signal transductions, which could further benefit research into cell biology, immunology and medicine. The detection of membrane biomolecules at the single molecule level can provide some subtle information about the molecular structure and the functions of the cell membrane. In particular, information obtained about the molecular mechanisms and other information at the single molecule level are significantly different from that detected from a large amount of biomolecules at the large-scale through traditional techniques, and can thus provide a novel perspective for the study of cell membrane structures and functions. However, the precise investigations of membrane biomolecules prompts researchers to explore cell membranes at the single molecule level by the use of in situ imaging methods, as the exact conformation and functions of biomolecules are highly controlled by the native cellular environment. Recently, the in situ single molecule imaging of cell membranes has attracted increasing attention from cell biologists and immunologists. The size of biomolecules and their clusters on the cell surface are set at the nanoscale, which makes it mandatory to use high- and super-resolution imaging techniques to realize the in situ single molecule imaging of cell membranes. In the past few decades, some amazing imaging techniques and instruments with super resolution have been widely developed for molecule imaging, which can also be further employed for the in situ single molecule imaging of cell membranes. In

Image analysis for the automated estimation of clonal growth and its application to the growth of smooth muscle cells.

Science.gov (United States)

Gavino, V C; Milo, G E; Cornwell, D G

1982-03-01

Image analysis was used for the automated measurement of colony frequency (f) and colony diameter (d) in cultures of smooth muscle cells, Initial studies with the inverted microscope showed that number of cells (N) in a colony varied directly with d: log N = 1.98 log d - 3.469 Image analysis generated the complement of a cumulative distribution for f as a function of d. The number of cells in each segment of the distribution function was calculated by multiplying f and the average N for the segment. These data were displayed as a cumulative distribution function. The total number of colonies (fT) and the total number of cells (NT) were used to calculate the average colony size (NA). Population doublings (PD) were then expressed as log2 NA. Image analysis confirmed previous studies in which colonies were sized and counted with an inverted microscope. Thus, image analysis is a rapid and automated technique for the measurement of clonal growth.

Closed-Loop Real-Time Imaging Enables Fully Automated Cell-Targeted Patch-Clamp Neural Recording In Vivo.

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Suk, Ho-Jun; van Welie, Ingrid; Kodandaramaiah, Suhasa B; Allen, Brian; Forest, Craig R; Boyden, Edward S

2017-08-30

Targeted patch-clamp recording is a powerful method for characterizing visually identified cells in intact neural circuits, but it requires skill to perform. We previously developed an algorithm that automates "blind" patching in vivo, but full automation of visually guided, targeted in vivo patching has not been demonstrated, with currently available approaches requiring human intervention to compensate for cell movement as a patch pipette approaches a targeted neuron. Here we present a closed-loop real-time imaging strategy that automatically compensates for cell movement by tracking cell position and adjusting pipette motion while approaching a target. We demonstrate our system's ability to adaptively patch, under continuous two-photon imaging and real-time analysis, fluorophore-expressing neurons of multiple types in the living mouse cortex, without human intervention, with yields comparable to skilled human experimenters. Our "imagepatching" robot is easy to implement and will help enable scalable characterization of identified cell types in intact neural circuits. Copyright © 2017 Elsevier Inc. All rights reserved.

Quantification of sterol-specific response in human macrophages using automated imaged-based analysis.

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Gater, Deborah L; Widatalla, Namareq; Islam, Kinza; AlRaeesi, Maryam; Teo, Jeremy C M; Pearson, Yanthe E

2017-12-13

The transformation of normal macrophage cells into lipid-laden foam cells is an important step in the progression of atherosclerosis. One major contributor to foam cell formation in vivo is the intracellular accumulation of cholesterol. Here, we report the effects of various combinations of low-density lipoprotein, sterols, lipids and other factors on human macrophages, using an automated image analysis program to quantitatively compare single cell properties, such as cell size and lipid content, in different conditions. We observed that the addition of cholesterol caused an increase in average cell lipid content across a range of conditions. All of the sterol-lipid mixtures examined were capable of inducing increases in average cell lipid content, with variations in the distribution of the response, in cytotoxicity and in how the sterol-lipid combination interacted with other activating factors. For example, cholesterol and lipopolysaccharide acted synergistically to increase cell lipid content while also increasing cell survival compared with the addition of lipopolysaccharide alone. Additionally, ergosterol and cholesteryl hemisuccinate caused similar increases in lipid content but also exhibited considerably greater cytotoxicity than cholesterol. The use of automated image analysis enables us to assess not only changes in average cell size and content, but also to rapidly and automatically compare population distributions based on simple fluorescence images. Our observations add to increasing understanding of the complex and multifactorial nature of foam-cell formation and provide a novel approach to assessing the heterogeneity of macrophage response to a variety of factors.

Nano-imaging of single cells using STIM

Energy Technology Data Exchange (ETDEWEB)

Ren Minqin [Centre for Ion Beam Applications (CIBA), Department of Physics, National University of Singapore, Singapore 117542 (Singapore); Department of Biochemistry, National University of Singapore (Singapore); Kan, J.A. van [Centre for Ion Beam Applications (CIBA), Department of Physics, National University of Singapore, Singapore 117542 (Singapore); Bettiol, A.A. [Centre for Ion Beam Applications (CIBA), Department of Physics, National University of Singapore, Singapore 117542 (Singapore); Daina, Lim [Department of Anatomy, National University of Singapore (Singapore); Gek, Chan Yee [Department of Anatomy, National University of Singapore (Singapore); Huat, Bay Boon [Department of Anatomy, National University of Singapore (Singapore); Whitlow, H.J. [Department of Physics, University of Jyvaskyla, P.O. Box 35 (YFL), FIN-40014 (Finland); Osipowicz, T. [Centre for Ion Beam Applications (CIBA), Department of Physics, National University of Singapore, Singapore 117542 (Singapore); Watt, F. [Centre for Ion Beam Applications (CIBA), Department of Physics, National University of Singapore, Singapore 117542 (Singapore)]. E-mail: phywattf@nus.edu.sg

2007-07-15

Scanning transmission ion microscopy (STIM) is a technique which utilizes the energy loss of high energy (MeV) ions passing through a sample to provide structural images. In this paper, we have successfully demonstrated STIM imaging of single cells at the nano-level using the high resolution capability of the proton beam writing facility at the Centre for Ion Beam Applications, National University of Singapore. MCF-7 breast cancer cells (American Type Culture Collection [ATCC]) were seeded on to silicon nitride windows, backed by a Hamamatsu pin diode acting as a particle detector. A reasonable contrast was obtained using 1 MeV protons and excellent contrast obtained using 1 MeV alpha particles. In a further experiment, nano-STIM was also demonstrated using cells seeded on to the pin diode directly, and high quality nano-STIM images showing the nucleus and multiple nucleoli were extracted before the detector was significantly damaged.

Video-rate confocal microscopy for single-molecule imaging in live cells and superresolution fluorescence imaging.

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Lee, Jinwoo; Miyanaga, Yukihiro; Ueda, Masahiro; Hohng, Sungchul

2012-10-17

There is no confocal microscope optimized for single-molecule imaging in live cells and superresolution fluorescence imaging. By combining the swiftness of the line-scanning method and the high sensitivity of wide-field detection, we have developed a, to our knowledge, novel confocal fluorescence microscope with a good optical-sectioning capability (1.0 μm), fast frame rates (fluorescence detection efficiency. Full compatibility of the microscope with conventional cell-imaging techniques allowed us to do single-molecule imaging with a great ease at arbitrary depths of live cells. With the new microscope, we monitored diffusion motion of fluorescently labeled cAMP receptors of Dictyostelium discoideum at both the basal and apical surfaces and obtained superresolution fluorescence images of microtubules of COS-7 cells at depths in the range 0-85 μm from the surface of a coverglass. Copyright © 2012 Biophysical Society. Published by Elsevier Inc. All rights reserved.

Automated measurement of cell motility and proliferation

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Goff Julie

2005-04-01

Full Text Available Abstract Background Time-lapse microscopic imaging provides a powerful approach for following changes in cell phenotype over time. Visible responses of whole cells can yield insight into functional changes that underlie physiological processes in health and disease. For example, features of cell motility accompany molecular changes that are central to the immune response, to carcinogenesis and metastasis, to wound healing and tissue regeneration, and to the myriad developmental processes that generate an organism. Previously reported image processing methods for motility analysis required custom viewing devices and manual interactions that may introduce bias, that slow throughput, and that constrain the scope of experiments in terms of the number of treatment variables, time period of observation, replication and statistical options. Here we describe a fully automated system in which images are acquired 24/7 from 384 well plates and are automatically processed to yield high-content motility and morphological data. Results We have applied this technology to study the effects of different extracellular matrix compounds on human osteoblast-like cell lines to explore functional changes that may underlie processes involved in bone formation and maintenance. We show dose-response and kinetic data for induction of increased motility by laminin and collagen type I without significant effects on growth rate. Differential motility response was evident within 4 hours of plating cells; long-term responses differed depending upon cell type and surface coating. Average velocities were increased approximately 0.1 um/min by ten-fold increases in laminin coating concentration in some cases. Comparison with manual tracking demonstrated the accuracy of the automated method and highlighted the comparative imprecision of human tracking for analysis of cell motility data. Quality statistics are reported that associate with stage noise, interference by non-cell

High resolution imaging of surface patterns of single bacterial cells

International Nuclear Information System (INIS)

Greif, Dominik; Wesner, Daniel; Regtmeier, Jan; Anselmetti, Dario

2010-01-01

We systematically studied the origin of surface patterns observed on single Sinorhizobium meliloti bacterial cells by comparing the complementary techniques atomic force microscopy (AFM) and scanning electron microscopy (SEM). Conditions ranged from living bacteria in liquid to fixed bacteria in high vacuum. Stepwise, we applied different sample modifications (fixation, drying, metal coating, etc.) and characterized the observed surface patterns. A detailed analysis revealed that the surface structure with wrinkled protrusions in SEM images were not generated de novo but most likely evolved from similar and naturally present structures on the surface of living bacteria. The influence of osmotic stress to the surface structure of living cells was evaluated and also the contribution of exopolysaccharide and lipopolysaccharide (LPS) by imaging two mutant strains of the bacterium under native conditions. AFM images of living bacteria in culture medium exhibited surface structures of the size of single proteins emphasizing the usefulness of AFM for high resolution cell imaging.

Cell-Detection Technique for Automated Patch Clamping

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McDowell, Mark; Gray, Elizabeth

2008-01-01

A unique and customizable machinevision and image-data-processing technique has been developed for use in automated identification of cells that are optimal for patch clamping. [Patch clamping (in which patch electrodes are pressed against cell membranes) is an electrophysiological technique widely applied for the study of ion channels, and of membrane proteins that regulate the flow of ions across the membranes. Patch clamping is used in many biological research fields such as neurobiology, pharmacology, and molecular biology.] While there exist several hardware techniques for automated patch clamping of cells, very few of those techniques incorporate machine vision for locating cells that are ideal subjects for patch clamping. In contrast, the present technique is embodied in a machine-vision algorithm that, in practical application, enables the user to identify good and bad cells for patch clamping in an image captured by a charge-coupled-device (CCD) camera attached to a microscope, within a processing time of one second. Hence, the present technique can save time, thereby increasing efficiency and reducing cost. The present technique involves the utilization of cell-feature metrics to accurately make decisions on the degree to which individual cells are "good" or "bad" candidates for patch clamping. These metrics include position coordinates (x,y) in the image plane, major-axis length, minor-axis length, area, elongation, roundness, smoothness, angle of orientation, and degree of inclusion in the field of view. The present technique does not require any special hardware beyond commercially available, off-the-shelf patch-clamping hardware: A standard patchclamping microscope system with an attached CCD camera, a personal computer with an imagedata- processing board, and some experience in utilizing imagedata- processing software are all that are needed. A cell image is first captured by the microscope CCD camera and image-data-processing board, then the image

A precise pointing nanopipette for single-cell imaging via electroosmotic injection.

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Lv, Jian; Qian, Ruo-Can; Hu, Yong-Xu; Liu, Shao-Chuang; Cao, Yue; Zheng, Yong-Jie; Long, Yi-Tao

2016-11-24

The precise transportation of fluorescent probes to the designated location in living cells is still a challenge. Here, we present a new addition to nanopipettes as a powerful tool to deliver fluorescent molecules to a given place in a single cell by electroosmotic flow, indicating favorable potential for further application in single-cell imaging.

High-recovery visual identification and single-cell retrieval of circulating tumor cells for genomic analysis using a dual-technology platform integrated with automated immunofluorescence staining

International Nuclear Information System (INIS)

Campton, Daniel E; Ramirez, Arturo B; Nordberg, Joshua J; Drovetto, Nick; Clein, Alisa C; Varshavskaya, Paulina; Friemel, Barry H; Quarre, Steve; Breman, Amy; Dorschner, Michael; Blau, Sibel; Blau, C Anthony; Sabath, Daniel E; Stilwell, Jackie L; Kaldjian, Eric P

2015-01-01

Circulating tumor cells (CTCs) are malignant cells that have migrated from solid cancers into the blood, where they are typically present in rare numbers. There is great interest in using CTCs to monitor response to therapies, to identify clinically actionable biomarkers, and to provide a non-invasive window on the molecular state of a tumor. Here we characterize the performance of the AccuCyte® – CyteFinder® system, a comprehensive, reproducible and highly sensitive platform for collecting, identifying and retrieving individual CTCs from microscopic slides for molecular analysis after automated immunofluorescence staining for epithelial markers. All experiments employed a density-based cell separation apparatus (AccuCyte) to separate nucleated cells from the blood and transfer them to microscopic slides. After staining, the slides were imaged using a digital scanning microscope (CyteFinder). Precisely counted model CTCs (mCTCs) from four cancer cell lines were spiked into whole blood to determine recovery rates. Individual mCTCs were removed from slides using a single-cell retrieval device (CytePicker™) for whole genome amplification and subsequent analysis by PCR and Sanger sequencing, whole exome sequencing, or array-based comparative genomic hybridization. Clinical CTCs were evaluated in blood samples from patients with different cancers in comparison with the CellSearch® system. AccuCyte – CyteFinder presented high-resolution images that allowed identification of mCTCs by morphologic and phenotypic features. Spike-in mCTC recoveries were between 90 and 91%. More than 80% of single-digit spike-in mCTCs were identified and even a single cell in 7.5 mL could be found. Analysis of single SKBR3 mCTCs identified presence of a known TP53 mutation by both PCR and whole exome sequencing, and confirmed the reported karyotype of this cell line. Patient sample CTC counts matched or exceeded CellSearch CTC counts in a small feasibility cohort. The AccuCyte �

Towards automated segmentation of cells and cell nuclei in nonlinear optical microscopy.

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Medyukhina, Anna; Meyer, Tobias; Schmitt, Michael; Romeike, Bernd F M; Dietzek, Benjamin; Popp, Jürgen

2012-11-01

Nonlinear optical (NLO) imaging techniques based e.g. on coherent anti-Stokes Raman scattering (CARS) or two photon excited fluorescence (TPEF) show great potential for biomedical imaging. In order to facilitate the diagnostic process based on NLO imaging, there is need for an automated calculation of quantitative values such as cell density, nucleus-to-cytoplasm ratio, average nuclear size. Extraction of these parameters is helpful for the histological assessment in general and specifically e.g. for the determination of tumor grades. This requires an accurate image segmentation and detection of locations and boundaries of cells and nuclei. Here we present an image processing approach for the detection of nuclei and cells in co-registered TPEF and CARS images. The algorithm developed utilizes the gray-scale information for the detection of the nuclei locations and the gradient information for the delineation of the nuclear and cellular boundaries. The approach reported is capable for an automated segmentation of cells and nuclei in multimodal TPEF-CARS images of human brain tumor samples. The results are important for the development of NLO microscopy into a clinically relevant diagnostic tool. Copyright © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Automated image based prominent nucleoli detection.

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Yap, Choon K; Kalaw, Emarene M; Singh, Malay; Chong, Kian T; Giron, Danilo M; Huang, Chao-Hui; Cheng, Li; Law, Yan N; Lee, Hwee Kuan

2015-01-01

Nucleolar changes in cancer cells are one of the cytologic features important to the tumor pathologist in cancer assessments of tissue biopsies. However, inter-observer variability and the manual approach to this work hamper the accuracy of the assessment by pathologists. In this paper, we propose a computational method for prominent nucleoli pattern detection. Thirty-five hematoxylin and eosin stained images were acquired from prostate cancer, breast cancer, renal clear cell cancer and renal papillary cell cancer tissues. Prostate cancer images were used for the development of a computer-based automated prominent nucleoli pattern detector built on a cascade farm. An ensemble of approximately 1000 cascades was constructed by permuting different combinations of classifiers such as support vector machines, eXclusive component analysis, boosting, and logistic regression. The output of cascades was then combined using the RankBoost algorithm. The output of our prominent nucleoli pattern detector is a ranked set of detected image patches of patterns of prominent nucleoli. The mean number of detected prominent nucleoli patterns in the top 100 ranked detected objects was 58 in the prostate cancer dataset, 68 in the breast cancer dataset, 86 in the renal clear cell cancer dataset, and 76 in the renal papillary cell cancer dataset. The proposed cascade farm performs twice as good as the use of a single cascade proposed in the seminal paper by Viola and Jones. For comparison, a naive algorithm that randomly chooses a pixel as a nucleoli pattern would detect five correct patterns in the first 100 ranked objects. Detection of sparse nucleoli patterns in a large background of highly variable tissue patterns is a difficult challenge our method has overcome. This study developed an accurate prominent nucleoli pattern detector with the potential to be used in the clinical settings.

Automated image based prominent nucleoli detection

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Choon K Yap

2015-01-01

Full Text Available Introduction: Nucleolar changes in cancer cells are one of the cytologic features important to the tumor pathologist in cancer assessments of tissue biopsies. However, inter-observer variability and the manual approach to this work hamper the accuracy of the assessment by pathologists. In this paper, we propose a computational method for prominent nucleoli pattern detection. Materials and Methods: Thirty-five hematoxylin and eosin stained images were acquired from prostate cancer, breast cancer, renal clear cell cancer and renal papillary cell cancer tissues. Prostate cancer images were used for the development of a computer-based automated prominent nucleoli pattern detector built on a cascade farm. An ensemble of approximately 1000 cascades was constructed by permuting different combinations of classifiers such as support vector machines, eXclusive component analysis, boosting, and logistic regression. The output of cascades was then combined using the RankBoost algorithm. The output of our prominent nucleoli pattern detector is a ranked set of detected image patches of patterns of prominent nucleoli. Results: The mean number of detected prominent nucleoli patterns in the top 100 ranked detected objects was 58 in the prostate cancer dataset, 68 in the breast cancer dataset, 86 in the renal clear cell cancer dataset, and 76 in the renal papillary cell cancer dataset. The proposed cascade farm performs twice as good as the use of a single cascade proposed in the seminal paper by Viola and Jones. For comparison, a naive algorithm that randomly chooses a pixel as a nucleoli pattern would detect five correct patterns in the first 100 ranked objects. Conclusions: Detection of sparse nucleoli patterns in a large background of highly variable tissue patterns is a difficult challenge our method has overcome. This study developed an accurate prominent nucleoli pattern detector with the potential to be used in the clinical settings.

Automated Tracking of Cell Migration with Rapid Data Analysis.

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DuChez, Brian J

2017-09-01

Cell migration is essential for many biological processes including development, wound healing, and metastasis. However, studying cell migration often requires the time-consuming and labor-intensive task of manually tracking cells. To accelerate the task of obtaining coordinate positions of migrating cells, we have developed a graphical user interface (GUI) capable of automating the tracking of fluorescently labeled nuclei. This GUI provides an intuitive user interface that makes automated tracking accessible to researchers with no image-processing experience or familiarity with particle-tracking approaches. Using this GUI, users can interactively determine a minimum of four parameters to identify fluorescently labeled cells and automate acquisition of cell trajectories. Additional features allow for batch processing of numerous time-lapse images, curation of unwanted tracks, and subsequent statistical analysis of tracked cells. Statistical outputs allow users to evaluate migratory phenotypes, including cell speed, distance, displacement, and persistence, as well as measures of directional movement, such as forward migration index (FMI) and angular displacement. © 2017 by John Wiley & Sons, Inc. Copyright © 2017 John Wiley & Sons, Inc.

Fast automatic quantitative cell replication with fluorescent live cell imaging

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Wang Ching-Wei

2012-01-01

Full Text Available Abstract Background live cell imaging is a useful tool to monitor cellular activities in living systems. It is often necessary in cancer research or experimental research to quantify the dividing capabilities of cells or the cell proliferation level when investigating manipulations of the cells or their environment. Manual quantification of fluorescence microscopic image is difficult because human is neither sensitive to fine differences in color intensity nor effective to count and average fluorescence level among cells. However, auto-quantification is not a straightforward problem to solve. As the sampling location of the microscopy changes, the amount of cells in individual microscopic images varies, which makes simple measurement methods such as the sum of stain intensity values or the total number of positive stain within each image inapplicable. Thus, automated quantification with robust cell segmentation techniques is required. Results An automated quantification system with robust cell segmentation technique are presented. The experimental results in application to monitor cellular replication activities show that the quantitative score is promising to represent the cell replication level, and scores for images from different cell replication groups are demonstrated to be statistically significantly different using ANOVA, LSD and Tukey HSD tests (p-value Conclusion A robust automated quantification method of live cell imaging is built to measure the cell replication level, providing a robust quantitative analysis system in fluorescent live cell imaging. In addition, the presented unsupervised entropy based cell segmentation for live cell images is demonstrated to be also applicable for nuclear segmentation of IHC tissue images.

Automated Micro-Object Detection for Mobile Diagnostics Using Lens-Free Imaging Technology

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Mohendra Roy

2016-05-01

Full Text Available Lens-free imaging technology has been extensively used recently for microparticle and biological cell analysis because of its high throughput, low cost, and simple and compact arrangement. However, this technology still lacks a dedicated and automated detection system. In this paper, we describe a custom-developed automated micro-object detection method for a lens-free imaging system. In our previous work (Roy et al., we developed a lens-free imaging system using low-cost components. This system was used to generate and capture the diffraction patterns of micro-objects and a global threshold was used to locate the diffraction patterns. In this work we used the same setup to develop an improved automated detection and analysis algorithm based on adaptive threshold and clustering of signals. For this purpose images from the lens-free system were then used to understand the features and characteristics of the diffraction patterns of several types of samples. On the basis of this information, we custom-developed an automated algorithm for the lens-free imaging system. Next, all the lens-free images were processed using this custom-developed automated algorithm. The performance of this approach was evaluated by comparing the counting results with standard optical microscope results. We evaluated the counting results for polystyrene microbeads, red blood cells, and HepG2, HeLa, and MCF7 cells. The comparison shows good agreement between the systems, with a correlation coefficient of 0.91 and linearity slope of 0.877. We also evaluated the automated size profiles of the microparticle samples. This Wi-Fi-enabled lens-free imaging system, along with the dedicated software, possesses great potential for telemedicine applications in resource-limited settings.

Simultaneous live cell imaging using dual FRET sensors with a single excitation light.

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Yusuke Niino

Full Text Available Fluorescence resonance energy transfer (FRET between fluorescent proteins is a powerful tool for visualization of signal transduction in living cells, and recently, some strategies for imaging of dual FRET pairs in a single cell have been reported. However, these necessitate alteration of excitation light between two different wavelengths to avoid the spectral overlap, resulting in sequential detection with a lag time. Thus, to follow fast signal dynamics or signal changes in highly motile cells, a single-excitation dual-FRET method should be required. Here we reported this by using four-color imaging with a single excitation light and subsequent linear unmixing to distinguish fluorescent proteins. We constructed new FRET sensors with Sapphire/RFP to combine with CFP/YFP, and accomplished simultaneous imaging of cAMP and cGMP in single cells. We confirmed that signal amplitude of our dual FRET measurement is comparable to of conventional single FRET measurement. Finally, we demonstrated to monitor both intracellular Ca(2+ and cAMP in highly motile cardiac myocytes. To cancel out artifacts caused by the movement of the cell, this method expands the applicability of the combined use of dual FRET sensors for cell samples with high motility.

Fluorescent metal nanoshell and CK19 detection on single cell image

International Nuclear Information System (INIS)

Zhang, Jian; Fu, Yi; Li, Ge; Lakowicz, Joseph R.; Zhao, Richard Y.

2011-01-01

Highlights: → Novel metal nanoshell as fluorescence imaging agent. → Fluorescent mAb-metal complex with enhanced intensity and shortened lifetime. → Immuno-interactions of mAb-metal complexes with CK19 molecules on CNCAP and HeLa cell surfaces. → Isolation of conjugated mAb-metal complexes from cellular autofluorescence on cell image. -- Abstract: In this article, we report the synthesis strategy and optical properties of a novel type of fluorescence metal nanoshell when it was used as imaging agent for fluorescence cell imaging. The metal nanoshells were made with 40 nm silica cores and 10 nm silver shells. Unlike typical fluorescence metal nanoshells which contain the organic dyes in the cores, novel metal nanoshells were composed of Cy5-labelled monoclonal anti-CK19 antibodies (mAbs) on the external surfaces of shells. Optical measurements to the single nanoparticles showed that in comparison with the metal free labelled mAbs, the mAb-Ag complexes displayed significantly enhanced emission intensity and dramatically shortened lifetime due to near-field interactions of fluorophores with metal. These metal nanoshells were found to be able to immunoreact with target cytokeratin 19 (CK19) molecules on the surfaces of LNCAP and HeLa cells. Fluorescence cell images were recorded on a time-resolved confocal microscope. The emissions from the metal nanoprobes could be clearly isolated from the cellular autofluorescence backgrounds on the cell images as either individuals or small clusters due to their stronger emission intensities and shorter lifetimes. These emission signals could also be precisely counted on single cell images. The count number may provide an approach for quantifying the target molecules in the cells.

Empirical gradient threshold technique for automated segmentation across image modalities and cell lines.

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Chalfoun, J; Majurski, M; Peskin, A; Breen, C; Bajcsy, P; Brady, M

2015-10-01

New microscopy technologies are enabling image acquisition of terabyte-sized data sets consisting of hundreds of thousands of images. In order to retrieve and analyze the biological information in these large data sets, segmentation is needed to detect the regions containing cells or cell colonies. Our work with hundreds of large images (each 21,000×21,000 pixels) requires a segmentation method that: (1) yields high segmentation accuracy, (2) is applicable to multiple cell lines with various densities of cells and cell colonies, and several imaging modalities, (3) can process large data sets in a timely manner, (4) has a low memory footprint and (5) has a small number of user-set parameters that do not require adjustment during the segmentation of large image sets. None of the currently available segmentation methods meet all these requirements. Segmentation based on image gradient thresholding is fast and has a low memory footprint. However, existing techniques that automate the selection of the gradient image threshold do not work across image modalities, multiple cell lines, and a wide range of foreground/background densities (requirement 2) and all failed the requirement for robust parameters that do not require re-adjustment with time (requirement 5). We present a novel and empirically derived image gradient threshold selection method for separating foreground and background pixels in an image that meets all the requirements listed above. We quantify the difference between our approach and existing ones in terms of accuracy, execution speed, memory usage and number of adjustable parameters on a reference data set. This reference data set consists of 501 validation images with manually determined segmentations and image sizes ranging from 0.36 Megapixels to 850 Megapixels. It includes four different cell lines and two image modalities: phase contrast and fluorescent. Our new technique, called Empirical Gradient Threshold (EGT), is derived from this reference

AUTOMATION OF IMAGE DATA PROCESSING

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Preuss Ryszard

2014-12-01

Full Text Available This article discusses the current capabilities of automate processing of the image data on the example of using PhotoScan software by Agisoft . At present, image data obtained by various registration systems (metric and non - metric cameras placed on airplanes , satellites , or more often on UAVs is used to create photogrammetric products. Multiple registrations of object or land area (large groups of photos are captured are usually performed in order to eliminate obscured area as well as to raise the final accuracy of the photogrammetric product. Because of such a situation t he geometry of the resulting image blocks is far from the typical configuration of images . For fast images georeferencing automatic image matching algorithms are currently applied . They can create a model of a block in the local coordinate system or using initial exterior orientation and measured control points can provide image georeference in an external reference frame. In the case of non - metric image application, it is also possible to carry out self - calibration process at this stage . Image matching algorithm is also used in generation of dense point clouds reconstructing spatial shape of the object ( area. In subsequent processing steps it is possible to obtain typical photogrammetric products such as orthomosaic , DSM or DTM and a photorealistic solid model of an object . All aforementioned processing steps are implemented in a single program in contrary to standard commercial software dividing all steps into dedicated modules . I mage processing leading to final geo referenced products can be fully automated including sequential implementation of the processing steps at predetermined control parameters . The paper presents the practical results of the application fully automatic generation of othomosaic for both images obtained by a metric Vexell camera and a block of images acquired by a non - metric UAV system.

Automated X-ray image analysis for cargo security: Critical review and future promise.

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Rogers, Thomas W; Jaccard, Nicolas; Morton, Edward J; Griffin, Lewis D

2017-01-01

We review the relatively immature field of automated image analysis for X-ray cargo imagery. There is increasing demand for automated analysis methods that can assist in the inspection and selection of containers, due to the ever-growing volumes of traded cargo and the increasing concerns that customs- and security-related threats are being smuggled across borders by organised crime and terrorist networks. We split the field into the classical pipeline of image preprocessing and image understanding. Preprocessing includes: image manipulation; quality improvement; Threat Image Projection (TIP); and material discrimination and segmentation. Image understanding includes: Automated Threat Detection (ATD); and Automated Contents Verification (ACV). We identify several gaps in the literature that need to be addressed and propose ideas for future research. Where the current literature is sparse we borrow from the single-view, multi-view, and CT X-ray baggage domains, which have some characteristics in common with X-ray cargo.

Intravital imaging of cardiac function at the single-cell level.

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Aguirre, Aaron D; Vinegoni, Claudio; Sebas, Matt; Weissleder, Ralph

2014-08-05

Knowledge of cardiomyocyte biology is limited by the lack of methods to interrogate single-cell physiology in vivo. Here we show that contracting myocytes can indeed be imaged with optical microscopy at high temporal and spatial resolution in the beating murine heart, allowing visualization of individual sarcomeres and measurement of the single cardiomyocyte contractile cycle. Collectively, this has been enabled by efficient tissue stabilization, a prospective real-time cardiac gating approach, an image processing algorithm for motion-artifact-free imaging throughout the cardiac cycle, and a fluorescent membrane staining protocol. Quantification of cardiomyocyte contractile function in vivo opens many possibilities for investigating myocardial disease and therapeutic intervention at the cellular level.

A fully automated cell segmentation and morphometric parameter system for quantifying corneal endothelial cell morphology.

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Al-Fahdawi, Shumoos; Qahwaji, Rami; Al-Waisy, Alaa S; Ipson, Stanley; Ferdousi, Maryam; Malik, Rayaz A; Brahma, Arun

2018-07-01

Corneal endothelial cell abnormalities may be associated with a number of corneal and systemic diseases. Damage to the endothelial cells can significantly affect corneal transparency by altering hydration of the corneal stroma, which can lead to irreversible endothelial cell pathology requiring corneal transplantation. To date, quantitative analysis of endothelial cell abnormalities has been manually performed by ophthalmologists using time consuming and highly subjective semi-automatic tools, which require an operator interaction. We developed and applied a fully-automated and real-time system, termed the Corneal Endothelium Analysis System (CEAS) for the segmentation and computation of endothelial cells in images of the human cornea obtained by in vivo corneal confocal microscopy. First, a Fast Fourier Transform (FFT) Band-pass filter is applied to reduce noise and enhance the image quality to make the cells more visible. Secondly, endothelial cell boundaries are detected using watershed transformations and Voronoi tessellations to accurately quantify the morphological parameters of the human corneal endothelial cells. The performance of the automated segmentation system was tested against manually traced ground-truth images based on a database consisting of 40 corneal confocal endothelial cell images in terms of segmentation accuracy and obtained clinical features. In addition, the robustness and efficiency of the proposed CEAS system were compared with manually obtained cell densities using a separate database of 40 images from controls (n = 11), obese subjects (n = 16) and patients with diabetes (n = 13). The Pearson correlation coefficient between automated and manual endothelial cell densities is 0.9 (p system, and the possibility of utilizing it in a real world clinical setting to enable rapid diagnosis and for patient follow-up, with an execution time of only 6 seconds per image. Copyright © 2018 Elsevier B.V. All rights reserved.

Automated data collection in single particle electron microscopy

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Tan, Yong Zi; Cheng, Anchi; Potter, Clinton S.; Carragher, Bridget

2016-01-01

Automated data collection is an integral part of modern workflows in single particle electron microscopy (EM) research. This review surveys the software packages available for automated single particle EM data collection. The degree of automation at each stage of data collection is evaluated, and the capabilities of the software packages are described. Finally, future trends in automation are discussed. PMID:26671944

Fuzzy Emotional Semantic Analysis and Automated Annotation of Scene Images

Directory of Open Access Journals (Sweden)

Jianfang Cao

2015-01-01

Full Text Available With the advances in electronic and imaging techniques, the production of digital images has rapidly increased, and the extraction and automated annotation of emotional semantics implied by images have become issues that must be urgently addressed. To better simulate human subjectivity and ambiguity for understanding scene images, the current study proposes an emotional semantic annotation method for scene images based on fuzzy set theory. A fuzzy membership degree was calculated to describe the emotional degree of a scene image and was implemented using the Adaboost algorithm and a back-propagation (BP neural network. The automated annotation method was trained and tested using scene images from the SUN Database. The annotation results were then compared with those based on artificial annotation. Our method showed an annotation accuracy rate of 91.2% for basic emotional values and 82.4% after extended emotional values were added, which correspond to increases of 5.5% and 8.9%, respectively, compared with the results from using a single BP neural network algorithm. Furthermore, the retrieval accuracy rate based on our method reached approximately 89%. This study attempts to lay a solid foundation for the automated emotional semantic annotation of more types of images and therefore is of practical significance.

Automated analysis of angle closure from anterior chamber angle images.

Science.gov (United States)

Baskaran, Mani; Cheng, Jun; Perera, Shamira A; Tun, Tin A; Liu, Jiang; Aung, Tin

2014-10-21

To evaluate a novel software capable of automatically grading angle closure on EyeCam angle images in comparison with manual grading of images, with gonioscopy as the reference standard. In this hospital-based, prospective study, subjects underwent gonioscopy by a single observer, and EyeCam imaging by a different operator. The anterior chamber angle in a quadrant was classified as closed if the posterior trabecular meshwork could not be seen. An eye was classified as having angle closure if there were two or more quadrants of closure. Automated grading of the angle images was performed using customized software. Agreement between the methods was ascertained by κ statistic and comparison of area under receiver operating characteristic curves (AUC). One hundred forty subjects (140 eyes) were included, most of whom were Chinese (102/140, 72.9%) and women (72/140, 51.5%). Angle closure was detected in 61 eyes (43.6%) with gonioscopy in comparison with 59 eyes (42.1%, P = 0.73) using manual grading, and 67 eyes (47.9%, P = 0.24) with automated grading of EyeCam images. The agreement for angle closure diagnosis between gonioscopy and both manual (κ = 0.88; 95% confidence interval [CI), 0.81-0.96) and automated grading of EyeCam images was good (κ = 0.74; 95% CI, 0.63-0.85). The AUC for detecting eyes with gonioscopic angle closure was comparable for manual and automated grading (AUC 0.974 vs. 0.954, P = 0.31) of EyeCam images. Customized software for automated grading of EyeCam angle images was found to have good agreement with gonioscopy. Human observation of the EyeCam images may still be needed to avoid gross misclassification, especially in eyes with extensive angle closure. Copyright 2014 The Association for Research in Vision and Ophthalmology, Inc.

An automated quantitative DNA image cytometry system detects abnormal cells in cervical cytology with high sensitivity.

Science.gov (United States)

Wong, O G; Ho, M W; Tsun, O K; Ng, A K; Tsui, E Y; Chow, J N; Ip, P P; Cheung, A N

2018-03-26

To evaluate the performance of an automated DNA-image-cytometry system as a tool to detect cervical carcinoma. Of 384 liquid-based cervical cytology samples with available biopsy follow-up were analyzed by both the Imager System and a high-risk HPV test (Cobas). The sensitivity and specificity of Imager System for detecting biopsy proven high-grade squamous intraepithelial lesion (HSIL, cervical intraepithelial neoplasia [CIN]2-3) and carcinoma were 89.58% and 56.25%, respectively, compared to 97.22% and 23.33% of HPV test but additional HPV 16/18 genotyping increased the specificity to 69.58%. The sensitivity and specificity of the Imager System for predicting HSIL+ (CIN2-3+) lesions among atypical squamous cells of undetermined significance samples were 80.00% and 70.53%, respectively, compared to 100% and 11.58% of HPV test whilst the HPV 16/18 genotyping increased the specificity to 77.89%. Among atypical squamous cells-cannot exclude HSIL, the sensitivity and specificity of Imager System for predicting HSIL+ (CIN2-3+) lesions upon follow up were 82.86% and 33.33%%, respectively, compared to 97.14% and 4.76% of HPV test and the HPV 16/18 genotyping increased the specificity to 19.05%. Among low-grade squamous intraepithelial lesion cases, the sensitivity and specificity of the Imager System for predicting HSIL+ (CIN2-3+) lesions were 66.67% and 35.71%%, respectively, compared to 66.67% and 29.76% of HPV test while HPV 16/18 genotyping increased the specificity to 79.76%. The overall results of imager and high-risk HPV test agreed in 69.43% (268) of all samples. The automated imager system and HPV 16/18 genotyping can enhance the specificity of detecting HSIL+ (CIN2-3+) lesions. © 2018 John Wiley & Sons Ltd.

Visualization of metallodrugs in single cells by secondary ion mass spectrometry imaging.

Science.gov (United States)

Wu, Kui; Jia, Feifei; Zheng, Wei; Luo, Qun; Zhao, Yao; Wang, Fuyi

2017-07-01

Secondary ion mass spectrometry, including nanoscale secondary ion mass spectrometry (NanoSIMS) and time-of-flight secondary ion mass spectrometry (ToF-SIMS), has emerged as a powerful tool for biological imaging, especially for single cell imaging. SIMS imaging can provide information on subcellular distribution of endogenous and exogenous chemicals, including metallodrugs, from membrane through to cytoplasm and nucleus without labeling, and with high spatial resolution and chemical specificity. In this mini-review, we summarize recent progress in the field of SIMS imaging, particularly in the characterization of the subcellular distribution of metallodrugs. We anticipate that the SIMS imaging method will be widely applied to visualize subcellular distributions of drugs and drug candidates in single cells, exerting significant influence on early drug evaluation and metabolism in medicinal and pharmaceutical chemistry. Recent progress of SIMS applications in characterizing the subcellular distributions of metallodrugs was summarized.

Single-cell and subcellular pharmacokinetic imaging allows insight into drug action in vivo.

Science.gov (United States)

Thurber, Greg M; Yang, Katy S; Reiner, Thomas; Kohler, Rainer H; Sorger, Peter; Mitchison, Tim; Weissleder, Ralph

2013-01-01

Pharmacokinetic analysis at the organ level provides insight into how drugs distribute throughout the body, but cannot explain how drugs work at the cellular level. Here we demonstrate in vivo single-cell pharmacokinetic imaging of PARP-1 inhibitors and model drug behaviour under varying conditions. We visualize intracellular kinetics of the PARP-1 inhibitor distribution in real time, showing that PARP-1 inhibitors reach their cellular target compartment, the nucleus, within minutes in vivo both in cancer and normal cells in various cancer models. We also use these data to validate predictive finite element modelling. Our theoretical and experimental data indicate that tumour cells are exposed to sufficiently high PARP-1 inhibitor concentrations in vivo and suggest that drug inefficiency is likely related to proteomic heterogeneity or insensitivity of cancer cells to DNA-repair inhibition. This suggests that single-cell pharmacokinetic imaging and derived modelling improve our understanding of drug action at single-cell resolution in vivo.

An automated cell-counting algorithm for fluorescently-stained cells in migration assays

Directory of Open Access Journals (Sweden)

Novielli Nicole M

2011-10-01

Full Text Available Abstract A cell-counting algorithm, developed in Matlab®, was created to efficiently count migrated fluorescently-stained cells on membranes from migration assays. At each concentration of cells used (10,000, and 100,000 cells, images were acquired at 2.5 ×, 5 ×, and 10 × objective magnifications. Automated cell counts strongly correlated to manual counts (r2 = 0.99, P

Comparison of manual & automated analysis methods for corneal endothelial cell density measurements by specular microscopy.

Science.gov (United States)

Huang, Jianyan; Maram, Jyotsna; Tepelus, Tudor C; Modak, Cristina; Marion, Ken; Sadda, SriniVas R; Chopra, Vikas; Lee, Olivia L

2017-08-07

To determine the reliability of corneal endothelial cell density (ECD) obtained by automated specular microscopy versus that of validated manual methods and factors that predict such reliability. Sharp central images from 94 control and 106 glaucomatous eyes were captured with Konan specular microscope NSP-9900. All images were analyzed by trained graders using Konan CellChek Software, employing the fully- and semi-automated methods as well as Center Method. Images with low cell count (input cells number <100) and/or guttata were compared with the Center and Flex-Center Methods. ECDs were compared and absolute error was used to assess variation. The effect on ECD of age, cell count, cell size, and cell size variation was evaluated. No significant difference was observed between the Center and Flex-Center Methods in corneas with guttata (p=0.48) or low ECD (p=0.11). No difference (p=0.32) was observed in ECD of normal controls <40 yrs old between the fully-automated method and manual Center Method. However, in older controls and glaucomatous eyes, ECD was overestimated by the fully-automated method (p=0.034) and semi-automated method (p=0.025) as compared to manual method. Our findings show that automated analysis significantly overestimates ECD in the eyes with high polymegathism and/or large cell size, compared to the manual method. Therefore, we discourage reliance upon the fully-automated method alone to perform specular microscopy analysis, particularly if an accurate ECD value is imperative. Copyright © 2017. Published by Elsevier España, S.L.U.

Identification and red blood cell automated counting from blood smear images using computer-aided system.

Science.gov (United States)

Acharya, Vasundhara; Kumar, Preetham

2018-03-01

Red blood cell count plays a vital role in identifying the overall health of the patient. Hospitals use the hemocytometer to count the blood cells. Conventional method of placing the smear under microscope and counting the cells manually lead to erroneous results, and medical laboratory technicians are put under stress. A computer-aided system will help to attain precise results in less amount of time. This research work proposes an image-processing technique for counting the number of red blood cells. It aims to examine and process the blood smear image, in order to support the counting of red blood cells and identify the number of normal and abnormal cells in the image automatically. K-medoids algorithm which is robust to external noise is used to extract the WBCs from the image. Granulometric analysis is used to separate the red blood cells from the white blood cells. The red blood cells obtained are counted using the labeling algorithm and circular Hough transform. The radius range for the circle-drawing algorithm is estimated by computing the distance of the pixels from the boundary which automates the entire algorithm. A comparison is done between the counts obtained using the labeling algorithm and circular Hough transform. Results of the work showed that circular Hough transform was more accurate in counting the red blood cells than the labeling algorithm as it was successful in identifying even the overlapping cells. The work also intends to compare the results of cell count done using the proposed methodology and manual approach. The work is designed to address all the drawbacks of the previous research work. The research work can be extended to extract various texture and shape features of abnormal cells identified so that diseases like anemia of inflammation and chronic disease can be detected at the earliest.

High-content analysis of single cells directly assembled on CMOS sensor based on color imaging.

Science.gov (United States)

Tanaka, Tsuyoshi; Saeki, Tatsuya; Sunaga, Yoshihiko; Matsunaga, Tadashi

2010-12-15

A complementary metal oxide semiconductor (CMOS) image sensor was applied to high-content analysis of single cells which were assembled closely or directly onto the CMOS sensor surface. The direct assembling of cell groups on CMOS sensor surface allows large-field (6.66 mm×5.32 mm in entire active area of CMOS sensor) imaging within a second. Trypan blue-stained and non-stained cells in the same field area on the CMOS sensor were successfully distinguished as white- and blue-colored images under white LED light irradiation. Furthermore, the chemiluminescent signals of each cell were successfully visualized as blue-colored images on CMOS sensor only when HeLa cells were placed directly on the micro-lens array of the CMOS sensor. Our proposed approach will be a promising technique for real-time and high-content analysis of single cells in a large-field area based on color imaging. Copyright © 2010 Elsevier B.V. All rights reserved.

Automated segmentation and isolation of touching cell nuclei in cytopathology smear images of pleural effusion using distance transform watershed method

Science.gov (United States)

Win, Khin Yadanar; Choomchuay, Somsak; Hamamoto, Kazuhiko

2017-06-01

The automated segmentation of cell nuclei is an essential stage in the quantitative image analysis of cell nuclei extracted from smear cytology images of pleural fluid. Cell nuclei can indicate cancer as the characteristics of cell nuclei are associated with cells proliferation and malignancy in term of size, shape and the stained color. Nevertheless, automatic nuclei segmentation has remained challenging due to the artifacts caused by slide preparation, nuclei heterogeneity such as the poor contrast, inconsistent stained color, the cells variation, and cells overlapping. In this paper, we proposed a watershed-based method that is capable to segment the nuclei of the variety of cells from cytology pleural fluid smear images. Firstly, the original image is preprocessed by converting into the grayscale image and enhancing by adjusting and equalizing the intensity using histogram equalization. Next, the cell nuclei are segmented using OTSU thresholding as the binary image. The undesirable artifacts are eliminated using morphological operations. Finally, the distance transform based watershed method is applied to isolate the touching and overlapping cell nuclei. The proposed method is tested with 25 Papanicolaou (Pap) stained pleural fluid images. The accuracy of our proposed method is 92%. The method is relatively simple, and the results are very promising.

Automated Method for the Rapid and Precise Estimation of Adherent Cell Culture Characteristics from Phase Contrast Microscopy Images

Science.gov (United States)

Jaccard, Nicolas; Griffin, Lewis D; Keser, Ana; Macown, Rhys J; Super, Alexandre; Veraitch, Farlan S; Szita, Nicolas

2014-01-01

The quantitative determination of key adherent cell culture characteristics such as confluency, morphology, and cell density is necessary for the evaluation of experimental outcomes and to provide a suitable basis for the establishment of robust cell culture protocols. Automated processing of images acquired using phase contrast microscopy (PCM), an imaging modality widely used for the visual inspection of adherent cell cultures, could enable the non-invasive determination of these characteristics. We present an image-processing approach that accurately detects cellular objects in PCM images through a combination of local contrast thresholding and post hoc correction of halo artifacts. The method was thoroughly validated using a variety of cell lines, microscope models and imaging conditions, demonstrating consistently high segmentation performance in all cases and very short processing times (image). Based on the high segmentation performance, it was possible to precisely determine culture confluency, cell density, and the morphology of cellular objects, demonstrating the wide applicability of our algorithm for typical microscopy image processing pipelines. Furthermore, PCM image segmentation was used to facilitate the interpretation and analysis of fluorescence microscopy data, enabling the determination of temporal and spatial expression patterns of a fluorescent reporter. We created a software toolbox (PHANTAST) that bundles all the algorithms and provides an easy to use graphical user interface. Source-code for MATLAB and ImageJ is freely available under a permissive open-source license. Biotechnol. Bioeng. 2014;111: 504–517. © 2013 Wiley Periodicals, Inc. PMID:24037521

Live cell imaging combined with high-energy single-ion microbeam

Science.gov (United States)

Guo, Na; Du, Guanghua; Liu, Wenjing; Guo, Jinlong; Wu, Ruqun; Chen, Hao; Wei, Junzhe

2016-03-01

DNA strand breaks can lead to cell carcinogenesis or cell death if not repaired rapidly and efficiently. An online live cell imaging system was established at the high energy microbeam facility at the Institute of Modern Physics to study early and fast cellular response to DNA damage after high linear energy transfer ion radiation. The HT1080 cells expressing XRCC1-RFP were irradiated with single high energy nickel ions, and time-lapse images of the irradiated cells were obtained online. The live cell imaging analysis shows that strand-break repair protein XRCC1 was recruited to the ion hit position within 20 s in the cells and formed bright foci in the cell nucleus. The fast recruitment of XRCC1 at the ion hits reached a maximum at about 200 s post-irradiation and then was followed by a slower release into the nucleoplasm. The measured dual-exponential kinetics of XRCC1 protein are consistent with the proposed consecutive reaction model, and the measurements obtained that the reaction rate constant of the XRCC1 recruitment to DNA strand break is 1.2 × 10-3 s-1 and the reaction rate constant of the XRCC1 release from the break-XRCC1 complex is 1.2 × 10-2 s-1.

RoboSCell: An automated single cell arraying and analysis instrument

KAUST Repository

Sakaki, Kelly; Foulds, Ian G.; Liu, William; Dechev, Nikolai; Burke, Robert Douglas; Park, Edward

2009-01-01

Single cell research has the potential to revolutionize experimental methods in biomedical sciences and contribute to clinical practices. Recent studies suggest analysis of single cells reveals novel features of intracellular processes, cell-to-cell

Long-term in vivo imaging of multiple organs at the single cell level.

Directory of Open Access Journals (Sweden)

Benny J Chen

Full Text Available Two-photon microscopy has enabled the study of individual cell behavior in live animals. Many organs and tissues cannot be studied, especially longitudinally, because they are located too deep, behind bony structures or too close to the lung and heart. Here we report a novel mouse model that allows long-term single cell imaging of many organs. A wide variety of live tissues were successfully engrafted in the pinna of the mouse ear. Many of these engrafted tissues maintained the normal tissue histology. Using the heart and thymus as models, we further demonstrated that the engrafted tissues functioned as would be expected. Combining two-photon microscopy with fluorescent tracers, we successfully visualized the engrafted tissues at the single cell level in live mice over several months. Four dimensional (three-dimensional (3D plus time information of individual cells was obtained from this imaging. This model makes long-term high resolution 4D imaging of multiple organs possible.

Establishment of automated culture system for murine induced pluripotent stem cells

Directory of Open Access Journals (Sweden)

Koike Hiroyuki

2012-11-01

Full Text Available Abstract Background Induced pluripotent stem (iPS cells can differentiate into any cell type, which makes them an attractive resource in fields such as regenerative medicine, drug screening, or in vitro toxicology. The most important prerequisite for these industrial applications is stable supply and uniform quality of iPS cells. Variation in quality largely results from differences in handling skills between operators in laboratories. To minimize these differences, establishment of an automated iPS cell culture system is necessary. Results We developed a standardized mouse iPS cell maintenance culture, using an automated cell culture system housed in a CO2 incubator commonly used in many laboratories. The iPS cells propagated in a chamber uniquely designed for automated culture and showed specific colony morphology, as for manual culture. A cell detachment device in the system passaged iPS cells automatically by dispersing colonies to single cells. In addition, iPS cells were passaged without any change in colony morphology or expression of undifferentiated stem cell markers during the 4 weeks of automated culture. Conclusions Our results show that use of this compact, automated cell culture system facilitates stable iPS cell culture without obvious effects on iPS cell pluripotency or colony-forming ability. The feasibility of iPS cell culture automation may greatly facilitate the use of this versatile cell source for a variety of biomedical applications.

Automated classification of cell morphology by coherence-controlled holographic microscopy

Science.gov (United States)

Strbkova, Lenka; Zicha, Daniel; Vesely, Pavel; Chmelik, Radim

2017-08-01

In the last few years, classification of cells by machine learning has become frequently used in biology. However, most of the approaches are based on morphometric (MO) features, which are not quantitative in terms of cell mass. This may result in poor classification accuracy. Here, we study the potential contribution of coherence-controlled holographic microscopy enabling quantitative phase imaging for the classification of cell morphologies. We compare our approach with the commonly used method based on MO features. We tested both classification approaches in an experiment with nutritionally deprived cancer tissue cells, while employing several supervised machine learning algorithms. Most of the classifiers provided higher performance when quantitative phase features were employed. Based on the results, it can be concluded that the quantitative phase features played an important role in improving the performance of the classification. The methodology could be valuable help in refining the monitoring of live cells in an automated fashion. We believe that coherence-controlled holographic microscopy, as a tool for quantitative phase imaging, offers all preconditions for the accurate automated analysis of live cell behavior while enabling noninvasive label-free imaging with sufficient contrast and high-spatiotemporal phase sensitivity.

Automated image quality assessment for chest CT scans.

Science.gov (United States)

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

2018-02-01

Medical image quality needs to be maintained at standards sufficient for effective clinical reading. Automated computer analytic methods may be applied to medical images for quality assessment. For chest CT scans in a lung cancer screening context, an automated quality assessment method is presented that characterizes image noise and image intensity calibration. This is achieved by image measurements in three automatically segmented homogeneous regions of the scan: external air, trachea lumen air, and descending aorta blood. Profiles of CT scanner behavior are also computed. The method has been evaluated on both phantom and real low-dose chest CT scans and results show that repeatable noise and calibration measures may be realized by automated computer algorithms. Noise and calibration profiles show relevant differences between different scanners and protocols. Automated image quality assessment may be useful for quality control for lung cancer screening and may enable performance improvements to automated computer analysis methods. © 2017 American Association of Physicists in Medicine.

Visualization and correction of automated segmentation, tracking and lineaging from 5-D stem cell image sequences.

Science.gov (United States)

Wait, Eric; Winter, Mark; Bjornsson, Chris; Kokovay, Erzsebet; Wang, Yue; Goderie, Susan; Temple, Sally; Cohen, Andrew R

2014-10-03

Neural stem cells are motile and proliferative cells that undergo mitosis, dividing to produce daughter cells and ultimately generating differentiated neurons and glia. Understanding the mechanisms controlling neural stem cell proliferation and differentiation will play a key role in the emerging fields of regenerative medicine and cancer therapeutics. Stem cell studies in vitro from 2-D image data are well established. Visualizing and analyzing large three dimensional images of intact tissue is a challenging task. It becomes more difficult as the dimensionality of the image data increases to include time and additional fluorescence channels. There is a pressing need for 5-D image analysis and visualization tools to study cellular dynamics in the intact niche and to quantify the role that environmental factors play in determining cell fate. We present an application that integrates visualization and quantitative analysis of 5-D (x,y,z,t,channel) and large montage confocal fluorescence microscopy images. The image sequences show stem cells together with blood vessels, enabling quantification of the dynamic behaviors of stem cells in relation to their vascular niche, with applications in developmental and cancer biology. Our application automatically segments, tracks, and lineages the image sequence data and then allows the user to view and edit the results of automated algorithms in a stereoscopic 3-D window while simultaneously viewing the stem cell lineage tree in a 2-D window. Using the GPU to store and render the image sequence data enables a hybrid computational approach. An inference-based approach utilizing user-provided edits to automatically correct related mistakes executes interactively on the system CPU while the GPU handles 3-D visualization tasks. By exploiting commodity computer gaming hardware, we have developed an application that can be run in the laboratory to facilitate rapid iteration through biological experiments. We combine unsupervised image

Live cell imaging combined with high-energy single-ion microbeam

Energy Technology Data Exchange (ETDEWEB)

Guo, Na; Du, Guanghua, E-mail: gh-du@impcas.ac.cn; Liu, Wenjing; Wu, Ruqun; Wei, Junzhe [Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou (China); Guo, Jinlong [Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou (China); Northwest Normal University, Lanzhou (China); Chen, Hao [Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou (China); Institute of Nuclear Science and Technology, University of Lanzhou, Lanzhou (China)

2016-03-15

DNA strand breaks can lead to cell carcinogenesis or cell death if not repaired rapidly and efficiently. An online live cell imaging system was established at the high energy microbeam facility at the Institute of Modern Physics to study early and fast cellular response to DNA damage after high linear energy transfer ion radiation. The HT1080 cells expressing XRCC1-RFP were irradiated with single high energy nickel ions, and time-lapse images of the irradiated cells were obtained online. The live cell imaging analysis shows that strand-break repair protein XRCC1 was recruited to the ion hit position within 20 s in the cells and formed bright foci in the cell nucleus. The fast recruitment of XRCC1 at the ion hits reached a maximum at about 200 s post-irradiation and then was followed by a slower release into the nucleoplasm. The measured dual-exponential kinetics of XRCC1 protein are consistent with the proposed consecutive reaction model, and the measurements obtained that the reaction rate constant of the XRCC1 recruitment to DNA strand break is 1.2 × 10{sup −3} s{sup −1} and the reaction rate constant of the XRCC1 release from the break-XRCC1 complex is 1.2 × 10{sup −2} s{sup −1}.

Live cell imaging combined with high-energy single-ion microbeam

International Nuclear Information System (INIS)

Guo, Na; Du, Guanghua; Liu, Wenjing; Wu, Ruqun; Wei, Junzhe; Guo, Jinlong; Chen, Hao

2016-01-01

DNA strand breaks can lead to cell carcinogenesis or cell death if not repaired rapidly and efficiently. An online live cell imaging system was established at the high energy microbeam facility at the Institute of Modern Physics to study early and fast cellular response to DNA damage after high linear energy transfer ion radiation. The HT1080 cells expressing XRCC1-RFP were irradiated with single high energy nickel ions, and time-lapse images of the irradiated cells were obtained online. The live cell imaging analysis shows that strand-break repair protein XRCC1 was recruited to the ion hit position within 20 s in the cells and formed bright foci in the cell nucleus. The fast recruitment of XRCC1 at the ion hits reached a maximum at about 200 s post-irradiation and then was followed by a slower release into the nucleoplasm. The measured dual-exponential kinetics of XRCC1 protein are consistent with the proposed consecutive reaction model, and the measurements obtained that the reaction rate constant of the XRCC1 recruitment to DNA strand break is 1.2 × 10"−"3 s"−"1 and the reaction rate constant of the XRCC1 release from the break-XRCC1 complex is 1.2 × 10"−"2 s"−"1.

Adaptive Algorithms for Automated Processing of Document Images

Science.gov (United States)

2011-01-01

ABSTRACT Title of dissertation: ADAPTIVE ALGORITHMS FOR AUTOMATED PROCESSING OF DOCUMENT IMAGES Mudit Agrawal, Doctor of Philosophy, 2011...2011 4. TITLE AND SUBTITLE Adaptive Algorithms for Automated Processing of Document Images 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM...ALGORITHMS FOR AUTOMATED PROCESSING OF DOCUMENT IMAGES by Mudit Agrawal Dissertation submitted to the Faculty of the Graduate School of the University

RootAnalyzer: A Cross-Section Image Analysis Tool for Automated Characterization of Root Cells and Tissues.

Directory of Open Access Journals (Sweden)

Joshua Chopin

Full Text Available The morphology of plant root anatomical features is a key factor in effective water and nutrient uptake. Existing techniques for phenotyping root anatomical traits are often based on manual or semi-automatic segmentation and annotation of microscopic images of root cross sections. In this article, we propose a fully automated tool, hereinafter referred to as RootAnalyzer, for efficiently extracting and analyzing anatomical traits from root-cross section images. Using a range of image processing techniques such as local thresholding and nearest neighbor identification, RootAnalyzer segments the plant root from the image's background, classifies and characterizes the cortex, stele, endodermis and epidermis, and subsequently produces statistics about the morphological properties of the root cells and tissues. We use RootAnalyzer to analyze 15 images of wheat plants and one maize plant image and evaluate its performance against manually-obtained ground truth data. The comparison shows that RootAnalyzer can fully characterize most root tissue regions with over 90% accuracy.

Biomek Cell Workstation: A Variable System for Automated Cell Cultivation.

Science.gov (United States)

Lehmann, R; Severitt, J C; Roddelkopf, T; Junginger, S; Thurow, K

2016-06-01

Automated cell cultivation is an important tool for simplifying routine laboratory work. Automated methods are independent of skill levels and daily constitution of laboratory staff in combination with a constant quality and performance of the methods. The Biomek Cell Workstation was configured as a flexible and compatible system. The modified Biomek Cell Workstation enables the cultivation of adherent and suspension cells. Until now, no commercially available systems enabled the automated handling of both types of cells in one system. In particular, the automated cultivation of suspension cells in this form has not been published. The cell counts and viabilities were nonsignificantly decreased for cells cultivated in AutoFlasks in automated handling. The proliferation of manual and automated bioscreening by the WST-1 assay showed a nonsignificant lower proliferation of automatically disseminated cells associated with a mostly lower standard error. The disseminated suspension cell lines showed different pronounced proliferations in descending order, starting with Jurkat cells followed by SEM, Molt4, and RS4 cells having the lowest proliferation. In this respect, we successfully disseminated and screened suspension cells in an automated way. The automated cultivation and dissemination of a variety of suspension cells can replace the manual method. © 2015 Society for Laboratory Automation and Screening.

Automated classification of bone marrow cells in microscopic images for diagnosis of leukemia: a comparison of two classification schemes with respect to the segmentation quality

Science.gov (United States)

Krappe, Sebastian; Benz, Michaela; Wittenberg, Thomas; Haferlach, Torsten; Münzenmayer, Christian

2015-03-01

The morphological analysis of bone marrow smears is fundamental for the diagnosis of leukemia. Currently, the counting and classification of the different types of bone marrow cells is done manually with the use of bright field microscope. This is a time consuming, partly subjective and tedious process. Furthermore, repeated examinations of a slide yield intra- and inter-observer variances. For this reason an automation of morphological bone marrow analysis is pursued. This analysis comprises several steps: image acquisition and smear detection, cell localization and segmentation, feature extraction and cell classification. The automated classification of bone marrow cells is depending on the automated cell segmentation and the choice of adequate features extracted from different parts of the cell. In this work we focus on the evaluation of support vector machines (SVMs) and random forests (RFs) for the differentiation of bone marrow cells in 16 different classes, including immature and abnormal cell classes. Data sets of different segmentation quality are used to test the two approaches. Automated solutions for the morphological analysis for bone marrow smears could use such a classifier to pre-classify bone marrow cells and thereby shortening the examination duration.

GoIFISH: a system for the quantification of single cell heterogeneity from IFISH images.

Science.gov (United States)

Trinh, Anne; Rye, Inga H; Almendro, Vanessa; Helland, Aslaug; Russnes, Hege G; Markowetz, Florian

2014-08-26

Molecular analysis has revealed extensive intra-tumor heterogeneity in human cancer samples, but cannot identify cell-to-cell variations within the tissue microenvironment. In contrast, in situ analysis can identify genetic aberrations in phenotypically defined cell subpopulations while preserving tissue-context specificity. GoIFISHGoIFISH is a widely applicable, user-friendly system tailored for the objective and semi-automated visualization, detection and quantification of genomic alterations and protein expression obtained from fluorescence in situ analysis. In a sample set of HER2-positive breast cancers GoIFISHGoIFISH is highly robust in visual analysis and its accuracy compares favorably to other leading image analysis methods. GoIFISHGoIFISH is freely available at www.sourceforge.net/projects/goifish/.

Automated classification of cell morphology by coherence-controlled holographic microscopy.

Science.gov (United States)

Strbkova, Lenka; Zicha, Daniel; Vesely, Pavel; Chmelik, Radim

2017-08-01

In the last few years, classification of cells by machine learning has become frequently used in biology. However, most of the approaches are based on morphometric (MO) features, which are not quantitative in terms of cell mass. This may result in poor classification accuracy. Here, we study the potential contribution of coherence-controlled holographic microscopy enabling quantitative phase imaging for the classification of cell morphologies. We compare our approach with the commonly used method based on MO features. We tested both classification approaches in an experiment with nutritionally deprived cancer tissue cells, while employing several supervised machine learning algorithms. Most of the classifiers provided higher performance when quantitative phase features were employed. Based on the results, it can be concluded that the quantitative phase features played an important role in improving the performance of the classification. The methodology could be valuable help in refining the monitoring of live cells in an automated fashion. We believe that coherence-controlled holographic microscopy, as a tool for quantitative phase imaging, offers all preconditions for the accurate automated analysis of live cell behavior while enabling noninvasive label-free imaging with sufficient contrast and high-spatiotemporal phase sensitivity. (2017) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE).

Using simulated fluorescence cell micrographs for the evaluation of cell image segmentation algorithms.

Science.gov (United States)

Wiesmann, Veit; Bergler, Matthias; Palmisano, Ralf; Prinzen, Martin; Franz, Daniela; Wittenberg, Thomas

2017-03-18

Manual assessment and evaluation of fluorescent micrograph cell experiments is time-consuming and tedious. Automated segmentation pipelines can ensure efficient and reproducible evaluation and analysis with constant high quality for all images of an experiment. Such cell segmentation approaches are usually validated and rated in comparison to manually annotated micrographs. Nevertheless, manual annotations are prone to errors and display inter- and intra-observer variability which influence the validation results of automated cell segmentation pipelines. We present a new approach to simulate fluorescent cell micrographs that provides an objective ground truth for the validation of cell segmentation methods. The cell simulation was evaluated twofold: (1) An expert observer study shows that the proposed approach generates realistic fluorescent cell micrograph simulations. (2) An automated segmentation pipeline on the simulated fluorescent cell micrographs reproduces segmentation performances of that pipeline on real fluorescent cell micrographs. The proposed simulation approach produces realistic fluorescent cell micrographs with corresponding ground truth. The simulated data is suited to evaluate image segmentation pipelines more efficiently and reproducibly than it is possible on manually annotated real micrographs.

Multichannel Image Mosaicing of Stem Cells

OpenAIRE

Alessandro Bevilacqua; Alessandro Gherardi; Filippo Piccinini

2010-01-01

Image mosaicing techniques are usually employed to offer researchers a wider field of view of microscopic image of biological samples. a mosaic is commonly achieved using automated microscopes and often with one “color" channel, whether it refers to natural or fluorescent analysis. In this work we present a method to achieve three subsequent mosaics of the same part of a stem cell culture analyzed in phase contrast and in fluorescence, with a common non-automated inverted microscope. The mosa...

Tilted Light Sheet Microscopy with 3D Point Spread Functions for Single-Molecule Super-Resolution Imaging in Mammalian Cells.

Science.gov (United States)

Gustavsson, Anna-Karin; Petrov, Petar N; Lee, Maurice Y; Shechtman, Yoav; Moerner, W E

2018-02-01

To obtain a complete picture of subcellular nanostructures, cells must be imaged with high resolution in all three dimensions (3D). Here, we present tilted light sheet microscopy with 3D point spread functions (TILT3D), an imaging platform that combines a novel, tilted light sheet illumination strategy with engineered long axial range point spread functions (PSFs) for low-background, 3D super localization of single molecules as well as 3D super-resolution imaging in thick cells. TILT3D is built upon a standard inverted microscope and has minimal custom parts. The axial positions of the single molecules are encoded in the shape of the PSF rather than in the position or thickness of the light sheet, and the light sheet can therefore be formed using simple optics. The result is flexible and user-friendly 3D super-resolution imaging with tens of nm localization precision throughout thick mammalian cells. We validated TILT3D for 3D super-resolution imaging in mammalian cells by imaging mitochondria and the full nuclear lamina using the double-helix PSF for single-molecule detection and the recently developed Tetrapod PSF for fiducial bead tracking and live axial drift correction. We envision TILT3D to become an important tool not only for 3D super-resolution imaging, but also for live whole-cell single-particle and single-molecule tracking.

Tilted light sheet microscopy with 3D point spread functions for single-molecule super-resolution imaging in mammalian cells

Science.gov (United States)

Gustavsson, Anna-Karin; Petrov, Petar N.; Lee, Maurice Y.; Shechtman, Yoav; Moerner, W. E.

2018-02-01

To obtain a complete picture of subcellular nanostructures, cells must be imaged with high resolution in all three dimensions (3D). Here, we present tilted light sheet microscopy with 3D point spread functions (TILT3D), an imaging platform that combines a novel, tilted light sheet illumination strategy with engineered long axial range point spread functions (PSFs) for low-background, 3D super localization of single molecules as well as 3D super-resolution imaging in thick cells. TILT3D is built upon a standard inverted microscope and has minimal custom parts. The axial positions of the single molecules are encoded in the shape of the PSF rather than in the position or thickness of the light sheet, and the light sheet can therefore be formed using simple optics. The result is flexible and user-friendly 3D super-resolution imaging with tens of nm localization precision throughout thick mammalian cells. We validated TILT3D for 3D superresolution imaging in mammalian cells by imaging mitochondria and the full nuclear lamina using the double-helix PSF for single-molecule detection and the recently developed Tetrapod PSF for fiducial bead tracking and live axial drift correction. We envision TILT3D to become an important tool not only for 3D super-resolution imaging, but also for live whole-cell single-particle and single-molecule tracking.

Semi-automated relative quantification of cell culture contamination with mycoplasma by Photoshop-based image analysis on immunofluorescence preparations.

Science.gov (United States)

Kumar, Ashok; Yerneni, Lakshmana K

2009-01-01

Mycoplasma contamination in cell culture is a serious setback for the cell-culturist. The experiments undertaken using contaminated cell cultures are known to yield unreliable or false results due to various morphological, biochemical and genetic effects. Earlier surveys revealed incidences of mycoplasma contamination in cell cultures to range from 15 to 80%. Out of a vast array of methods for detecting mycoplasma in cell culture, the cytological methods directly demonstrate the contaminating organism present in association with the cultured cells. In this investigation, we report the adoption of a cytological immunofluorescence assay (IFA), in an attempt to obtain a semi-automated relative quantification of contamination by employing the user-friendly Photoshop-based image analysis. The study performed on 77 cell cultures randomly collected from various laboratories revealed mycoplasma contamination in 18 cell cultures simultaneously by IFA and Hoechst DNA fluorochrome staining methods. It was observed that the Photoshop-based image analysis on IFA stained slides was very valuable as a sensitive tool in providing quantitative assessment on the extent of contamination both per se and in comparison to cellularity of cell cultures. The technique could be useful in estimating the efficacy of anti-mycoplasma agents during decontaminating measures.

Biofilm growth program and architecture revealed by single-cell live imaging

Science.gov (United States)

Yan, Jing; Sabass, Benedikt; Stone, Howard; Wingreen, Ned; Bassler, Bonnie

Biofilms are surface-associated bacterial communities. Little is known about biofilm structure at the level of individual cells. We image living, growing Vibrio cholerae biofilms from founder cells to ten thousand cells at single-cell resolution, and discover the forces underpinning the architectural evolution of the biofilm. Mutagenesis, matrix labeling, and simulations demonstrate that surface-adhesion-mediated compression causes V. cholerae biofilms to transition from a two-dimensional branched morphology to a dense, ordered three-dimensional cluster. We discover that directional proliferation of rod-shaped bacteria plays a dominant role in shaping the biofilm architecture, and this growth pattern is controlled by a single gene. Competition analyses reveal the advantages of the dense growth mode in providing the biofilm with superior mechanical properties. We will further present continuum theory to model the three-dimensional growth of biofilms at the solid-liquid interface as well as solid-air interface.

Automated cell type discovery and classification through knowledge transfer

Science.gov (United States)

Lee, Hao-Chih; Kosoy, Roman; Becker, Christine E.

2017-01-01

Abstract Motivation: Recent advances in mass cytometry allow simultaneous measurements of up to 50 markers at single-cell resolution. However, the high dimensionality of mass cytometry data introduces computational challenges for automated data analysis and hinders translation of new biological understanding into clinical applications. Previous studies have applied machine learning to facilitate processing of mass cytometry data. However, manual inspection is still inevitable and becoming the barrier to reliable large-scale analysis. Results: We present a new algorithm called Automated Cell-type Discovery and Classification (ACDC) that fully automates the classification of canonical cell populations and highlights novel cell types in mass cytometry data. Evaluations on real-world data show ACDC provides accurate and reliable estimations compared to manual gating results. Additionally, ACDC automatically classifies previously ambiguous cell types to facilitate discovery. Our findings suggest that ACDC substantially improves both reliability and interpretability of results obtained from high-dimensional mass cytometry profiling data. Availability and Implementation: A Python package (Python 3) and analysis scripts for reproducing the results are availability on https://bitbucket.org/dudleylab/acdc. Contact: brian.kidd@mssm.edu or joel.dudley@mssm.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:28158442

Automated image analysis for quantitative fluorescence in situ hybridization with environmental samples.

Science.gov (United States)

Zhou, Zhi; Pons, Marie Noëlle; Raskin, Lutgarde; Zilles, Julie L

2007-05-01

When fluorescence in situ hybridization (FISH) analyses are performed with complex environmental samples, difficulties related to the presence of microbial cell aggregates and nonuniform background fluorescence are often encountered. The objective of this study was to develop a robust and automated quantitative FISH method for complex environmental samples, such as manure and soil. The method and duration of sample dispersion were optimized to reduce the interference of cell aggregates. An automated image analysis program that detects cells from 4',6'-diamidino-2-phenylindole (DAPI) micrographs and extracts the maximum and mean fluorescence intensities for each cell from corresponding FISH images was developed with the software Visilog. Intensity thresholds were not consistent even for duplicate analyses, so alternative ways of classifying signals were investigated. In the resulting method, the intensity data were divided into clusters using fuzzy c-means clustering, and the resulting clusters were classified as target (positive) or nontarget (negative). A manual quality control confirmed this classification. With this method, 50.4, 72.1, and 64.9% of the cells in two swine manure samples and one soil sample, respectively, were positive as determined with a 16S rRNA-targeted bacterial probe (S-D-Bact-0338-a-A-18). Manual counting resulted in corresponding values of 52.3, 70.6, and 61.5%, respectively. In two swine manure samples and one soil sample 21.6, 12.3, and 2.5% of the cells were positive with an archaeal probe (S-D-Arch-0915-a-A-20), respectively. Manual counting resulted in corresponding values of 22.4, 14.0, and 2.9%, respectively. This automated method should facilitate quantitative analysis of FISH images for a variety of complex environmental samples.

Current automated 3D cell detection methods are not a suitable replacement for manual stereologic cell counting

Directory of Open Access Journals (Sweden)

Christoph eSchmitz

2014-05-01

Full Text Available Stereologic cell counting has had a major impact on the field of neuroscience. A major bottleneck in stereologic cell counting is that the user must manually decide whether or not each cell is counted according to three-dimensional (3D stereologic counting rules by visual inspection within hundreds of microscopic fields-of-view per investigated brain or brain region. Reliance on visual inspection forces stereologic cell counting to be very labor-intensive and time-consuming, and is the main reason why biased, non-stereologic two-dimensional (2D cell counting approaches have remained in widespread use. We present an evaluation of the performance of modern automated cell detection and segmentation algorithms as a potential alternative to the manual approach in stereologic cell counting. The image data used in this study were 3D microscopic images of thick brain tissue sections prepared with a variety of commonly used nuclear and cytoplasmic stains. The evaluation compared the numbers and locations of cells identified unambiguously and counted exhaustively by an expert observer with those found by three automated 3D cell detection algorithms: nuclei segmentation from the FARSIGHT toolkit, nuclei segmentation by 3D multiple level set methods, and the 3D object counter plug-in for ImageJ. Of these methods, FARSIGHT performed best, with true-positive detection rates between 38–99% and false-positive rates from 3.6–82%. The results demonstrate that the current automated methods suffer from lower detection rates and higher false-positive rates than are acceptable for obtaining valid estimates of cell numbers. Thus, at present, stereologic cell counting with manual decision for object inclusion according to unbiased stereologic counting rules remains the only adequate method for unbiased cell quantification in histologic tissue sections.

ATOM - an OMERO add-on for automated import of image data

Directory of Open Access Journals (Sweden)

Lipp Peter

2011-10-01

Full Text Available Abstract Background Modern microscope platforms are able to generate multiple gigabytes of image data in a single experimental session. In a routine research laboratory workflow, these data are initially stored on the local acquisition computer from which files need to be transferred to the experimenter's (remote image repository (e.g., DVDs, portable hard discs or server-based storage because of limited local data storage. Although manual solutions for this migration, such as OMERO - a client-server software for visualising and managing large amounts of image data - exist, this import process may be a time-consuming and tedious task. Findings We have developed ATOM, a Java-based and thus platform-independent add-on for OMERO enabling automated transfer of image data from a wide variety of acquisition software packages into OMERO. ATOM provides a graphical user interface and allows pre-organisation of experimental data for the transfer. Conclusions ATOM is a convenient extension of the OMERO software system. An automated interface to OMERO will be a useful tool for scientists working with file formats supported by the Bio-Formats file format library, a platform-independent library for reading the most common file formats of microscope images.

NeuronMetrics: software for semi-automated processing of cultured neuron images.

Science.gov (United States)

Narro, Martha L; Yang, Fan; Kraft, Robert; Wenk, Carola; Efrat, Alon; Restifo, Linda L

2007-03-23

Using primary cell culture to screen for changes in neuronal morphology requires specialized analysis software. We developed NeuronMetrics for semi-automated, quantitative analysis of two-dimensional (2D) images of fluorescently labeled cultured neurons. It skeletonizes the neuron image using two complementary image-processing techniques, capturing fine terminal neurites with high fidelity. An algorithm was devised to span wide gaps in the skeleton. NeuronMetrics uses a novel strategy based on geometric features called faces to extract a branch number estimate from complex arbors with numerous neurite-to-neurite contacts, without creating a precise, contact-free representation of the neurite arbor. It estimates total neurite length, branch number, primary neurite number, territory (the area of the convex polygon bounding the skeleton and cell body), and Polarity Index (a measure of neuronal polarity). These parameters provide fundamental information about the size and shape of neurite arbors, which are critical factors for neuronal function. NeuronMetrics streamlines optional manual tasks such as removing noise, isolating the largest primary neurite, and correcting length for self-fasciculating neurites. Numeric data are output in a single text file, readily imported into other applications for further analysis. Written as modules for ImageJ, NeuronMetrics provides practical analysis tools that are easy to use and support batch processing. Depending on the need for manual intervention, processing time for a batch of approximately 60 2D images is 1.0-2.5 h, from a folder of images to a table of numeric data. NeuronMetrics' output accelerates the quantitative detection of mutations and chemical compounds that alter neurite morphology in vitro, and will contribute to the use of cultured neurons for drug discovery.

Low-dose DNA damage and replication stress responses quantified by optimized automated single-cell image analysis

DEFF Research Database (Denmark)

Mistrik, Martin; Oplustilova, Lenka; Lukas, Jiri

2009-01-01

sensitive, quantitative, rapid and simple fluorescence image analysis in thousands of adherent cells per day. Sensitive DNA breakage estimation through analysis of phosphorylated histone H2AX (gamma-H2AX), and homologous recombination (HR) assessed by a new RPA/Rad51 dual-marker approach illustrate...

An automated digital imaging system for environmental monitoring applications

Science.gov (United States)

Bogle, Rian; Velasco, Miguel; Vogel, John

2013-01-01

Recent improvements in the affordability and availability of high-resolution digital cameras, data loggers, embedded computers, and radio/cellular modems have advanced the development of sophisticated automated systems for remote imaging. Researchers have successfully placed and operated automated digital cameras in remote locations and in extremes of temperature and humidity, ranging from the islands of the South Pacific to the Mojave Desert and the Grand Canyon. With the integration of environmental sensors, these automated systems are able to respond to local conditions and modify their imaging regimes as needed. In this report we describe in detail the design of one type of automated imaging system developed by our group. It is easily replicated, low-cost, highly robust, and is a stand-alone automated camera designed to be placed in remote locations, without wireless connectivity.

Fast and accurate automated cell boundary determination for fluorescence microscopy

Science.gov (United States)

Arce, Stephen Hugo; Wu, Pei-Hsun; Tseng, Yiider

2013-07-01

Detailed measurement of cell phenotype information from digital fluorescence images has the potential to greatly advance biomedicine in various disciplines such as patient diagnostics or drug screening. Yet, the complexity of cell conformations presents a major barrier preventing effective determination of cell boundaries, and introduces measurement error that propagates throughout subsequent assessment of cellular parameters and statistical analysis. State-of-the-art image segmentation techniques that require user-interaction, prolonged computation time and specialized training cannot adequately provide the support for high content platforms, which often sacrifice resolution to foster the speedy collection of massive amounts of cellular data. This work introduces a strategy that allows us to rapidly obtain accurate cell boundaries from digital fluorescent images in an automated format. Hence, this new method has broad applicability to promote biotechnology.

ARTIP: Automated Radio Telescope Image Processing Pipeline

Science.gov (United States)

Sharma, Ravi; Gyanchandani, Dolly; Kulkarni, Sarang; Gupta, Neeraj; Pathak, Vineet; Pande, Arti; Joshi, Unmesh

2018-02-01

The Automated Radio Telescope Image Processing Pipeline (ARTIP) automates the entire process of flagging, calibrating, and imaging for radio-interferometric data. ARTIP starts with raw data, i.e. a measurement set and goes through multiple stages, such as flux calibration, bandpass calibration, phase calibration, and imaging to generate continuum and spectral line images. Each stage can also be run independently. The pipeline provides continuous feedback to the user through various messages, charts and logs. It is written using standard python libraries and the CASA package. The pipeline can deal with datasets with multiple spectral windows and also multiple target sources which may have arbitrary combinations of flux/bandpass/phase calibrators.

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.

Cell nuclei segmentation in fluorescence microscopy images using inter- and intra-region discriminative information.

Science.gov (United States)

Song, Yang; Cai, Weidong; Feng, David Dagan; Chen, Mei

2013-01-01

Automated segmentation of cell nuclei in microscopic images is critical to high throughput analysis of the ever increasing amount of data. Although cell nuclei are generally visually distinguishable for human, automated segmentation faces challenges when there is significant intensity inhomogeneity among cell nuclei or in the background. In this paper, we propose an effective method for automated cell nucleus segmentation using a three-step approach. It first obtains an initial segmentation by extracting salient regions in the image, then reduces false positives using inter-region feature discrimination, and finally refines the boundary of the cell nuclei using intra-region contrast information. This method has been evaluated on two publicly available datasets of fluorescence microscopic images with 4009 cells, and has achieved superior performance compared to popular state of the art methods using established metrics.

Label-free imaging of gold nanoparticles in single live cells by photoacoustic microscopy

Science.gov (United States)

Tian, Chao; Qian, Wei; Shao, Xia; Xie, Zhixing; Cheng, Xu; Liu, Shengchun; Cheng, Qian; Liu, Bing; Wang, Xueding

2016-03-01

Gold nanoparticles (AuNPs) have been extensively explored as a model nanostructure in nanomedicine and have been widely used to provide advanced biomedical research tools in diagnostic imaging and therapy. Due to the necessity of targeting AuNPs to individual cells, evaluation and visualization of AuNPs in the cellular level is critical to fully understand their interaction with cellular environment. Currently imaging technologies, such as fluorescence microscopy and transmission electron microscopy all have advantages and disadvantages. In this paper, we synthesized AuNPs by femtosecond pulsed laser ablation, modified their surface chemistry through sequential bioconjugation, and targeted the functionalized AuNPs with individual cancer cells. Based on their high optical absorption contrast, we developed a novel, label-free imaging method to evaluate and visualize intracellular AuNPs using photoacoustic microscopy (PAM). Preliminary study shows that the PAM imaging technique is capable of imaging cellular uptake of AuNPs in vivo at single-cell resolution, which provide an important tool for the study of AuNPs in nanomedicine.

Atomic force microscopy imaging and 3-D reconstructions of serial thin sections of a single cell and its interior structures

International Nuclear Information System (INIS)

Chen Yong; Cai Jiye; Zhao Tao; Wang Chenxi; Dong Shuo; Luo Shuqian; Chen, Zheng W.

2005-01-01

The thin sectioning has been widely applied in electron microscopy (EM), and successfully used for an in situ observation of inner ultrastructure of cells. This powerful technique has recently been extended to the research field of atomic force microscopy (AFM). However, there have been no reports describing AFM imaging of serial thin sections and three-dimensional (3-D) reconstruction of cells and their inner structures. In the present study, we used AFM to scan serial thin sections approximately 60 nm thick of a mouse embryonic stem (ES) cell, and to observe the in situ inner ultrastructure including cell membrane, cytoplasm, mitochondria, nucleus membrane, and linear chromatin. The high-magnification AFM imaging of single mitochondria clearly demonstrated the outer membrane, inner boundary membrane and cristal membrane of mitochondria in the cellular compartment. Importantly, AFM imaging on six serial thin sections of a single mouse ES cell showed that mitochondria underwent sequential changes in the number, morphology and distribution. These nanoscale images allowed us to perform 3-D surface reconstruction of interested interior structures in cells. Based on the serial in situ images, 3-D models of morphological characteristics, numbers and distributions of interior structures of the single ES cells were validated and reconstructed. Our results suggest that the combined AFM and serial-thin-section technique is useful for the nanoscale imaging and 3-D reconstruction of single cells and their inner structures. This technique may facilitate studies of proliferating and differentiating stages of stem cells or somatic cells at a nanoscale

Automated processing of zebrafish imaging data: a survey.

Science.gov (United States)

Mikut, Ralf; Dickmeis, Thomas; Driever, Wolfgang; Geurts, Pierre; Hamprecht, Fred A; Kausler, Bernhard X; Ledesma-Carbayo, María J; Marée, Raphaël; Mikula, Karol; Pantazis, Periklis; Ronneberger, Olaf; Santos, Andres; Stotzka, Rainer; Strähle, Uwe; Peyriéras, Nadine

2013-09-01

Due to the relative transparency of its embryos and larvae, the zebrafish is an ideal model organism for bioimaging approaches in vertebrates. Novel microscope technologies allow the imaging of developmental processes in unprecedented detail, and they enable the use of complex image-based read-outs for high-throughput/high-content screening. Such applications can easily generate Terabytes of image data, the handling and analysis of which becomes a major bottleneck in extracting the targeted information. Here, we describe the current state of the art in computational image analysis in the zebrafish system. We discuss the challenges encountered when handling high-content image data, especially with regard to data quality, annotation, and storage. We survey methods for preprocessing image data for further analysis, and describe selected examples of automated image analysis, including the tracking of cells during embryogenesis, heartbeat detection, identification of dead embryos, recognition of tissues and anatomical landmarks, and quantification of behavioral patterns of adult fish. We review recent examples for applications using such methods, such as the comprehensive analysis of cell lineages during early development, the generation of a three-dimensional brain atlas of zebrafish larvae, and high-throughput drug screens based on movement patterns. Finally, we identify future challenges for the zebrafish image analysis community, notably those concerning the compatibility of algorithms and data formats for the assembly of modular analysis pipelines.

Automated Processing of Zebrafish Imaging Data: A Survey

Science.gov (United States)

Dickmeis, Thomas; Driever, Wolfgang; Geurts, Pierre; Hamprecht, Fred A.; Kausler, Bernhard X.; Ledesma-Carbayo, María J.; Marée, Raphaël; Mikula, Karol; Pantazis, Periklis; Ronneberger, Olaf; Santos, Andres; Stotzka, Rainer; Strähle, Uwe; Peyriéras, Nadine

2013-01-01

Abstract Due to the relative transparency of its embryos and larvae, the zebrafish is an ideal model organism for bioimaging approaches in vertebrates. Novel microscope technologies allow the imaging of developmental processes in unprecedented detail, and they enable the use of complex image-based read-outs for high-throughput/high-content screening. Such applications can easily generate Terabytes of image data, the handling and analysis of which becomes a major bottleneck in extracting the targeted information. Here, we describe the current state of the art in computational image analysis in the zebrafish system. We discuss the challenges encountered when handling high-content image data, especially with regard to data quality, annotation, and storage. We survey methods for preprocessing image data for further analysis, and describe selected examples of automated image analysis, including the tracking of cells during embryogenesis, heartbeat detection, identification of dead embryos, recognition of tissues and anatomical landmarks, and quantification of behavioral patterns of adult fish. We review recent examples for applications using such methods, such as the comprehensive analysis of cell lineages during early development, the generation of a three-dimensional brain atlas of zebrafish larvae, and high-throughput drug screens based on movement patterns. Finally, we identify future challenges for the zebrafish image analysis community, notably those concerning the compatibility of algorithms and data formats for the assembly of modular analysis pipelines. PMID:23758125

A three-dimensional image processing program for accurate, rapid, and semi-automated segmentation of neuronal somata with dense neurite outgrowth

Science.gov (United States)

Ross, James D.; Cullen, D. Kacy; Harris, James P.; LaPlaca, Michelle C.; DeWeerth, Stephen P.

2015-01-01

Three-dimensional (3-D) image analysis techniques provide a powerful means to rapidly and accurately assess complex morphological and functional interactions between neural cells. Current software-based identification methods of neural cells generally fall into two applications: (1) segmentation of cell nuclei in high-density constructs or (2) tracing of cell neurites in single cell investigations. We have developed novel methodologies to permit the systematic identification of populations of neuronal somata possessing rich morphological detail and dense neurite arborization throughout thick tissue or 3-D in vitro constructs. The image analysis incorporates several novel automated features for the discrimination of neurites and somata by initially classifying features in 2-D and merging these classifications into 3-D objects; the 3-D reconstructions automatically identify and adjust for over and under segmentation errors. Additionally, the platform provides for software-assisted error corrections to further minimize error. These features attain very accurate cell boundary identifications to handle a wide range of morphological complexities. We validated these tools using confocal z-stacks from thick 3-D neural constructs where neuronal somata had varying degrees of neurite arborization and complexity, achieving an accuracy of ≥95%. We demonstrated the robustness of these algorithms in a more complex arena through the automated segmentation of neural cells in ex vivo brain slices. These novel methods surpass previous techniques by improving the robustness and accuracy by: (1) the ability to process neurites and somata, (2) bidirectional segmentation correction, and (3) validation via software-assisted user input. This 3-D image analysis platform provides valuable tools for the unbiased analysis of neural tissue or tissue surrogates within a 3-D context, appropriate for the study of multi-dimensional cell-cell and cell-extracellular matrix interactions. PMID

Single cell analysis: the new frontier in 'Omics'

Energy Technology Data Exchange (ETDEWEB)

Wang, Daojing; Bodovitz, Steven

2010-01-14

Cellular heterogeneity arising from stochastic expression of genes, proteins, and metabolites is a fundamental principle of cell biology, but single cell analysis has been beyond the capabilities of 'Omics' technologies. This is rapidly changing with the recent examples of single cell genomics, transcriptomics, proteomics, and metabolomics. The rate of change is expected to accelerate owing to emerging technologies that range from micro/nanofluidics to microfabricated interfaces for mass spectrometry to third- and fourth-generation automated DNA sequencers. As described in this review, single cell analysis is the new frontier in Omics, and single cell Omics has the potential to transform systems biology through new discoveries derived from cellular heterogeneity.

High-Throughput Screening Enhances Kidney Organoid Differentiation from Human Pluripotent Stem Cells and Enables Automated Multidimensional Phenotyping.

Science.gov (United States)

Czerniecki, Stefan M; Cruz, Nelly M; Harder, Jennifer L; Menon, Rajasree; Annis, James; Otto, Edgar A; Gulieva, Ramila E; Islas, Laura V; Kim, Yong Kyun; Tran, Linh M; Martins, Timothy J; Pippin, Jeffrey W; Fu, Hongxia; Kretzler, Matthias; Shankland, Stuart J; Himmelfarb, Jonathan; Moon, Randall T; Paragas, Neal; Freedman, Benjamin S

2018-05-15

Organoids derived from human pluripotent stem cells are a potentially powerful tool for high-throughput screening (HTS), but the complexity of organoid cultures poses a significant challenge for miniaturization and automation. Here, we present a fully automated, HTS-compatible platform for enhanced differentiation and phenotyping of human kidney organoids. The entire 21-day protocol, from plating to differentiation to analysis, can be performed automatically by liquid-handling robots, or alternatively by manual pipetting. High-content imaging analysis reveals both dose-dependent and threshold effects during organoid differentiation. Immunofluorescence and single-cell RNA sequencing identify previously undetected parietal, interstitial, and partially differentiated compartments within organoids and define conditions that greatly expand the vascular endothelium. Chemical modulation of toxicity and disease phenotypes can be quantified for safety and efficacy prediction. Screening in gene-edited organoids in this system reveals an unexpected role for myosin in polycystic kidney disease. Organoids in HTS formats thus establish an attractive platform for multidimensional phenotypic screening. Copyright © 2018 Elsevier Inc. All rights reserved.

Automated marker tracking using noisy X-ray images degraded by the treatment beam

Energy Technology Data Exchange (ETDEWEB)

Wisotzky, E. [Fraunhofer Institute for Production Systems and Design Technology (IPK), Berlin (Germany); German Cancer Research Center (DKFZ), Heidelberg (Germany); Fast, M.F.; Nill, S. [The Royal Marsden NHS Foundation Trust, London (United Kingdom). Joint Dept. of Physics; Oelfke, U. [The Royal Marsden NHS Foundation Trust, London (United Kingdom). Joint Dept. of Physics; German Cancer Research Center (DKFZ), Heidelberg (Germany)

2015-09-01

This study demonstrates the feasibility of automated marker tracking for the real-time detection of intrafractional target motion using noisy kilovoltage (kV) X-ray images degraded by the megavoltage (MV) treatment beam. The authors previously introduced the in-line imaging geometry, in which the flat-panel detector (FPD) is mounted directly underneath the treatment head of the linear accelerator. They found that the 121 kVp image quality was severely compromised by the 6 MV beam passing through the FPD at the same time. Specific MV-induced artefacts present a considerable challenge for automated marker detection algorithms. For this study, the authors developed a new imaging geometry by re-positioning the FPD and the X-ray tube. This improved the contrast-to-noise-ratio between 40% and 72% at the 1.2 mAs/image exposure setting. The increase in image quality clearly facilitates the quick and stable detection of motion with the aid of a template matching algorithm. The setup was tested with an anthropomorphic lung phantom (including an artificial lung tumour). In the tumour one or three Calypso {sup registered} beacons were embedded to achieve better contrast during MV radiation. For a single beacon, image acquisition and automated marker detection typically took around 76±6 ms. The success rate was found to be highly dependent on imaging dose and gantry angle. To eliminate possible false detections, the authors implemented a training phase prior to treatment beam irradiation and also introduced speed limits for motion between subsequent images.

Automated marker tracking using noisy X-ray images degraded by the treatment beam

International Nuclear Information System (INIS)

Wisotzky, E.; Fast, M.F.; Nill, S.

2015-01-01

This study demonstrates the feasibility of automated marker tracking for the real-time detection of intrafractional target motion using noisy kilovoltage (kV) X-ray images degraded by the megavoltage (MV) treatment beam. The authors previously introduced the in-line imaging geometry, in which the flat-panel detector (FPD) is mounted directly underneath the treatment head of the linear accelerator. They found that the 121 kVp image quality was severely compromised by the 6 MV beam passing through the FPD at the same time. Specific MV-induced artefacts present a considerable challenge for automated marker detection algorithms. For this study, the authors developed a new imaging geometry by re-positioning the FPD and the X-ray tube. This improved the contrast-to-noise-ratio between 40% and 72% at the 1.2 mAs/image exposure setting. The increase in image quality clearly facilitates the quick and stable detection of motion with the aid of a template matching algorithm. The setup was tested with an anthropomorphic lung phantom (including an artificial lung tumour). In the tumour one or three Calypso registered beacons were embedded to achieve better contrast during MV radiation. For a single beacon, image acquisition and automated marker detection typically took around 76±6 ms. The success rate was found to be highly dependent on imaging dose and gantry angle. To eliminate possible false detections, the authors implemented a training phase prior to treatment beam irradiation and also introduced speed limits for motion between subsequent images.

Deep Learning Automates the Quantitative Analysis of Individual Cells in Live-Cell Imaging Experiments.

Science.gov (United States)

Van Valen, David A; Kudo, Takamasa; Lane, Keara M; Macklin, Derek N; Quach, Nicolas T; DeFelice, Mialy M; Maayan, Inbal; Tanouchi, Yu; Ashley, Euan A; Covert, Markus W

2016-11-01

Live-cell imaging has opened an exciting window into the role cellular heterogeneity plays in dynamic, living systems. A major critical challenge for this class of experiments is the problem of image segmentation, or determining which parts of a microscope image correspond to which individual cells. Current approaches require many hours of manual curation and depend on approaches that are difficult to share between labs. They are also unable to robustly segment the cytoplasms of mammalian cells. Here, we show that deep convolutional neural networks, a supervised machine learning method, can solve this challenge for multiple cell types across the domains of life. We demonstrate that this approach can robustly segment fluorescent images of cell nuclei as well as phase images of the cytoplasms of individual bacterial and mammalian cells from phase contrast images without the need for a fluorescent cytoplasmic marker. These networks also enable the simultaneous segmentation and identification of different mammalian cell types grown in co-culture. A quantitative comparison with prior methods demonstrates that convolutional neural networks have improved accuracy and lead to a significant reduction in curation time. We relay our experience in designing and optimizing deep convolutional neural networks for this task and outline several design rules that we found led to robust performance. We conclude that deep convolutional neural networks are an accurate method that require less curation time, are generalizable to a multiplicity of cell types, from bacteria to mammalian cells, and expand live-cell imaging capabilities to include multi-cell type systems.

Automated Methods Of Corrosion Measurements

DEFF Research Database (Denmark)

Bech-Nielsen, Gregers; Andersen, Jens Enevold Thaulov; Reeve, John Ch

1997-01-01

The chapter describes the following automated measurements: Corrosion Measurements by Titration, Imaging Corrosion by Scanning Probe Microscopy, Critical Pitting Temperature and Application of the Electrochemical Hydrogen Permeation Cell.......The chapter describes the following automated measurements: Corrosion Measurements by Titration, Imaging Corrosion by Scanning Probe Microscopy, Critical Pitting Temperature and Application of the Electrochemical Hydrogen Permeation Cell....

Automated detection of fluorescent cells in in-resin fluorescence sections for integrated light and electron microscopy.

Science.gov (United States)

Delpiano, J; Pizarro, L; Peddie, C J; Jones, M L; Griffin, L D; Collinson, L M

2018-04-26

Integrated array tomography combines fluorescence and electron imaging of ultrathin sections in one microscope, and enables accurate high-resolution correlation of fluorescent proteins to cell organelles and membranes. Large numbers of serial sections can be imaged sequentially to produce aligned volumes from both imaging modalities, thus producing enormous amounts of data that must be handled and processed using novel techniques. Here, we present a scheme for automated detection of fluorescent cells within thin resin sections, which could then be used to drive automated electron image acquisition from target regions via 'smart tracking'. The aim of this work is to aid in optimization of the data acquisition process through automation, freeing the operator to work on other tasks and speeding up the process, while reducing data rates by only acquiring images from regions of interest. This new method is shown to be robust against noise and able to deal with regions of low fluorescence. © 2018 The Authors. Journal of Microscopy published by JohnWiley & Sons Ltd on behalf of Royal Microscopical Society.

Development and application of an automated analysis method for individual cerebral perfusion single photon emission tomography images

International Nuclear Information System (INIS)

Cluckie, Alice Jane

2001-01-01

Neurological images may be analysed by performing voxel by voxel comparisons with a group of control subject images. An automated, 3D, voxel-based method has been developed for the analysis of individual single photon emission tomography (SPET) scans. Clusters of voxels are identified that represent regions of abnormal radiopharmaceutical uptake. Morphological operators are applied to reduce noise in the clusters, then quantitative estimates of the size and degree of the radiopharmaceutical uptake abnormalities are derived. Statistical inference has been performed using a Monte Carlo method that has not previously been applied to SPET scans, or for the analysis of individual images. This has been validated for group comparisons of SPET scans and for the analysis of an individual image using comparison with a group. Accurate statistical inference was obtained independent of experimental factors such as degrees of freedom, image smoothing and voxel significance level threshold. The analysis method has been evaluated for application to cerebral perfusion SPET imaging in ischaemic stroke. It has been shown that useful quantitative estimates, high sensitivity and high specificity may be obtained. Sensitivity and the accuracy of signal quantification were found to be dependent on the operator defined analysis parameters. Recommendations for the values of these parameters have been made. The analysis method developed has been compared with an established method and shown to result in higher specificity for the data and analysis parameter sets tested. In addition, application to a group of ischaemic stroke patient SPET scans has demonstrated its clinical utility. The influence of imaging conditions has been assessed using phantom data acquired with different gamma camera SPET acquisition parameters. A lower limit of five million counts and standardisation of all acquisition parameters has been recommended for the analysis of individual SPET scans. (author)

Semi-automated procedures for shoreline extraction using single RADARSAT-1 SAR image

Science.gov (United States)

Al Fugura, A.'kif; Billa, Lawal; Pradhan, Biswajeet

2011-12-01

Coastline identification is important for surveying and mapping reasons. Coastline serves as the basic point of reference and is used on nautical charts for navigation purposes. Its delineation has become crucial and more important in the wake of the many recent earthquakes and tsunamis resulting in complete change and redraw of some shorelines. In a tropical country like Malaysia, presence of cloud cover hinders the application of optical remote sensing data. In this study a semi-automated technique and procedures are presented for shoreline delineation from RADARSAT-1 image. A scene of RADARSAT-1 satellite image was processed using enhanced filtering technique to identify and extract the shoreline coast of Kuala Terengganu, Malaysia. RADSARSAT image has many advantages over the optical data because of its ability to penetrate cloud cover and its night sensing capabilities. At first, speckles were removed from the image by using Lee sigma filter which was used to reduce random noise and to enhance the image and discriminate the boundary between land and water. The results showed an accurate and improved extraction and delineation of the entire coastline of Kuala Terrenganu. The study demonstrated the reliability of the image averaging filter in reducing random noise over the sea surface especially near the shoreline. It enhanced land-water boundary differentiation, enabling better delineation of the shoreline. Overall, the developed techniques showed the potential of radar imagery for accurate shoreline mapping and will be useful for monitoring shoreline changes during high and low tides as well as shoreline erosion in a tropical country like Malaysia.

An automated three-dimensional detection and segmentation method for touching cells by integrating concave points clustering and random walker algorithm.

Directory of Open Access Journals (Sweden)

Yong He

Full Text Available Characterizing cytoarchitecture is crucial for understanding brain functions and neural diseases. In neuroanatomy, it is an important task to accurately extract cell populations' centroids and contours. Recent advances have permitted imaging at single cell resolution for an entire mouse brain using the Nissl staining method. However, it is difficult to precisely segment numerous cells, especially those cells touching each other. As presented herein, we have developed an automated three-dimensional detection and segmentation method applied to the Nissl staining data, with the following two key steps: 1 concave points clustering to determine the seed points of touching cells; and 2 random walker segmentation to obtain cell contours. Also, we have evaluated the performance of our proposed method with several mouse brain datasets, which were captured with the micro-optical sectioning tomography imaging system, and the datasets include closely touching cells. Comparing with traditional detection and segmentation methods, our approach shows promising detection accuracy and high robustness.

Novel single-cell mega-size chambers for electrochemical etching of panorama position-sensitive polycarbonate ion image detectors

Science.gov (United States)

Sohrabi, Mehdi

2017-11-01

A novel development is made here by inventing panorama single-cell mega-size electrochemical etching (MS-ECE) chamber systems for processing panorama position-sensitive mega-size polycarbonate ion image detectors (MS-PCIDs) of potential for many neutron and ion detection applications in particular hydrogen ions or proton tracks and images detected for the first time in polycarbonates in this study. The MS-PCID is simply a large polycarbonate sheet of a desired size. The single-cell MS-ECE invented consists of two large equally sized transparent Plexiglas sheets as chamber walls holding a MS-PCID and the ECE chamber components tightly together. One wall has a large flat stainless steel electrode (dry cell) attached to it which is directly in contact with the MS-PCID and the other wall has a rod electrode with two holes to facilitate feeding and draining out the etching solution from the wet cell. A silicon rubber washer plays the role of the wet cell to hold the etchant and the electrical insulator to isolate the dry cell from the wet cell. A simple 50 Hz-HV home-made generator provides an adequate field strength through the two electrodes across the MS-ECE chamber. Two panorama single-cell MS-ECE chamber systems (circular and rectangular shapes) constructed were efficiently applied to processing the MS-PCIDs for 4π ion emission image detection of different gases in particular hydrogen ions or protons in a 3.5 kJ plasma focus device (PFD as uniquely observed by the unaided eyes). The panorama MS-PCID/MS-ECE image detection systems invented are novel with high potential for many applications in particular as applied to 4π panorama ion emission angular distribution image detection studies in PFD space, some results of which are presented and discussed.

Automated Detection of Binucleated Cell and Micronuclei using CellProfiler 2.0 Software

Directory of Open Access Journals (Sweden)

DWI RAMADHANI

2013-12-01

Full Text Available Micronucleus assay in human peripheral lymphocytes usually used to assess chromosomal damage. Manual scoring of micronuclei can be time consuming and large numbers of binucleated cells have to be analyzed to obtain statistically relevant data. Automation of the micronuclei analysis using image processing analysis software can provide a faster and more reliable analysis of micronucleus assay. Here the used of CellProfiler an open access cell image analysis software for automatic detection of binucleated cells and micronuclei were reported. We aimed to know whether there was a significant difference in the number of binucleated cells and micronuclei that obtained by manual and CellProfiler counting. Wilcoxon Rank test was used for statistical analysis to test H0 hypothesis that there was no significant difference in the number of binucleated cells and micronuclei that obtained by manual and CellProfiler counting. We analyzed 135 images for both manual and CellProfiler counting. Our results showed that there was no significant difference between manual and CellProfiler counting for binucleated cells (P = 0.851 and for micronuclei (P = 0.917. In conclusion, the binucleated cells and micronuclei counting using CellProfiler were comparable but not better than manual counting.

Cascade classification of endocytoscopic images of colorectal lesions for automated pathological diagnosis

Science.gov (United States)

Itoh, Hayato; Mori, Yuichi; Misawa, Masashi; Oda, Masahiro; Kudo, Shin-ei; Mori, Kensaku

2018-02-01

This paper presents a new classification method for endocytoscopic images. Endocytoscopy is a new endoscope that enables us to perform conventional endoscopic observation and ultramagnified observation of cell level. This ultramagnified views (endocytoscopic images) make possible to perform pathological diagnosis only on endo-scopic views of polyps during colonoscopy. However, endocytoscopic image diagnosis requires higher experiences for physicians. An automated pathological diagnosis system is required to prevent the overlooking of neoplastic lesions in endocytoscopy. For this purpose, we propose a new automated endocytoscopic image classification method that classifies neoplastic and non-neoplastic endocytoscopic images. This method consists of two classification steps. At the first step, we classify an input image by support vector machine. We forward the image to the second step if the confidence of the first classification is low. At the second step, we classify the forwarded image by convolutional neural network. We reject the input image if the confidence of the second classification is also low. We experimentally evaluate the classification performance of the proposed method. In this experiment, we use about 16,000 and 4,000 colorectal endocytoscopic images as training and test data, respectively. The results show that the proposed method achieves high sensitivity 93.4% with small rejection rate 9.3% even for difficult test data.

Single-cell real-time imaging of transgene expression upon lipofection.

Science.gov (United States)

Fiume, Giuseppe; Di Rienzo, Carmine; Marchetti, Laura; Pozzi, Daniela; Caracciolo, Giulio; Cardarelli, Francesco

2016-05-20

Here we address the process of lipofection by quantifying the expression of a genetically-encoded fluorescent reporter at the single-cell level, and in real-time, by confocal imaging in live cells. The Lipofectamine gold-standard formulation is compared to the alternative promising DC-Chol/DOPE formulation. In both cases, we report that only dividing cells are able to produce a detectable amount of the fluorescent reporter protein. Notably, by measuring fluorescence over time in each pair of daughter cells, we find that Lipofectamine-based transfection statistically yields a remarkably higher degree of "symmetry" in protein expression between daughter cells as compared to DC-Chol/DOPE. A model is envisioned in which the degree of symmetry of protein expression is linked to the number of bioavailable DNA copies within the cell before nuclear breakdown. Reported results open new perspectives for the understanding of the lipofection mechanism and define a new experimental platform for the quantitative comparison of transfection reagents. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

Rapid assessment of different oxygenic phototrophs and single-cell photosynthesis with multicolour variable chlorophyll fluorescence imaging

DEFF Research Database (Denmark)

Trampe, Erik Christian Løvbjerg; Kolbowski, J.; Schreiber, U.

2011-01-01

, red or white light. Automated sequential exposure of microscopic samples to the three excitation colours enables subsequent deconvolution of the resulting fluorescence signals and colour marking of cells with different photopigmentation, i.e., cyanobacteria, green algae, red algae and diatoms....... The photosynthetic activity in complex mixtures of phototrophs and natural samples can thus be assigned to different types of phototrophs, which can be quantified simultaneously. Here, we describe the composition and performance of the new imaging system and present applications with both natural phytoplankton...

Automated otolith image classification with multiple views: an evaluation on Sciaenidae.

Science.gov (United States)

Wong, J Y; Chu, C; Chong, V C; Dhillon, S K; Loh, K H

2016-08-01

Combined multiple 2D views (proximal, anterior and ventral aspects) of the sagittal otolith are proposed here as a method to capture shape information for fish classification. Classification performance of single view compared with combined 2D views show improved classification accuracy of the latter, for nine species of Sciaenidae. The effects of shape description methods (shape indices, Procrustes analysis and elliptical Fourier analysis) on classification performance were evaluated. Procrustes analysis and elliptical Fourier analysis perform better than shape indices when single view is considered, but all perform equally well with combined views. A generic content-based image retrieval (CBIR) system that ranks dissimilarity (Procrustes distance) of otolith images was built to search query images without the need for detailed information of side (left or right), aspect (proximal or distal) and direction (positive or negative) of the otolith. Methods for the development of this automated classification system are discussed. © 2016 The Fisheries Society of the British Isles.

Scoring of radiation-induced micronuclei in cytokinesis-blocked human lymphocytes by automated image analysis

International Nuclear Information System (INIS)

Verhaegen, F.; Seuntjens, J.; Thierens, H.

1994-01-01

The micronucleus assay in human lymphocytes is, at present, frequently used to assess chromosomal damage caused by ionizing radiation or mutagens. Manual scoring of micronuclei (MN) by trained personnel is very time-consuming, tiring work, and the results depend on subjective interpretation of scoring criteria. More objective scoring can be accomplished only if the test can be automated. Furthermore, an automated system allows scoring of large numbers of cells, thereby increasing the statistical significance of the results. This is of special importance for screening programs for low doses of chromosome-damaging agents. In this paper, the first results of our effort to automate the micronucleus assay with an image-analysis system are represented. The method we used is described in detail, and the results are compared to those of other groups. Our system is able to detect 88% of the binucleated lymphocytes on the slides. The procedure consists of a fully automated localization of binucleated cells and counting of the MN within these cells, followed by a simple and fast manual operation in which the false positives are removed. Preliminary measurements for blood samples irradiated with a dose of 1 Gy X-rays indicate that the automated system can find 89% ± 12% of the micronuclei within the binucleated cells compared to a manual screening. 18 refs., 8 figs., 1 tab

AUTOMATED DETECTION OF OIL DEPOTS FROM HIGH RESOLUTION IMAGES: A NEW PERSPECTIVE

Directory of Open Access Journals (Sweden)

A. O. Ok

2015-03-01

Full Text Available This paper presents an original approach to identify oil depots from single high resolution aerial/satellite images in an automated manner. The new approach considers the symmetric nature of circular oil depots, and it computes the radial symmetry in a unique way. An automated thresholding method to focus on circular regions and a new measure to verify circles are proposed. Experiments are performed on six GeoEye-1 test images. Besides, we perform tests on 16 Google Earth images of an industrial test site acquired in a time series manner (between the years 1995 and 2012. The results reveal that our approach is capable of detecting circle objects in very different/difficult images. We computed an overall performance of 95.8% for the GeoEye-1 dataset. The time series investigation reveals that our approach is robust enough to locate oil depots in industrial environments under varying illumination and environmental conditions. The overall performance is computed as 89.4% for the Google Earth dataset, and this result secures the success of our approach compared to a state-of-the-art approach.

Noninvasive imaging of protein metabolic labeling in single human cells using stable isotopes and Raman microscopy

NARCIS (Netherlands)

van Manen, H.J.; Lenferink, Aufrid T.M.; Otto, Cornelis

2008-01-01

We have combined nonresonant Raman microspectroscopy and spectral imaging with stable isotope labeling by amino acids in cell culture (SILAC) to selectively detect the incorporation of deuterium-labeled phenylalanine, tyrosine, and methionine into proteins in intact, single HeLa cells. The C−D

FULLY AUTOMATED IMAGE ORIENTATION IN THE ABSENCE OF TARGETS

Directory of Open Access Journals (Sweden)

C. Stamatopoulos

2012-07-01

Full Text Available Automated close-range photogrammetric network orientation has traditionally been associated with the use of coded targets in the object space to allow for an initial relative orientation (RO and subsequent spatial resection of the images. Over the past decade, automated orientation via feature-based matching (FBM techniques has attracted renewed research attention in both the photogrammetry and computer vision (CV communities. This is largely due to advances made towards the goal of automated relative orientation of multi-image networks covering untargetted (markerless objects. There are now a number of CV-based algorithms, with accompanying open-source software, that can achieve multi-image orientation within narrow-baseline networks. From a photogrammetric standpoint, the results are typically disappointing as the metric integrity of the resulting models is generally poor, or even unknown, while the number of outliers within the image matching and triangulation is large, and generally too large to allow relative orientation (RO via the commonly used coplanarity equations. On the other hand, there are few examples within the photogrammetric research field of automated markerless camera calibration to metric tolerances, and these too are restricted to narrow-baseline, low-convergence imaging geometry. The objective addressed in this paper is markerless automatic multi-image orientation, maintaining metric integrity, within networks that incorporate wide-baseline imagery. By wide-baseline we imply convergent multi-image configurations with convergence angles of up to around 90°. An associated aim is provision of a fast, fully automated process, which can be performed without user intervention. For this purpose, various algorithms require optimisation to allow parallel processing utilising multiple PC cores and graphics processing units (GPUs.

Intracellular Drug Uptake-A Comparison of Single Cell Measurements Using ToF-SIMS Imaging and Quantification from Cell Populations with LC/MS/MS.

Science.gov (United States)

Newman, Carla F; Havelund, Rasmus; Passarelli, Melissa K; Marshall, Peter S; Francis, Ian; West, Andy; Alexander, Morgan R; Gilmore, Ian S; Dollery, Colin T

2017-11-21

ToF-SIMS is a label-free imaging method that has been shown to enable imaging of amiodarone in single rat macrophage (NR8383) cells. In this study, we show that the method extends to three other cell lines relevant to drug discovery: human embryonic kidney (HEK293), cervical cancer (HeLa), and liver cancer (HepG2). There is significant interest in the variation of drug uptake at the single cell level, and we use ToF-SIMS to show that there is great diversity between individual cells and when comparing each of the cell types. These single cell measurements are compared to quantitative measurements of cell-associated amiodarone for the population using LC/MS/MS and cell counting with flow cytometry. NR8383 and HepG2 cells uptake the greatest amount of amiodarone with an average of 2.38 and 2.60 pg per cell, respectively, and HeLa and Hek 293 have a significantly lower amount of amiodarone at 0.43 and 0.36 pg per cell, respectively. The amount of cell-associated drug for the ensemble population measurement (LC/MS/MS) is compared with the ToF-SIMS single cell data: a similar amount of drug was detected per cell for the NR8383, and HepG2 cells at a greater level than that for the HEK293 cells. However, the two techniques did not agree for the HeLa cells, and we postulate potential reasons for this.

Single-cell photoacoustic thermometry

Science.gov (United States)

Gao, Liang; Wang, Lidai; Li, Chiye; Liu, Yan; Ke, Haixin; Zhang, Chi

2013-01-01

Abstract. A novel photoacoustic thermometric method is presented for simultaneously imaging cells and sensing their temperature. With three-seconds-per-frame imaging speed, a temperature resolution of 0.2°C was achieved in a photo-thermal cell heating experiment. Compared to other approaches, the photoacoustic thermometric method has the advantage of not requiring custom-developed temperature-sensitive biosensors. This feature should facilitate the conversion of single-cell thermometry into a routine lab tool and make it accessible to a much broader biological research community. PMID:23377004

Under the Microscope: Single-Domain Antibodies for Live-Cell Imaging and Super-Resolution Microscopy

OpenAIRE

Traenkle, Bjoern; Rothbauer, Ulrich

2017-01-01

Single-domain antibodies (sdAbs) have substantially expanded the possibilities of advanced cellular imaging such as live-cell or super-resolution microscopy to visualize cellular antigens and their dynamics. In addition to their unique properties including small size, high stability, and solubility in many environments, sdAbs can be efficiently functionalized according to the needs of the respective imaging approach. Genetically encoded intrabodies fused to fluorescent proteins (chromobodies)...

Automated image enhancement using power law transformations

Indian Academy of Sciences (India)

We propose a scheme for automating power law transformations which are used for image enhancement. The scheme we propose does not require the user to choose the exponent in the power law transformation. This method works well for images having poor contrast, especially to those images in which the peaks ...

Cardiac imaging: working towards fully-automated machine analysis & interpretation.

Science.gov (United States)

Slomka, Piotr J; Dey, Damini; Sitek, Arkadiusz; Motwani, Manish; Berman, Daniel S; Germano, Guido

2017-03-01

Non-invasive imaging plays a critical role in managing patients with cardiovascular disease. Although subjective visual interpretation remains the clinical mainstay, quantitative analysis facilitates objective, evidence-based management, and advances in clinical research. This has driven developments in computing and software tools aimed at achieving fully automated image processing and quantitative analysis. In parallel, machine learning techniques have been used to rapidly integrate large amounts of clinical and quantitative imaging data to provide highly personalized individual patient-based conclusions. Areas covered: This review summarizes recent advances in automated quantitative imaging in cardiology and describes the latest techniques which incorporate machine learning principles. The review focuses on the cardiac imaging techniques which are in wide clinical use. It also discusses key issues and obstacles for these tools to become utilized in mainstream clinical practice. Expert commentary: Fully-automated processing and high-level computer interpretation of cardiac imaging are becoming a reality. Application of machine learning to the vast amounts of quantitative data generated per scan and integration with clinical data also facilitates a move to more patient-specific interpretation. These developments are unlikely to replace interpreting physicians but will provide them with highly accurate tools to detect disease, risk-stratify, and optimize patient-specific treatment. However, with each technological advance, we move further from human dependence and closer to fully-automated machine interpretation.

Single-cell resolution imaging of retinal ganglion cell apoptosis in vivo using a cell-penetrating caspase-activatable peptide probe.

Directory of Open Access Journals (Sweden)

Xudong Qiu

Full Text Available Peptide probes for imaging retinal ganglion cell (RGC apoptosis consist of a cell-penetrating peptide targeting moiety and a fluorophore-quencher pair flanking an effector caspase consensus sequence. Using ex vivo fluorescence imaging, we previously validated the capacity of these probes to identify apoptotic RGCs in cell culture and in an in vivo rat model of N-methyl- D-aspartate (NMDA-induced neurotoxicity. Herein, using TcapQ488, a new probe designed and synthesized for compatibility with clinically-relevant imaging instruments, and real time imaging of a live rat RGC degeneration model, we fully characterized time- and dose-dependent probe activation, signal-to-noise ratios, and probe safety profiles in vivo. Adult rats received intravitreal injections of four NMDA concentrations followed by varying TcapQ488 doses. Fluorescence fundus imaging was performed sequentially in vivo using a confocal scanning laser ophthalmoscope and individual RGCs displaying activated probe were counted and analyzed. Rats also underwent electroretinography following intravitreal injection of probe. In vivo fluorescence fundus imaging revealed distinct single-cell probe activation as an indicator of RGC apoptosis induced by intravitreal NMDA injection that corresponded to the identical cells observed in retinal flat mounts of the same eye. Peak activation of probe in vivo was detected 12 hours post probe injection. Detectable fluorescent RGCs increased with increasing NMDA concentration; sensitivity of detection generally increased with increasing TcapQ488 dose until saturating at 0.387 nmol. Electroretinography following intravitreal injections of TcapQ488 showed no significant difference compared with control injections. We optimized the signal-to-noise ratio of a caspase-activatable cell penetrating peptide probe for quantitative non-invasive detection of RGC apoptosis in vivo. Full characterization of probe performance in this setting creates an important in

Untangling cell tracks: Quantifying cell migration by time lapse image data analysis.

Science.gov (United States)

Svensson, Carl-Magnus; Medyukhina, Anna; Belyaev, Ivan; Al-Zaben, Naim; Figge, Marc Thilo

2018-03-01

Automated microscopy has given researchers access to great amounts of live cell imaging data from in vitro and in vivo experiments. Much focus has been put on extracting cell tracks from such data using a plethora of segmentation and tracking algorithms, but further analysis is normally required to draw biologically relevant conclusions. Such relevant conclusions may be whether the migration is directed or not, whether the population has homogeneous or heterogeneous migration patterns. This review focuses on the analysis of cell migration data that are extracted from time lapse images. We discuss a range of measures and models used to analyze cell tracks independent of the biological system or the way the tracks were obtained. For single-cell migration, we focus on measures and models giving examples of biological systems where they have been applied, for example, migration of bacteria, fibroblasts, and immune cells. For collective migration, we describe the model systems wound healing, neural crest migration, and Drosophila gastrulation and discuss methods for cell migration within these systems. We also discuss the role of the extracellular matrix and subsequent differences between track analysis in vitro and in vivo. Besides methods and measures, we are putting special focus on the need for openly available data and code, as well as a lack of common vocabulary in cell track analysis. © 2017 International Society for Advancement of Cytometry. © 2017 International Society for Advancement of Cytometry.

Automating proliferation rate estimation from Ki-67 histology images

Science.gov (United States)

Al-Lahham, Heba Z.; Alomari, Raja S.; Hiary, Hazem; Chaudhary, Vipin

2012-03-01

Breast cancer is the second cause of women death and the most diagnosed female cancer in the US. Proliferation rate estimation (PRE) is one of the prognostic indicators that guide the treatment protocols and it is clinically performed from Ki-67 histopathology images. Automating PRE substantially increases the efficiency of the pathologists. Moreover, presenting a deterministic and reproducible proliferation rate value is crucial to reduce inter-observer variability. To that end, we propose a fully automated CAD system for PRE from the Ki-67 histopathology images. This CAD system is based on a model of three steps: image pre-processing, image clustering, and nuclei segmentation and counting that are finally followed by PRE. The first step is based on customized color modification and color-space transformation. Then, image pixels are clustered by K-Means depending on the features extracted from the images derived from the first step. Finally, nuclei are segmented and counted using global thresholding, mathematical morphology and connected component analysis. Our experimental results on fifty Ki-67-stained histopathology images show a significant agreement between our CAD's automated PRE and the gold standard's one, where the latter is an average between two observers' estimates. The Paired T-Test, for the automated and manual estimates, shows ρ = 0.86, 0.45, 0.8 for the brown nuclei count, blue nuclei count, and proliferation rate, respectively. Thus, our proposed CAD system is as reliable as the pathologist estimating the proliferation rate. Yet, its estimate is reproducible.

Automated landmark-guided deformable image registration.

Science.gov (United States)

Kearney, Vasant; Chen, Susie; Gu, Xuejun; Chiu, Tsuicheng; Liu, Honghuan; Jiang, Lan; Wang, Jing; Yordy, John; Nedzi, Lucien; Mao, Weihua

2015-01-07

The purpose of this work is to develop an automated landmark-guided deformable image registration (LDIR) algorithm between the planning CT and daily cone-beam CT (CBCT) with low image quality. This method uses an automated landmark generation algorithm in conjunction with a local small volume gradient matching search engine to map corresponding landmarks between the CBCT and the planning CT. The landmarks act as stabilizing control points in the following Demons deformable image registration. LDIR is implemented on graphics processing units (GPUs) for parallel computation to achieve ultra fast calculation. The accuracy of the LDIR algorithm has been evaluated on a synthetic case in the presence of different noise levels and data of six head and neck cancer patients. The results indicate that LDIR performed better than rigid registration, Demons, and intensity corrected Demons for all similarity metrics used. In conclusion, LDIR achieves high accuracy in the presence of multimodality intensity mismatch and CBCT noise contamination, while simultaneously preserving high computational efficiency.

Automated landmark-guided deformable image registration

International Nuclear Information System (INIS)

Kearney, Vasant; Chen, Susie; Gu, Xuejun; Chiu, Tsuicheng; Liu, Honghuan; Jiang, Lan; Wang, Jing; Yordy, John; Nedzi, Lucien; Mao, Weihua

2015-01-01

The purpose of this work is to develop an automated landmark-guided deformable image registration (LDIR) algorithm between the planning CT and daily cone-beam CT (CBCT) with low image quality. This method uses an automated landmark generation algorithm in conjunction with a local small volume gradient matching search engine to map corresponding landmarks between the CBCT and the planning CT. The landmarks act as stabilizing control points in the following Demons deformable image registration. LDIR is implemented on graphics processing units (GPUs) for parallel computation to achieve ultra fast calculation. The accuracy of the LDIR algorithm has been evaluated on a synthetic case in the presence of different noise levels and data of six head and neck cancer patients. The results indicate that LDIR performed better than rigid registration, Demons, and intensity corrected Demons for all similarity metrics used. In conclusion, LDIR achieves high accuracy in the presence of multimodality intensity mismatch and CBCT noise contamination, while simultaneously preserving high computational efficiency. (paper)

Labeling of mesenchymal stem cells for MRI with single-cell sensitivity

Directory of Open Access Journals (Sweden)

Ariza de Schellenberger A

2016-04-01

Full Text Available Angela Ariza de Schellenberger,1 Harald Kratz,1 Tracy D Farr,2,3 Norbert Löwa,4 Ralf Hauptmann,1 Susanne Wagner,1 Matthias Taupitz,1 Jörg Schnorr,1 Eyk A Schellenberger1 1Department of Radiology, 2Department of Experimental Neurology, Center for Stroke Research Berlin, Charité – Universitätsmedizin Berlin, Berlin, Germany; 3School of Life Sciences, University of Nottingham, Medical School, Nottingham, UK; 4Department of Biomagnetic Signals, Physikalisch-Technische Bundesanstalt Berlin, Berlin, Germany Abstract: Sensitive cell detection by magnetic resonance imaging (MRI is an important tool for the development of cell therapies. However, clinically approved contrast agents that allow single-cell detection are currently not available. Therefore, we compared very small iron oxide nanoparticles (VSOP and new multicore carboxymethyl dextran-coated iron oxide nanoparticles (multicore particles, MCP designed by our department for magnetic particle imaging (MPI with discontinued Resovist® regarding their suitability for detection of single mesenchymal stem cells (MSC by MRI. We achieved an average intracellular nanoparticle (NP load of >10 pg Fe per cell without the use of transfection agents. NP loading did not lead to significantly different results in proliferation, colony formation, and multilineage in vitro differentiation assays in comparison to controls. MRI allowed single-cell detection using VSOP, MCP, and Resovist® in conjunction with high-resolution T2*-weighted imaging at 7 T with postprocessing of phase images in agarose cell phantoms and in vivo after delivery of 2,000 NP-labeled MSC into mouse brains via the left carotid artery. With optimized labeling conditions, a detection rate of ~45% was achieved; however, the experiments were limited by nonhomogeneous NP loading of the MSC population. Attempts should be made to achieve better cell separation for homogeneous NP loading and to thus improve NP

Automation of Cassini Support Imaging Uplink Command Development

Science.gov (United States)

Ly-Hollins, Lisa; Breneman, Herbert H.; Brooks, Robert

2010-01-01

"Support imaging" is imagery requested by other Cassini science teams to aid in the interpretation of their data. The generation of the spacecraft command sequences for these images is performed by the Cassini Instrument Operations Team. The process initially established for doing this was very labor-intensive, tedious and prone to human error. Team management recognized this process as one that could easily benefit from automation. Team members were tasked to document the existing manual process, develop a plan and strategy to automate the process, implement the plan and strategy, test and validate the new automated process, and deliver the new software tools and documentation to Flight Operations for use during the Cassini extended mission. In addition to the goals of higher efficiency and lower risk in the processing of support imaging requests, an effort was made to maximize adaptability of the process to accommodate uplink procedure changes and the potential addition of new capabilities outside the scope of the initial effort.

Imaging of single cells and tissue using MeV ions

International Nuclear Information System (INIS)

Watt, F.; Bettiol, A.A.; Kan, J.A. van; Ynsa, M.D.; Ren Minqin; Rajendran, R.; Cui Huifang; Sheu, F.-S.; Jenner, A.M.

2009-01-01

With the attainment of sub-100 nm high energy (MeV) ion beams, comes the opportunity to image cells and tissue at nano-dimensions. The advantage of MeV ion imaging is that the ions will penetrate whole cells, or relatively thick tissue sections, without any significant loss of resolution. In this paper, we demonstrate that whole cells (cultured N2A neuroblastoma cells ATCC) and tissue sections (rabbit pancreas tissue) can be imaged at sub-100 nm resolutions using scanning transmission ion microscopy (STIM), and that sub-cellular structural details can be identified. In addition to STIM imaging we have also demonstrated for the first time, that sub-cellular proton induced fluorescence imaging (on cultured N2A neuroblastoma cells ATCC) can also be carried out at resolutions of 200 nm, compared with 300-400 nm resolutions achieved by conventional optical fluorescence imaging. The combination of both techniques offers a potentially powerful tool in the quest for elucidating cell function, particularly when it should be possible in the near future to image down to sub-50 nm.

Fast-FISH Detection and Semi-Automated Image Analysis of Numerical Chromosome Aberrations in Hematological Malignancies

Directory of Open Access Journals (Sweden)

Arif Esa

1998-01-01

Full Text Available A new fluorescence in situ hybridization (FISH technique called Fast-FISH in combination with semi-automated image analysis was applied to detect numerical aberrations of chromosomes 8 and 12 in interphase nuclei of peripheral blood lymphocytes and bone marrow cells from patients with acute myelogenous leukemia (AML and chronic lymphocytic leukemia (CLL. Commercially available α-satellite DNA probes specific for the centromere regions of chromosome 8 and chromosome 12, respectively, were used. After application of the Fast-FISH protocol, the microscopic images of the fluorescence-labelled cell nuclei were recorded by the true color CCD camera Kappa CF 15 MC and evaluated quantitatively by computer analysis on a PC. These results were compared to results obtained from the same type of specimens using the same analysis system but with a standard FISH protocol. In addition, automated spot counting after both FISH techniques was compared to visual spot counting after standard FISH. A total number of about 3,000 cell nuclei was evaluated. For quantitative brightness parameters, a good correlation between standard FISH labelling and Fast-FISH was found. Automated spot counting after Fast-FISH coincided within a few percent to automated and visual spot counting after standard FISH. The examples shown indicate the reliability and reproducibility of Fast-FISH and its potential for automatized interphase cell diagnostics of numerical chromosome aberrations. Since the Fast-FISH technique requires a hybridization time as low as 1/20 of established standard FISH techniques, omitting most of the time consuming working steps in the protocol, it may contribute considerably to clinical diagnostics. This may especially be interesting in cases where an accurate result is required within a few hours.

Context based mixture model for cell phase identification in automated fluorescence microscopy

Directory of Open Access Journals (Sweden)

Zhou Xiaobo

2007-01-01

Full Text Available Abstract Background Automated identification of cell cycle phases of individual live cells in a large population captured via automated fluorescence microscopy technique is important for cancer drug discovery and cell cycle studies. Time-lapse fluorescence microscopy images provide an important method to study the cell cycle process under different conditions of perturbation. Existing methods are limited in dealing with such time-lapse data sets while manual analysis is not feasible. This paper presents statistical data analysis and statistical pattern recognition to perform this task. Results The data is generated from Hela H2B GFP cells imaged during a 2-day period with images acquired 15 minutes apart using an automated time-lapse fluorescence microscopy. The patterns are described with four kinds of features, including twelve general features, Haralick texture features, Zernike moment features, and wavelet features. To generate a new set of features with more discriminate power, the commonly used feature reduction techniques are used, which include Principle Component Analysis (PCA, Linear Discriminant Analysis (LDA, Maximum Margin Criterion (MMC, Stepwise Discriminate Analysis based Feature Selection (SDAFS, and Genetic Algorithm based Feature Selection (GAFS. Then, we propose a Context Based Mixture Model (CBMM for dealing with the time-series cell sequence information and compare it to other traditional classifiers: Support Vector Machine (SVM, Neural Network (NN, and K-Nearest Neighbor (KNN. Being a standard practice in machine learning, we systematically compare the performance of a number of common feature reduction techniques and classifiers to select an optimal combination of a feature reduction technique and a classifier. A cellular database containing 100 manually labelled subsequence is built for evaluating the performance of the classifiers. The generalization error is estimated using the cross validation technique. The

Automated Processing of Imaging Data through Multi-tiered Classification of Biological Structures Illustrated Using Caenorhabditis elegans.

Directory of Open Access Journals (Sweden)

Mei Zhan

2015-04-01

Full Text Available Quantitative imaging has become a vital technique in biological discovery and clinical diagnostics; a plethora of tools have recently been developed to enable new and accelerated forms of biological investigation. Increasingly, the capacity for high-throughput experimentation provided by new imaging modalities, contrast techniques, microscopy tools, microfluidics and computer controlled systems shifts the experimental bottleneck from the level of physical manipulation and raw data collection to automated recognition and data processing. Yet, despite their broad importance, image analysis solutions to address these needs have been narrowly tailored. Here, we present a generalizable formulation for autonomous identification of specific biological structures that is applicable for many problems. The process flow architecture we present here utilizes standard image processing techniques and the multi-tiered application of classification models such as support vector machines (SVM. These low-level functions are readily available in a large array of image processing software packages and programming languages. Our framework is thus both easy to implement at the modular level and provides specific high-level architecture to guide the solution of more complicated image-processing problems. We demonstrate the utility of the classification routine by developing two specific classifiers as a toolset for automation and cell identification in the model organism Caenorhabditis elegans. To serve a common need for automated high-resolution imaging and behavior applications in the C. elegans research community, we contribute a ready-to-use classifier for the identification of the head of the animal under bright field imaging. Furthermore, we extend our framework to address the pervasive problem of cell-specific identification under fluorescent imaging, which is critical for biological investigation in multicellular organisms or tissues. Using these examples as a

Automated Processing of Imaging Data through Multi-tiered Classification of Biological Structures Illustrated Using Caenorhabditis elegans.

Science.gov (United States)

Zhan, Mei; Crane, Matthew M; Entchev, Eugeni V; Caballero, Antonio; Fernandes de Abreu, Diana Andrea; Ch'ng, QueeLim; Lu, Hang

2015-04-01

Quantitative imaging has become a vital technique in biological discovery and clinical diagnostics; a plethora of tools have recently been developed to enable new and accelerated forms of biological investigation. Increasingly, the capacity for high-throughput experimentation provided by new imaging modalities, contrast techniques, microscopy tools, microfluidics and computer controlled systems shifts the experimental bottleneck from the level of physical manipulation and raw data collection to automated recognition and data processing. Yet, despite their broad importance, image analysis solutions to address these needs have been narrowly tailored. Here, we present a generalizable formulation for autonomous identification of specific biological structures that is applicable for many problems. The process flow architecture we present here utilizes standard image processing techniques and the multi-tiered application of classification models such as support vector machines (SVM). These low-level functions are readily available in a large array of image processing software packages and programming languages. Our framework is thus both easy to implement at the modular level and provides specific high-level architecture to guide the solution of more complicated image-processing problems. We demonstrate the utility of the classification routine by developing two specific classifiers as a toolset for automation and cell identification in the model organism Caenorhabditis elegans. To serve a common need for automated high-resolution imaging and behavior applications in the C. elegans research community, we contribute a ready-to-use classifier for the identification of the head of the animal under bright field imaging. Furthermore, we extend our framework to address the pervasive problem of cell-specific identification under fluorescent imaging, which is critical for biological investigation in multicellular organisms or tissues. Using these examples as a guide, we envision

Automated data processing architecture for the Gemini Planet Imager Exoplanet Survey

Science.gov (United States)

Wang, Jason J.; Perrin, Marshall D.; Savransky, Dmitry; Arriaga, Pauline; Chilcote, Jeffrey K.; De Rosa, Robert J.; Millar-Blanchaer, Maxwell A.; Marois, Christian; Rameau, Julien; Wolff, Schuyler G.; Shapiro, Jacob; Ruffio, Jean-Baptiste; Maire, Jérôme; Marchis, Franck; Graham, James R.; Macintosh, Bruce; Ammons, S. Mark; Bailey, Vanessa P.; Barman, Travis S.; Bruzzone, Sebastian; Bulger, Joanna; Cotten, Tara; Doyon, René; Duchêne, Gaspard; Fitzgerald, Michael P.; Follette, Katherine B.; Goodsell, Stephen; Greenbaum, Alexandra Z.; Hibon, Pascale; Hung, Li-Wei; Ingraham, Patrick; Kalas, Paul; Konopacky, Quinn M.; Larkin, James E.; Marley, Mark S.; Metchev, Stanimir; Nielsen, Eric L.; Oppenheimer, Rebecca; Palmer, David W.; Patience, Jennifer; Poyneer, Lisa A.; Pueyo, Laurent; Rajan, Abhijith; Rantakyrö, Fredrik T.; Schneider, Adam C.; Sivaramakrishnan, Anand; Song, Inseok; Soummer, Remi; Thomas, Sandrine; Wallace, J. Kent; Ward-Duong, Kimberly; Wiktorowicz, Sloane J.

2018-01-01

The Gemini Planet Imager Exoplanet Survey (GPIES) is a multiyear direct imaging survey of 600 stars to discover and characterize young Jovian exoplanets and their environments. We have developed an automated data architecture to process and index all data related to the survey uniformly. An automated and flexible data processing framework, which we term the Data Cruncher, combines multiple data reduction pipelines (DRPs) together to process all spectroscopic, polarimetric, and calibration data taken with GPIES. With no human intervention, fully reduced and calibrated data products are available less than an hour after the data are taken to expedite follow up on potential objects of interest. The Data Cruncher can run on a supercomputer to reprocess all GPIES data in a single day as improvements are made to our DRPs. A backend MySQL database indexes all files, which are synced to the cloud, and a front-end web server allows for easy browsing of all files associated with GPIES. To help observers, quicklook displays show reduced data as they are processed in real time, and chatbots on Slack post observing information as well as reduced data products. Together, the GPIES automated data processing architecture reduces our workload, provides real-time data reduction, optimizes our observing strategy, and maintains a homogeneously reduced dataset to study planet occurrence and instrument performance.

How automated image analysis techniques help scientists in species identification and classification?

Science.gov (United States)

Yousef Kalafi, Elham; Town, Christopher; Kaur Dhillon, Sarinder

2017-09-04

Identification of taxonomy at a specific level is time consuming and reliant upon expert ecologists. Hence the demand for automated species identification increased over the last two decades. Automation of data classification is primarily focussed on images, incorporating and analysing image data has recently become easier due to developments in computational technology. Research efforts in identification of species include specimens' image processing, extraction of identical features, followed by classifying them into correct categories. In this paper, we discuss recent automated species identification systems, categorizing and evaluating their methods. We reviewed and compared different methods in step by step scheme of automated identification and classification systems of species images. The selection of methods is influenced by many variables such as level of classification, number of training data and complexity of images. The aim of writing this paper is to provide researchers and scientists an extensive background study on work related to automated species identification, focusing on pattern recognition techniques in building such systems for biodiversity studies.

An Automated, Image Processing System for Concrete Evaluation

International Nuclear Information System (INIS)

Baumgart, C.W.; Cave, S.P.; Linder, K.E.

1998-01-01

Allied Signal Federal Manufacturing ampersand Technologies (FM ampersand T) was asked to perform a proof-of-concept study for the Missouri Highway and Transportation Department (MHTD), Research Division, in June 1997. The goal of this proof-of-concept study was to ascertain if automated scanning and imaging techniques might be applied effectively to the problem of concrete evaluation. In the current evaluation process, a concrete sample core is manually scanned under a microscope. Voids (or air spaces) within the concrete are then detected visually by a human operator by incrementing the sample under the cross-hairs of a microscope and by counting the number of ''pixels'' which fall within a void. Automation of the scanning and image analysis processes is desired to improve the speed of the scanning process, to improve evaluation consistency, and to reduce operator fatigue. An initial, proof-of-concept image analysis approach was successfully developed and demonstrated using acquired black and white imagery of concrete samples. In this paper, the automated scanning and image capture system currently under development will be described and the image processing approach developed for the proof-of-concept study will be demonstrated. A development update and plans for future enhancements are also presented

Sequential processing of quantitative phase images for the study of cell behaviour in real-time digital holographic microscopy.

Science.gov (United States)

Zikmund, T; Kvasnica, L; Týč, M; Křížová, A; Colláková, J; Chmelík, R

2014-11-01

Transmitted light holographic microscopy is particularly used for quantitative phase imaging of transparent microscopic objects such as living cells. The study of the cell is based on extraction of the dynamic data on cell behaviour from the time-lapse sequence of the phase images. However, the phase images are affected by the phase aberrations that make the analysis particularly difficult. This is because the phase deformation is prone to change during long-term experiments. Here, we present a novel algorithm for sequential processing of living cells phase images in a time-lapse sequence. The algorithm compensates for the deformation of a phase image using weighted least-squares surface fitting. Moreover, it identifies and segments the individual cells in the phase image. All these procedures are performed automatically and applied immediately after obtaining every single phase image. This property of the algorithm is important for real-time cell quantitative phase imaging and instantaneous control of the course of the experiment by playback of the recorded sequence up to actual time. Such operator's intervention is a forerunner of process automation derived from image analysis. The efficiency of the propounded algorithm is demonstrated on images of rat fibrosarcoma cells using an off-axis holographic microscope. © 2014 The Authors Journal of Microscopy © 2014 Royal Microscopical Society.

An automated image processing method for classification of diabetic retinopathy stages from conjunctival microvasculature images

Science.gov (United States)

Khansari, Maziyar M.; O'Neill, William; Penn, Richard; Blair, Norman P.; Chau, Felix; Shahidi, Mahnaz

2017-03-01

The conjunctiva is a densely vascularized tissue of the eye that provides an opportunity for imaging of human microcirculation. In the current study, automated fine structure analysis of conjunctival microvasculature images was performed to discriminate stages of diabetic retinopathy (DR). The study population consisted of one group of nondiabetic control subjects (NC) and 3 groups of diabetic subjects, with no clinical DR (NDR), non-proliferative DR (NPDR), or proliferative DR (PDR). Ordinary least square regression and Fisher linear discriminant analyses were performed to automatically discriminate images between group pairs of subjects. Human observers who were masked to the grouping of subjects performed image discrimination between group pairs. Over 80% and 70% of images of subjects with clinical and non-clinical DR were correctly discriminated by the automated method, respectively. The discrimination rates of the automated method were higher than human observers. The fine structure analysis of conjunctival microvasculature images provided discrimination of DR stages and can be potentially useful for DR screening and monitoring.

Automated Image Analysis Corrosion Working Group Update: February 1, 2018

Energy Technology Data Exchange (ETDEWEB)

Wendelberger, James G. [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

2018-02-01

These are slides for the automated image analysis corrosion working group update. The overall goals were: automate the detection and quantification of features in images (faster, more accurate), how to do this (obtain data, analyze data), focus on Laser Scanning Confocal Microscope (LCM) data (laser intensity, laser height/depth, optical RGB, optical plus laser RGB).

Design of a Single-Cell Positioning Controller Using Electroosmotic Flow and Image Processing

Directory of Open Access Journals (Sweden)

Jhong-Yin Chen

2013-05-01

Full Text Available The objective of the current research was not only to provide a fast and automatic positioning platform for single cells, but also improved biomolecular manipulation techniques. In this study, an automatic platform for cell positioning using electroosmotic flow and image processing technology was designed. The platform was developed using a PCI image acquisition interface card for capturing images from a microscope and then transferring them to a computer using human-machine interface software. This software was designed by the Laboratory Virtual Instrument Engineering Workbench, a graphical language for finding cell positions and viewing the driving trace, and the fuzzy logic method for controlling the voltage or time of an electric field. After experiments on real human leukemic cells (U-937, the success of the cell positioning rate achieved by controlling the voltage factor reaches 100% within 5 s. A greater precision is obtained when controlling the time factor, whereby the success rate reaches 100% within 28 s. Advantages in both high speed and high precision are attained if these two voltage and time control methods are combined. The control speed with the combined method is about 5.18 times greater than that achieved by the time method, and the control precision with the combined method is more than five times greater than that achieved by the voltage method.

Automated facial acne assessment from smartphone images

Science.gov (United States)

Amini, Mohammad; Vasefi, Fartash; Valdebran, Manuel; Huang, Kevin; Zhang, Haomiao; Kemp, William; MacKinnon, Nicholas

2018-02-01

A smartphone mobile medical application is presented, that provides analysis of the health of skin on the face using a smartphone image and cloud-based image processing techniques. The mobile application employs the use of the camera to capture a front face image of a subject, after which the captured image is spatially calibrated based on fiducial points such as position of the iris of the eye. A facial recognition algorithm is used to identify features of the human face image, to normalize the image, and to define facial regions of interest (ROI) for acne assessment. We identify acne lesions and classify them into two categories: those that are papules and those that are pustules. Automated facial acne assessment was validated by performing tests on images of 60 digital human models and 10 real human face images. The application was able to identify 92% of acne lesions within five facial ROIs. The classification accuracy for separating papules from pustules was 98%. Combined with in-app documentation of treatment, lifestyle factors, and automated facial acne assessment, the app can be used in both cosmetic and clinical dermatology. It allows users to quantitatively self-measure acne severity and treatment efficacy on an ongoing basis to help them manage their chronic facial acne.

iSBatch: a batch-processing platform for data analysis and exploration of live-cell single-molecule microscopy images and other hierarchical datasets.

Science.gov (United States)

Caldas, Victor E A; Punter, Christiaan M; Ghodke, Harshad; Robinson, Andrew; van Oijen, Antoine M

2015-10-01

Recent technical advances have made it possible to visualize single molecules inside live cells. Microscopes with single-molecule sensitivity enable the imaging of low-abundance proteins, allowing for a quantitative characterization of molecular properties. Such data sets contain information on a wide spectrum of important molecular properties, with different aspects highlighted in different imaging strategies. The time-lapsed acquisition of images provides information on protein dynamics over long time scales, giving insight into expression dynamics and localization properties. Rapid burst imaging reveals properties of individual molecules in real-time, informing on their diffusion characteristics, binding dynamics and stoichiometries within complexes. This richness of information, however, adds significant complexity to analysis protocols. In general, large datasets of images must be collected and processed in order to produce statistically robust results and identify rare events. More importantly, as live-cell single-molecule measurements remain on the cutting edge of imaging, few protocols for analysis have been established and thus analysis strategies often need to be explored for each individual scenario. Existing analysis packages are geared towards either single-cell imaging data or in vitro single-molecule data and typically operate with highly specific algorithms developed for particular situations. Our tool, iSBatch, instead allows users to exploit the inherent flexibility of the popular open-source package ImageJ, providing a hierarchical framework in which existing plugins or custom macros may be executed over entire datasets or portions thereof. This strategy affords users freedom to explore new analysis protocols within large imaging datasets, while maintaining hierarchical relationships between experiments, samples, fields of view, cells, and individual molecules.

Bioinformatics approaches to single-cell analysis in developmental biology.

Science.gov (United States)

Yalcin, Dicle; Hakguder, Zeynep M; Otu, Hasan H

2016-03-01

Individual cells within the same population show various degrees of heterogeneity, which may be better handled with single-cell analysis to address biological and clinical questions. Single-cell analysis is especially important in developmental biology as subtle spatial and temporal differences in cells have significant associations with cell fate decisions during differentiation and with the description of a particular state of a cell exhibiting an aberrant phenotype. Biotechnological advances, especially in the area of microfluidics, have led to a robust, massively parallel and multi-dimensional capturing, sorting, and lysis of single-cells and amplification of related macromolecules, which have enabled the use of imaging and omics techniques on single cells. There have been improvements in computational single-cell image analysis in developmental biology regarding feature extraction, segmentation, image enhancement and machine learning, handling limitations of optical resolution to gain new perspectives from the raw microscopy images. Omics approaches, such as transcriptomics, genomics and epigenomics, targeting gene and small RNA expression, single nucleotide and structural variations and methylation and histone modifications, rely heavily on high-throughput sequencing technologies. Although there are well-established bioinformatics methods for analysis of sequence data, there are limited bioinformatics approaches which address experimental design, sample size considerations, amplification bias, normalization, differential expression, coverage, clustering and classification issues, specifically applied at the single-cell level. In this review, we summarize biological and technological advancements, discuss challenges faced in the aforementioned data acquisition and analysis issues and present future prospects for application of single-cell analyses to developmental biology. © The Author 2015. Published by Oxford University Press on behalf of the European

Immunohistochemical Ki-67/KL1 double stains increase accuracy of Ki-67 indices in breast cancer and simplify automated image analysis

DEFF Research Database (Denmark)

Nielsen, Patricia S; Bentzer, Nina K; Jensen, Vibeke

2014-01-01

observers and automated image analysis. RESULTS: Indices were predominantly higher for single stains than double stains (P≤0.002), yet the difference between observers was statistically significant (PPearson correlation coefficient for manual and automated indices ranged from 0.......69 to 0.85 (Pcorrelating automated indices with tumor characteristics, for example, tumor size (P... stains, Ki-67 should be quantified on double stains to reach a higher accuracy. Automated indices correlated well with manual estimates and tumor characteristics, and they are thus possibly valuable tools in future exploration of Ki-67 in breast cancer....

[The segmentation of urinary cells--a first step in the automated processing in urine cytology (author's transl)].

Science.gov (United States)

Liedtke, C E; Aeikens, B

1980-01-01

By segmentation of cell images we understand the automated decomposition of microscopic cell scenes into nucleus, plasma and background. A segmentation is achieved by using information from the microscope image and prior knowledge about the content of the scene. Different algorithms have been investigated and applied to samples of urothelial cells. A particular algorithm based on a histogram approach which can be easily implemented in hardware is discussed in more detail.

Long-term maintenance of human induced pluripotent stem cells by automated cell culture system.

Science.gov (United States)

Konagaya, Shuhei; Ando, Takeshi; Yamauchi, Toshiaki; Suemori, Hirofumi; Iwata, Hiroo

2015-11-17

Pluripotent stem cells, such as embryonic stem cells and induced pluripotent stem (iPS) cells, are regarded as new sources for cell replacement therapy. These cells can unlimitedly expand under undifferentiated conditions and be differentiated into multiple cell types. Automated culture systems enable the large-scale production of cells. In addition to reducing the time and effort of researchers, an automated culture system improves the reproducibility of cell cultures. In the present study, we newly designed a fully automated cell culture system for human iPS maintenance. Using an automated culture system, hiPS cells maintained their undifferentiated state for 60 days. Automatically prepared hiPS cells had a potency of differentiation into three germ layer cells including dopaminergic neurons and pancreatic cells.

Imaging Live Cells at the Nanometer-Scale with Single-Molecule Microscopy: Obstacles and Achievements in Experiment Optimization for Microbiology

Science.gov (United States)

Haas, Beth L.; Matson, Jyl S.; DiRita, Victor J.; Biteen, Julie S.

2015-01-01

Single-molecule fluorescence microscopy enables biological investigations inside living cells to achieve millisecond- and nanometer-scale resolution. Although single-molecule-based methods are becoming increasingly accessible to non-experts, optimizing new single-molecule experiments can be challenging, in particular when super-resolution imaging and tracking are applied to live cells. In this review, we summarize common obstacles to live-cell single-molecule microscopy and describe the methods we have developed and applied to overcome these challenges in live bacteria. We examine the choice of fluorophore and labeling scheme, approaches to achieving single-molecule levels of fluorescence, considerations for maintaining cell viability, and strategies for detecting single-molecule signals in the presence of noise and sample drift. We also discuss methods for analyzing single-molecule trajectories and the challenges presented by the finite size of a bacterial cell and the curvature of the bacterial membrane. PMID:25123183

Under the Microscope: Single-Domain Antibodies for Live-Cell Imaging and Super-Resolution Microscopy

Directory of Open Access Journals (Sweden)

Bjoern Traenkle

2017-08-01

Full Text Available Single-domain antibodies (sdAbs have substantially expanded the possibilities of advanced cellular imaging such as live-cell or super-resolution microscopy to visualize cellular antigens and their dynamics. In addition to their unique properties including small size, high stability, and solubility in many environments, sdAbs can be efficiently functionalized according to the needs of the respective imaging approach. Genetically encoded intrabodies fused to fluorescent proteins (chromobodies have become versatile tools to study dynamics of endogenous proteins in living cells. Additionally, sdAbs conjugated to organic dyes were shown to label cellular structures with high density and minimal fluorophore displacement making them highly attractive probes for super-resolution microscopy. Here, we review recent advances of the chromobody technology to visualize localization and dynamics of cellular targets and the application of chromobody-based cell models for compound screening. Acknowledging the emerging importance of super-resolution microscopy in cell biology, we further discuss advantages and challenges of sdAbs for this technology.

Under the Microscope: Single-Domain Antibodies for Live-Cell Imaging and Super-Resolution Microscopy.

Science.gov (United States)

Traenkle, Bjoern; Rothbauer, Ulrich

2017-01-01

Single-domain antibodies (sdAbs) have substantially expanded the possibilities of advanced cellular imaging such as live-cell or super-resolution microscopy to visualize cellular antigens and their dynamics. In addition to their unique properties including small size, high stability, and solubility in many environments, sdAbs can be efficiently functionalized according to the needs of the respective imaging approach. Genetically encoded intrabodies fused to fluorescent proteins (chromobodies) have become versatile tools to study dynamics of endogenous proteins in living cells. Additionally, sdAbs conjugated to organic dyes were shown to label cellular structures with high density and minimal fluorophore displacement making them highly attractive probes for super-resolution microscopy. Here, we review recent advances of the chromobody technology to visualize localization and dynamics of cellular targets and the application of chromobody-based cell models for compound screening. Acknowledging the emerging importance of super-resolution microscopy in cell biology, we further discuss advantages and challenges of sdAbs for this technology.

Single-photon imaging

International Nuclear Information System (INIS)

Seitz, Peter; Theuwissen, Albert J.P.

2011-01-01

The acquisition and interpretation of images is a central capability in almost all scientific and technological domains. In particular, the acquisition of electromagnetic radiation, in the form of visible light, UV, infrared, X-ray, etc. is of enormous practical importance. The ultimate sensitivity in electronic imaging is the detection of individual photons. With this book, the first comprehensive review of all aspects of single-photon electronic imaging has been created. Topics include theoretical basics, semiconductor fabrication, single-photon detection principles, imager design and applications of different spectral domains. Today, the solid-state fabrication capabilities for several types of image sensors has advanced to a point, where uncooled single-photon electronic imaging will soon become a consumer product. This book is giving a specialist's view from different domains to the forthcoming ''single-photon imaging'' revolution. The various aspects of single-photon imaging are treated by internationally renowned, leading scientists and technologists who have all pioneered their respective fields. (orig.)

Radiographic examination takes on an automated image

International Nuclear Information System (INIS)

Aman, J.

1988-01-01

Automation can be effectively applied to nondestructive testing (NDT). Until recently, film radiography used in NDT was largely a manual process, involving the shooting of a series of x-rays, manually positioned and manually processed. In other words, much radiographic work is being done the way it was over 50 years ago. Significant advances in automation have changed the face of manufacturing, and industry has shared in the benefits brought by such progress. The handling of parts, which was once responsible for a large measure of labor costs, is now assigned to robotic equipment. In nondestructive testing processes, some progress has been achieved in automation - for example, in real-time imaging systems. However, only recently have truly automated NDT begun to emerge. There are two major reasons to introduce automation into NDT - reliability and productivity. Any process or technique that can improve the reliability of parts testing could easily justify the capital investments required

Single-photon imaging

CERN Document Server

Seitz, Peter

2011-01-01

The acquisition and interpretation of images is a central capability in almost all scientific and technological domains. In particular, the acquisition of electromagnetic radiation, in the form of visible light, UV, infrared, X-ray, etc. is of enormous practical importance. The ultimate sensitivity in electronic imaging is the detection of individual photons. With this book, the first comprehensive review of all aspects of single-photon electronic imaging has been created. Topics include theoretical basics, semiconductor fabrication, single-photon detection principles, imager design and applications of different spectral domains. Today, the solid-state fabrication capabilities for several types of image sensors has advanced to a point, where uncoooled single-photon electronic imaging will soon become a consumer product. This book is giving a specialist´s view from different domains to the forthcoming “single-photon imaging” revolution. The various aspects of single-photon imaging are treated by internati...

A robotics platform for automated batch fabrication of high density, microfluidics-based DNA microarrays, with applications to single cell, multiplex assays of secreted proteins

Science.gov (United States)

Ahmad, Habib; Sutherland, Alex; Shin, Young Shik; Hwang, Kiwook; Qin, Lidong; Krom, Russell-John; Heath, James R.

2011-09-01

Microfluidics flow-patterning has been utilized for the construction of chip-scale miniaturized DNA and protein barcode arrays. Such arrays have been used for specific clinical and fundamental investigations in which many proteins are assayed from single cells or other small sample sizes. However, flow-patterned arrays are hand-prepared, and so are impractical for broad applications. We describe an integrated robotics/microfluidics platform for the automated preparation of such arrays, and we apply it to the batch fabrication of up to eighteen chips of flow-patterned DNA barcodes. The resulting substrates are comparable in quality with hand-made arrays and exhibit excellent substrate-to-substrate consistency. We demonstrate the utility and reproducibility of robotics-patterned barcodes by utilizing two flow-patterned chips for highly parallel assays of a panel of secreted proteins from single macrophage cells.

A robotics platform for automated batch fabrication of high density, microfluidics-based DNA microarrays, with applications to single cell, multiplex assays of secreted proteins.

Science.gov (United States)

Ahmad, Habib; Sutherland, Alex; Shin, Young Shik; Hwang, Kiwook; Qin, Lidong; Krom, Russell-John; Heath, James R

2011-09-01

Microfluidics flow-patterning has been utilized for the construction of chip-scale miniaturized DNA and protein barcode arrays. Such arrays have been used for specific clinical and fundamental investigations in which many proteins are assayed from single cells or other small sample sizes. However, flow-patterned arrays are hand-prepared, and so are impractical for broad applications. We describe an integrated robotics/microfluidics platform for the automated preparation of such arrays, and we apply it to the batch fabrication of up to eighteen chips of flow-patterned DNA barcodes. The resulting substrates are comparable in quality with hand-made arrays and exhibit excellent substrate-to-substrate consistency. We demonstrate the utility and reproducibility of robotics-patterned barcodes by utilizing two flow-patterned chips for highly parallel assays of a panel of secreted proteins from single macrophage cells. © 2011 American Institute of Physics

Single-Cell RNA Sequencing of Glioblastoma Cells.

Science.gov (United States)

Sen, Rajeev; Dolgalev, Igor; Bayin, N Sumru; Heguy, Adriana; Tsirigos, Aris; Placantonakis, Dimitris G

2018-01-01

Single-cell RNA sequencing (sc-RNASeq) is a recently developed technique used to evaluate the transcriptome of individual cells. As opposed to conventional RNASeq in which entire populations are sequenced in bulk, sc-RNASeq can be beneficial when trying to better understand gene expression patterns in markedly heterogeneous populations of cells or when trying to identify transcriptional signatures of rare cells that may be underrepresented when using conventional bulk RNASeq. In this method, we describe the generation and analysis of cDNA libraries from single patient-derived glioblastoma cells using the C1 Fluidigm system. The protocol details the use of the C1 integrated fluidics circuit (IFC) for capturing, imaging and lysing cells; performing reverse transcription; and generating cDNA libraries that are ready for sequencing and analysis.

Localization-based super-resolution imaging meets high-content screening.

Science.gov (United States)

Beghin, Anne; Kechkar, Adel; Butler, Corey; Levet, Florian; Cabillic, Marine; Rossier, Olivier; Giannone, Gregory; Galland, Rémi; Choquet, Daniel; Sibarita, Jean-Baptiste

2017-12-01

Single-molecule localization microscopy techniques have proven to be essential tools for quantitatively monitoring biological processes at unprecedented spatial resolution. However, these techniques are very low throughput and are not yet compatible with fully automated, multiparametric cellular assays. This shortcoming is primarily due to the huge amount of data generated during imaging and the lack of software for automation and dedicated data mining. We describe an automated quantitative single-molecule-based super-resolution methodology that operates in standard multiwell plates and uses analysis based on high-content screening and data-mining software. The workflow is compatible with fixed- and live-cell imaging and allows extraction of quantitative data like fluorophore photophysics, protein clustering or dynamic behavior of biomolecules. We demonstrate that the method is compatible with high-content screening using 3D dSTORM and DNA-PAINT based super-resolution microscopy as well as single-particle tracking.

An Algorithm to Automate Yeast Segmentation and Tracking

Science.gov (United States)

Doncic, Andreas; Eser, Umut; Atay, Oguzhan; Skotheim, Jan M.

2013-01-01

Our understanding of dynamic cellular processes has been greatly enhanced by rapid advances in quantitative fluorescence microscopy. Imaging single cells has emphasized the prevalence of phenomena that can be difficult to infer from population measurements, such as all-or-none cellular decisions, cell-to-cell variability, and oscillations. Examination of these phenomena requires segmenting and tracking individual cells over long periods of time. However, accurate segmentation and tracking of cells is difficult and is often the rate-limiting step in an experimental pipeline. Here, we present an algorithm that accomplishes fully automated segmentation and tracking of budding yeast cells within growing colonies. The algorithm incorporates prior information of yeast-specific traits, such as immobility and growth rate, to segment an image using a set of threshold values rather than one specific optimized threshold. Results from the entire set of thresholds are then used to perform a robust final segmentation. PMID:23520484

Shielded cells transfer automation

International Nuclear Information System (INIS)

Fisher, J.J.

1984-01-01

Nuclear waste from shielded cells is removed, packaged, and transferred manually in many nuclear facilities. Radiation exposure is absorbed by operators during these operations and limited only through procedural controls. Technological advances in automation using robotics have allowed a production waste removal operation to be automated to reduce radiation exposure. The robotic system bags waste containers out of glove box and transfers them to a shielded container. Operators control the system outside the system work area via television cameras. 9 figures

Single-cell real-time imaging of transgene expression upon lipofection

Energy Technology Data Exchange (ETDEWEB)

Fiume, Giuseppe [Center for Nanotechnology Innovation @NEST, Istituto Italiano di Tecnologia, Piazza San Silvestro 12, 56127 Pisa (Italy); Di Rienzo, Carmine [Center for Nanotechnology Innovation @NEST, Istituto Italiano di Tecnologia, Piazza San Silvestro 12, 56127 Pisa (Italy); NEST, Scuola Normale Superiore and Istituto Nanoscienze-CNR, Piazza San Silvestro 12, 56127, Pisa (Italy); Marchetti, Laura [Center for Nanotechnology Innovation @NEST, Istituto Italiano di Tecnologia, Piazza San Silvestro 12, 56127 Pisa (Italy); Pozzi, Daniela; Caracciolo, Giulio [Department of Molecular Medicine, “Sapienza” University of Rome, Viale Regina Elena 291, 00161, Rome (Italy); Cardarelli, Francesco, E-mail: francesco.cardarelli@iit.it [Center for Nanotechnology Innovation @NEST, Istituto Italiano di Tecnologia, Piazza San Silvestro 12, 56127 Pisa (Italy)

2016-05-20

Here we address the process of lipofection by quantifying the expression of a genetically-encoded fluorescent reporter at the single-cell level, and in real-time, by confocal imaging in live cells. The Lipofectamine gold-standard formulation is compared to the alternative promising DC-Chol/DOPE formulation. In both cases, we report that only dividing cells are able to produce a detectable amount of the fluorescent reporter protein. Notably, by measuring fluorescence over time in each pair of daughter cells, we find that Lipofectamine-based transfection statistically yields a remarkably higher degree of “symmetry” in protein expression between daughter cells as compared to DC-Chol/DOPE. A model is envisioned in which the degree of symmetry of protein expression is linked to the number of bioavailable DNA copies within the cell before nuclear breakdown. Reported results open new perspectives for the understanding of the lipofection mechanism and define a new experimental platform for the quantitative comparison of transfection reagents. -- Highlights: •The process of lipofection is followed by quantifying the transgene expression in real time. •The Lipofectamine gold-standard is compared to the promising DC-Chol/DOPE formulation. •We report that only dividing cells are able to produce the fluorescent reporter protein. •The degree of symmetry of protein expression in daughter cells is linked to DNA bioavailability. •A new experimental platform for the quantitative comparison of transfection reagents is proposed.

Single-cell real-time imaging of transgene expression upon lipofection

International Nuclear Information System (INIS)

Fiume, Giuseppe; Di Rienzo, Carmine; Marchetti, Laura; Pozzi, Daniela; Caracciolo, Giulio; Cardarelli, Francesco

2016-01-01

Here we address the process of lipofection by quantifying the expression of a genetically-encoded fluorescent reporter at the single-cell level, and in real-time, by confocal imaging in live cells. The Lipofectamine gold-standard formulation is compared to the alternative promising DC-Chol/DOPE formulation. In both cases, we report that only dividing cells are able to produce a detectable amount of the fluorescent reporter protein. Notably, by measuring fluorescence over time in each pair of daughter cells, we find that Lipofectamine-based transfection statistically yields a remarkably higher degree of “symmetry” in protein expression between daughter cells as compared to DC-Chol/DOPE. A model is envisioned in which the degree of symmetry of protein expression is linked to the number of bioavailable DNA copies within the cell before nuclear breakdown. Reported results open new perspectives for the understanding of the lipofection mechanism and define a new experimental platform for the quantitative comparison of transfection reagents. -- Highlights: •The process of lipofection is followed by quantifying the transgene expression in real time. •The Lipofectamine gold-standard is compared to the promising DC-Chol/DOPE formulation. •We report that only dividing cells are able to produce the fluorescent reporter protein. •The degree of symmetry of protein expression in daughter cells is linked to DNA bioavailability. •A new experimental platform for the quantitative comparison of transfection reagents is proposed.

Fibered confocal fluorescence microscopy for imaging apoptotic DNA fragmentation at the single-cell level in vivo

International Nuclear Information System (INIS)

Al-Gubory, Kais H.

2005-01-01

The major characteristic of cell death by apoptosis is the loss of nuclear DNA integrity by endonucleases, resulting in the formation of small DNA fragments. The application of confocal imaging to in vivo monitoring of dynamic cellular events, like apoptosis, within internal organs and tissues has been limited by the accessibility to these sites. Therefore, the aim of the present study was to test the feasibility of fibered confocal fluorescence microscopy (FCFM) to image in situ apoptotic DNA fragmentation in surgically exteriorized sheep corpus luteum in the living animal. Following intra-luteal administration of a fluorescent DNA-staining dye, YO-PRO-1, DNA cleavage within nuclei of apoptotic cells was serially imaged at the single-cell level by FCFM. This imaging technology is sufficiently simple and rapid to allow time series in situ detection and visualization of cells undergoing apoptosis in the intact animal. Combined with endoscope, this approach can be used for minimally invasive detection of fluorescent signals and visualization of cellular events within internal organs and tissues and thereby provides the opportunity to study biological processes in the natural physiological environment of the cell in living animals

21 CFR 864.5200 - Automated cell counter.

Science.gov (United States)

2010-04-01

... 21 Food and Drugs 8 2010-04-01 2010-04-01 false Automated cell counter. 864.5200 Section 864.5200 Food and Drugs FOOD AND DRUG ADMINISTRATION, DEPARTMENT OF HEALTH AND HUMAN SERVICES (CONTINUED) MEDICAL DEVICES HEMATOLOGY AND PATHOLOGY DEVICES Automated and Semi-Automated Hematology Devices § 864...

Photothermal optical coherence tomography for depth-resolved imaging of mesenchymal stem cells via single wall carbon nanotubes

Science.gov (United States)

Subhash, Hrebesh M.; Connolly, Emma; Murphy, Mary; Barron, Valerie; Leahy, Martin

2014-03-01

The progress in stem cell research over the past decade holds promise and potential to address many unmet clinical therapeutic needs. Tracking stem cell with modern imaging modalities are critically needed for optimizing stem cell therapy, which offers insight into various underlying biological processes such as cell migration, engraftment, homing, differentiation, and functions etc. In this study we report the feasibility of photothermal optical coherence tomography (PT-OCT) to image human mesenchymal stem cells (hMSCs) labeled with single-walled carbon nanotubes (SWNTs) for in vitro cell tracking in three dimensional scaffolds. PT-OCT is a functional extension of conventional OCT with extended capability of localized detection of absorbing targets from scattering background to provide depth-resolved molecular contrast imaging. A 91 kHz line rate, spectral domain PT-OCT system at 1310nm was developed to detect the photothermal signal generated by 800nm excitation laser. In general, MSCs do not have obvious optical absorption properties and cannot be directly visualized using PT-OCT imaging. However, the optical absorption properties of hMSCs can me modified by labeling with SWNTs. Using this approach, MSC were labeled with SWNT and the cell distribution imaged in a 3D polymer scaffold using PT-OCT.

Automated setpoint adjustment for biological contact mode atomic force microscopy imaging

International Nuclear Information System (INIS)

Casuso, Ignacio; Scheuring, Simon

2010-01-01

Contact mode atomic force microscopy (AFM) is the most frequently used AFM imaging mode in biology. It is about 5-10 times faster than oscillating mode imaging (in conventional AFM setups), and provides topographs of biological samples with sub-molecular resolution and at a high signal-to-noise ratio. Unfortunately, contact mode imaging is sensitive to the applied force and intrinsic force drift: inappropriate force applied by the AFM tip damages the soft biological samples. We present a methodology that automatically searches for and maintains high resolution imaging forces. We found that the vertical and lateral vibrations of the probe during scanning are valuable signals for the characterization of the actual applied force by the tip. This allows automated adjustment and correction of the setpoint force during an experiment. A system that permanently performs this methodology steered the AFM towards high resolution imaging forces and imaged purple membrane at molecular resolution and live cells at high signal-to-noise ratio for hours without an operator.

Automated detection of diabetic retinopathy lesions on ultrawidefield pseudocolour images.

Science.gov (United States)

Wang, Kang; Jayadev, Chaitra; Nittala, Muneeswar G; Velaga, Swetha B; Ramachandra, Chaithanya A; Bhaskaranand, Malavika; Bhat, Sandeep; Solanki, Kaushal; Sadda, SriniVas R

2018-03-01

We examined the sensitivity and specificity of an automated algorithm for detecting referral-warranted diabetic retinopathy (DR) on Optos ultrawidefield (UWF) pseudocolour images. Patients with diabetes were recruited for UWF imaging. A total of 383 subjects (754 eyes) were enrolled. Nonproliferative DR graded to be moderate or higher on the 5-level International Clinical Diabetic Retinopathy (ICDR) severity scale was considered as grounds for referral. The software automatically detected DR lesions using the previously trained classifiers and classified each image in the test set as referral-warranted or not warranted. Sensitivity, specificity and the area under the receiver operating curve (AUROC) of the algorithm were computed. The automated algorithm achieved a 91.7%/90.3% sensitivity (95% CI 90.1-93.9/80.4-89.4) with a 50.0%/53.6% specificity (95% CI 31.7-72.8/36.5-71.4) for detecting referral-warranted retinopathy at the patient/eye levels, respectively; the AUROC was 0.873/0.851 (95% CI 0.819-0.922/0.804-0.894). Diabetic retinopathy (DR) lesions were detected from Optos pseudocolour UWF images using an automated algorithm. Images were classified as referral-warranted DR with a high degree of sensitivity and moderate specificity. Automated analysis of UWF images could be of value in DR screening programmes and could allow for more complete and accurate disease staging. © 2017 Acta Ophthalmologica Scandinavica Foundation. Published by John Wiley & Sons Ltd.

Twelve automated thresholding methods for segmentation of PET images: a phantom study

International Nuclear Information System (INIS)

Prieto, Elena; Peñuelas, Iván; Martí-Climent, Josep M; Lecumberri, Pablo; Gómez, Marisol; Pagola, Miguel; Bilbao, Izaskun; Ecay, Margarita

2012-01-01

Tumor volume delineation over positron emission tomography (PET) images is of great interest for proper diagnosis and therapy planning. However, standard segmentation techniques (manual or semi-automated) are operator dependent and time consuming while fully automated procedures are cumbersome or require complex mathematical development. The aim of this study was to segment PET images in a fully automated way by implementing a set of 12 automated thresholding algorithms, classical in the fields of optical character recognition, tissue engineering or non-destructive testing images in high-tech structures. Automated thresholding algorithms select a specific threshold for each image without any a priori spatial information of the segmented object or any special calibration of the tomograph, as opposed to usual thresholding methods for PET. Spherical 18 F-filled objects of different volumes were acquired on clinical PET/CT and on a small animal PET scanner, with three different signal-to-background ratios. Images were segmented with 12 automatic thresholding algorithms and results were compared with the standard segmentation reference, a threshold at 42% of the maximum uptake. Ridler and Ramesh thresholding algorithms based on clustering and histogram-shape information, respectively, provided better results that the classical 42%-based threshold (p < 0.05). We have herein demonstrated that fully automated thresholding algorithms can provide better results than classical PET segmentation tools. (paper)

IDAPS (Image Data Automated Processing System) System Description

Science.gov (United States)

1988-06-24

This document describes the physical configuration and components used in the image processing system referred to as IDAPS (Image Data Automated ... Processing System). This system was developed by the Environmental Research Institute of Michigan (ERIM) for Eglin Air Force Base. The system is designed

Quantifying biodiversity using digital cameras and automated image analysis.

Science.gov (United States)

Roadknight, C. M.; Rose, R. J.; Barber, M. L.; Price, M. C.; Marshall, I. W.

2009-04-01

Monitoring the effects on biodiversity of extensive grazing in complex semi-natural habitats is labour intensive. There are also concerns about the standardization of semi-quantitative data collection. We have chosen to focus initially on automating the most time consuming aspect - the image analysis. The advent of cheaper and more sophisticated digital camera technology has lead to a sudden increase in the number of habitat monitoring images and information that is being collected. We report on the use of automated trail cameras (designed for the game hunting market) to continuously capture images of grazer activity in a variety of habitats at Moor House National Nature Reserve, which is situated in the North of England at an average altitude of over 600m. Rainfall is high, and in most areas the soil consists of deep peat (1m to 3m), populated by a mix of heather, mosses and sedges. The cameras have been continuously in operation over a 6 month period, daylight images are in full colour and night images (IR flash) are black and white. We have developed artificial intelligence based methods to assist in the analysis of the large number of images collected, generating alert states for new or unusual image conditions. This paper describes the data collection techniques, outlines the quantitative and qualitative data collected and proposes online and offline systems that can reduce the manpower overheads and increase focus on important subsets in the collected data. By converting digital image data into statistical composite data it can be handled in a similar way to other biodiversity statistics thus improving the scalability of monitoring experiments. Unsupervised feature detection methods and supervised neural methods were tested and offered solutions to simplifying the process. Accurate (85 to 95%) categorization of faunal content can be obtained, requiring human intervention for only those images containing rare animals or unusual (undecidable) conditions, and

Distinction of metaphases in the first cell cycle for automated system in radiation dosimetry

International Nuclear Information System (INIS)

Hayata, I.; Kajima, J.; Okabe, N.

1992-01-01

As part of the biological improvements for developing an automated scoring system of radiation induced chromosome aberrations for radiation dosimetry, we introduce a new method for identifying the metaphases in the first cell cycle. Differing from the conventional method with BrdUrd, it focuses on the difference of chromosome number to be induced by inhibiting the cytokinesis with Cytochalasin B. Majority of the cells with 46 chromosomes were in the first cell cycle, and the ratio of those with 46 chromosomes in the second division was less than one per cent both when Cytochalasin B of 1.5 μg/ml was added to the culture of irradiated lymphocytes and when that of 1.8 μg/ml was added to that of non-irradiated cells for one day, respectively. The ratio of metaphases with over-condensed chromosomes is reduced, the clear-cut image of chromosomes is obtained, culture and staining processes are simpler, and the device of UV irradiation is not necessary. Thus the present Cytochalasin B method offers more qualified input, data based on the numerical difference, than conventional image based recognition, and upgrades the quality of the scoring in the automated analysis system. (Author)

Artificial neural network-aided image analysis system for cell counting.

Science.gov (United States)

Sjöström, P J; Frydel, B R; Wahlberg, L U

1999-05-01

In histological preparations containing debris and synthetic materials, it is difficult to automate cell counting using standard image analysis tools, i.e., systems that rely on boundary contours, histogram thresholding, etc. In an attempt to mimic manual cell recognition, an automated cell counter was constructed using a combination of artificial intelligence and standard image analysis methods. Artificial neural network (ANN) methods were applied on digitized microscopy fields without pre-ANN feature extraction. A three-layer feed-forward network with extensive weight sharing in the first hidden layer was employed and trained on 1,830 examples using the error back-propagation algorithm on a Power Macintosh 7300/180 desktop computer. The optimal number of hidden neurons was determined and the trained system was validated by comparison with blinded human counts. System performance at 50x and lO0x magnification was evaluated. The correlation index at 100x magnification neared person-to-person variability, while 50x magnification was not useful. The system was approximately six times faster than an experienced human. ANN-based automated cell counting in noisy histological preparations is feasible. Consistent histology and computer power are crucial for system performance. The system provides several benefits, such as speed of analysis and consistency, and frees up personnel for other tasks.

HiHiMap: single-cell quantitation of histones and histone posttranslational modifications across the cell cycle by high-throughput imaging.

Science.gov (United States)

Zane, Linda; Chapus, Fleur; Pegoraro, Gianluca; Misteli, Tom

2017-08-15

We describe Hi gh-throughput Hi stone Map ping (HiHiMap), a high-throughput imaging method to measure histones and histone posttranslational modifications (PTMs) in single cells. HiHiMap uses imaging-based quantification of DNA and cyclin A to stage individual cells in the cell cycle to determine the levels of histones or histone PTMs in each stage of the cell cycle. As proof of principle, we apply HiHiMap to measure the level of 21 core histones, histone variants, and PTMs in primary, immortalized, and transformed cells. We identify several histone modifications associated with oncogenic transformation. HiHiMap allows the rapid, high-throughput study of histones and histone PTMs across the cell cycle and the study of subpopulations of cells. © 2017 Zane et al. This article is distributed by The American Society for Cell Biology under license from the author(s). Two months after publication it is available to the public under an Attribution–Noncommercial–Share Alike 3.0 Unported Creative Commons License (http://creativecommons.org/licenses/by-nc-sa/3.0).

Automated image analysis in the study of collagenous colitis

DEFF Research Database (Denmark)

Fiehn, Anne-Marie Kanstrup; Kristensson, Martin; Engel, Ulla

2016-01-01

PURPOSE: The aim of this study was to develop an automated image analysis software to measure the thickness of the subepithelial collagenous band in colon biopsies with collagenous colitis (CC) and incomplete CC (CCi). The software measures the thickness of the collagenous band on microscopic...... slides stained with Van Gieson (VG). PATIENTS AND METHODS: A training set consisting of ten biopsies diagnosed as CC, CCi, and normal colon mucosa was used to develop the automated image analysis (VG app) to match the assessment by a pathologist. The study set consisted of biopsies from 75 patients...

Cell surface expression of single chain antibodies with applications to imaging of gene expression in vivo

International Nuclear Information System (INIS)

Northrop, Jeffrey P.; Bednarski, Mark; Li, King C.; Barbieri, Susan O.; Lu, Amy T.; Nguyen, Dee; Varadarajan, John; Osen, Maureen; Star-Lack, Josh

2003-01-01

Imaging of gene expression in vivo has many potential uses for biomedical research and drug discovery, ranging from the study of gene regulation and cancer to the non-invasive assessment of gene therapies. To streamline the development of imaging marker gene technologies for nuclear medicine, we propose a new approach to the design of reporter/probe pairs wherein the reporter is a cell surface-expressed single chain antibody variable fragment that has been raised against a low molecular weight imaging probe with optimized pharmacokinetic properties. Proof of concept of the approach was achieved using a single chain antibody variable fragment that binds with high affinity to fluorescein and an imaging probe consisting of fluorescein isothiocyanate coupled to the chelator diethylene triamine penta-acetic acid labeled with the gamma-emitter 111 In. We demonstrate specific high-affinity binding of this probe to the cell surface-expressed reporter in vitro and assess the in vivo biodistribution of the probe both in wild-type mice and in mice harboring tumor xenografts expressing the reporter. Specific uptake of the probe by, and in vivo imaging of, tumors expressing the reporter are shown. Since ScFvs with high affinities can be raised to almost any protein or small molecule, the proposed methodology may offer a new flexibility in the design of imaging tracer/reporter pairs wherein both probe pharmacokinetics and binding affinities can be readily optimized. (orig.)

Automation of a single-DNA molecule stretching device

DEFF Research Database (Denmark)

Sørensen, Kristian Tølbøl; Lopacinska, Joanna M.; Tommerup, Niels

2015-01-01

We automate the manipulation of genomic-length DNA in a nanofluidic device based on real-time analysis of fluorescence images. In our protocol, individual molecules are picked from a microchannel and stretched with pN forces using pressure driven flows. The millimeter-long DNA fragments free...

Automated image analysis for quantification of filamentous bacteria

DEFF Research Database (Denmark)

Fredborg, Marlene; Rosenvinge, Flemming Schønning; Spillum, Erik

2015-01-01

in systems relying on colorimetry or turbidometry (such as Vitek-2, Phoenix, MicroScan WalkAway). The objective was to examine an automated image analysis algorithm for quantification of filamentous bacteria using the 3D digital microscopy imaging system, oCelloScope. Results Three E. coli strains displaying...

An algorithm to automate yeast segmentation and tracking.

Directory of Open Access Journals (Sweden)

Andreas Doncic

Full Text Available Our understanding of dynamic cellular processes has been greatly enhanced by rapid advances in quantitative fluorescence microscopy. Imaging single cells has emphasized the prevalence of phenomena that can be difficult to infer from population measurements, such as all-or-none cellular decisions, cell-to-cell variability, and oscillations. Examination of these phenomena requires segmenting and tracking individual cells over long periods of time. However, accurate segmentation and tracking of cells is difficult and is often the rate-limiting step in an experimental pipeline. Here, we present an algorithm that accomplishes fully automated segmentation and tracking of budding yeast cells within growing colonies. The algorithm incorporates prior information of yeast-specific traits, such as immobility and growth rate, to segment an image using a set of threshold values rather than one specific optimized threshold. Results from the entire set of thresholds are then used to perform a robust final segmentation.

Extended -Regular Sequence for Automated Analysis of Microarray Images

Directory of Open Access Journals (Sweden)

Jin Hee-Jeong

2006-01-01

Full Text Available Microarray study enables us to obtain hundreds of thousands of expressions of genes or genotypes at once, and it is an indispensable technology for genome research. The first step is the analysis of scanned microarray images. This is the most important procedure for obtaining biologically reliable data. Currently most microarray image processing systems require burdensome manual block/spot indexing work. Since the amount of experimental data is increasing very quickly, automated microarray image analysis software becomes important. In this paper, we propose two automated methods for analyzing microarray images. First, we propose the extended -regular sequence to index blocks and spots, which enables a novel automatic gridding procedure. Second, we provide a methodology, hierarchical metagrid alignment, to allow reliable and efficient batch processing for a set of microarray images. Experimental results show that the proposed methods are more reliable and convenient than the commercial tools.

Automated planning of breast radiotherapy using cone beam CT imaging

International Nuclear Information System (INIS)

Amit, Guy; Purdie, Thomas G.

2015-01-01

Purpose: Develop and clinically validate a methodology for using cone beam computed tomography (CBCT) imaging in an automated treatment planning framework for breast IMRT. Methods: A technique for intensity correction of CBCT images was developed and evaluated. The technique is based on histogram matching of CBCT image sets, using information from “similar” planning CT image sets from a database of paired CBCT and CT image sets (n = 38). Automated treatment plans were generated for a testing subset (n = 15) on the planning CT and the corrected CBCT. The plans generated on the corrected CBCT were compared to the CT-based plans in terms of beam parameters, dosimetric indices, and dose distributions. Results: The corrected CBCT images showed considerable similarity to their corresponding planning CTs (average mutual information 1.0±0.1, average sum of absolute differences 185 ± 38). The automated CBCT-based plans were clinically acceptable, as well as equivalent to the CT-based plans with average gantry angle difference of 0.99°±1.1°, target volume overlap index (Dice) of 0.89±0.04 although with slightly higher maximum target doses (4482±90 vs 4560±84, P < 0.05). Gamma index analysis (3%, 3 mm) showed that the CBCT-based plans had the same dose distribution as plans calculated with the same beams on the registered planning CTs (average gamma index 0.12±0.04, gamma <1 in 99.4%±0.3%). Conclusions: The proposed method demonstrates the potential for a clinically feasible and efficient online adaptive breast IMRT planning method based on CBCT imaging, integrating automation

A robust computational solution for automated quantification of a specific binding ratio based on [123I]FP-CIT SPECT images

International Nuclear Information System (INIS)

Oliveira, F. P. M.; Tavares, J. M. R. S.; Borges, Faria D.; Campos, Costa D.

2014-01-01

The purpose of the current paper is to present a computational solution to accurately quantify a specific to a non-specific uptake ratio in [ 123 I]fP-CIT single photon emission computed tomography (SPECT) images and simultaneously measure the spatial dimensions of the basal ganglia, also known as basal nuclei. A statistical analysis based on a reference dataset selected by the user is also automatically performed. The quantification of the specific to non-specific uptake ratio here is based on regions of interest defined after the registration of the image under study with a template image. The computational solution was tested on a dataset of 38 [ 123 I]FP-CIT SPECT images: 28 images were from patients with Parkinson’s disease and the remainder from normal patients, and the results of the automated quantification were compared to the ones obtained by three well-known semi-automated quantification methods. The results revealed a high correlation coefficient between the developed automated method and the three semi-automated methods used for comparison (r ≥0.975). The solution also showed good robustness against different positions of the patient, as an almost perfect agreement between the specific to non-specific uptake ratio was found (ICC=1.000). The mean processing time was around 6 seconds per study using a common notebook PC. The solution developed can be useful for clinicians to evaluate [ 123 I]FP-CIT SPECT images due to its accuracy, robustness and speed. Also, the comparison between case studies and the follow-up of patients can be done more accurately and proficiently since the intra- and inter-observer variability of the semi-automated calculation does not exist in automated solutions. The dimensions of the basal ganglia and their automatic comparison with the values of the population selected as reference are also important for professionals in this area.

Automated Orthorectification of VHR Satellite Images by SIFT-Based RPC Refinement

Directory of Open Access Journals (Sweden)

Hakan Kartal

2018-06-01

Full Text Available Raw remotely sensed images contain geometric distortions and cannot be used directly for map-based applications, accurate locational information extraction or geospatial data integration. A geometric correction process must be conducted to minimize the errors related to distortions and achieve the desired location accuracy before further analysis. A considerable number of images might be needed when working over large areas or in temporal domains in which manual geometric correction requires more labor and time. To overcome these problems, new algorithms have been developed to make the geometric correction process autonomous. The Scale Invariant Feature Transform (SIFT algorithm is an image matching algorithm used in remote sensing applications that has received attention in recent years. In this study, the effects of the incidence angle, surface topography and land cover (LC characteristics on SIFT-based automated orthorectification were investigated at three different study sites with different topographic conditions and LC characteristics using Pleiades very high resolution (VHR images acquired at different incidence angles. The results showed that the location accuracy of the orthorectified images increased with lower incidence angle images. More importantly, the topographic characteristics had no observable impacts on the location accuracy of SIFT-based automated orthorectification, and the results showed that Ground Control Points (GCPs are mainly concentrated in the “Forest” and “Semi Natural Area” LC classes. A multi-thread code was designed to reduce the automated processing time, and the results showed that the process performed 7 to 16 times faster using an automated approach. Analyses performed on various spectral modes of multispectral data showed that the arithmetic data derived from pan-sharpened multispectral images can be used in automated SIFT-based RPC orthorectification.

Application of a non-hazardous vital dye for cell counting with automated cell counters.

Science.gov (United States)

Kim, Soo In; Kim, Hyun Jeong; Lee, Ho-Jae; Lee, Kiwon; Hong, Dongpyo; Lim, Hyunchang; Cho, Keunchang; Jung, Neoncheol; Yi, Yong Weon

2016-01-01

Recent advances in automated cell counters enable us to count cells more easily with consistency. However, the wide use of the traditional vital dye trypan blue (TB) raises environmental and health concerns due to its potential teratogenic effects. To avoid this chemical hazard, it is of importance to introduce an alternative non-hazardous vital dye that is compatible with automated cell counters. Erythrosin B (EB) is a vital dye that is impermeable to biological membranes and is used as a food additive. Similarly to TB, EB stains only nonviable cells with disintegrated membranes. However, EB is less popular than TB and is seldom used with automated cell counters. We found that cell counting accuracy with EB was comparable to that with TB. EB was found to be an effective dye for accurate counting of cells with different viabilities across three different automated cell counters. In contrast to TB, EB was less toxic to cultured HL-60 cells during the cell counting process. These results indicate that replacing TB with EB for use with automated cell counters will significantly reduce the hazardous risk while producing comparable results. Copyright © 2015 Logos Biosystems, Inc. Published by Elsevier Inc. All rights reserved.

An engineered approach to stem cell culture: automating the decision process for real-time adaptive subculture of stem cells.

Directory of Open Access Journals (Sweden)

Dai Fei Elmer Ker

Full Text Available Current cell culture practices are dependent upon human operators and remain laborious and highly subjective, resulting in large variations and inconsistent outcomes, especially when using visual assessments of cell confluency to determine the appropriate time to subculture cells. Although efforts to automate cell culture with robotic systems are underway, the majority of such systems still require human intervention to determine when to subculture. Thus, it is necessary to accurately and objectively determine the appropriate time for cell passaging. Optimal stem cell culturing that maintains cell pluripotency while maximizing cell yields will be especially important for efficient, cost-effective stem cell-based therapies. Toward this goal we developed a real-time computer vision-based system that monitors the degree of cell confluency with a precision of 0.791±0.031 and recall of 0.559±0.043. The system consists of an automated phase-contrast time-lapse microscope and a server. Multiple dishes are sequentially imaged and the data is uploaded to the server that performs computer vision processing, predicts when cells will exceed a pre-defined threshold for optimal cell confluency, and provides a Web-based interface for remote cell culture monitoring. Human operators are also notified via text messaging and e-mail 4 hours prior to reaching this threshold and immediately upon reaching this threshold. This system was successfully used to direct the expansion of a paradigm stem cell population, C2C12 cells. Computer-directed and human-directed control subcultures required 3 serial cultures to achieve the theoretical target cell yield of 50 million C2C12 cells and showed no difference for myogenic and osteogenic differentiation. This automated vision-based system has potential as a tool toward adaptive real-time control of subculturing, cell culture optimization and quality assurance/quality control, and it could be integrated with current and

Automated detection of a prostate Ni-Ti stent in electronic portal images.

Science.gov (United States)

Carl, Jesper; Nielsen, Henning; Nielsen, Jane; Lund, Bente; Larsen, Erik Hoejkjaer

2006-12-01

Planning target volumes (PTV) in fractionated radiotherapy still have to be outlined with wide margins to the clinical target volume due to uncertainties arising from daily shift of the prostate position. A recently proposed new method of visualization of the prostate is based on insertion of a thermo-expandable Ni-Ti stent. The current study proposes a new detection algorithm for automated detection of the Ni-Ti stent in electronic portal images. The algorithm is based on the Ni-Ti stent having a cylindrical shape with a fixed diameter, which was used as the basis for an automated detection algorithm. The automated method uses enhancement of lines combined with a grayscale morphology operation that looks for enhanced pixels separated with a distance similar to the diameter of the stent. The images in this study are all from prostate cancer patients treated with radiotherapy in a previous study. Images of a stent inserted in a humanoid phantom demonstrated a localization accuracy of 0.4-0.7 mm which equals the pixel size in the image. The automated detection of the stent was compared to manual detection in 71 pairs of orthogonal images taken in nine patients. The algorithm was successful in 67 of 71 pairs of images. The method is fast, has a high success rate, good accuracy, and has a potential for unsupervised localization of the prostate before radiotherapy, which would enable automated repositioning before treatment and allow for the use of very tight PTV margins.

Automated detection of a prostate Ni-Ti stent in electronic portal images

International Nuclear Information System (INIS)

Carl, Jesper; Nielsen, Henning; Nielsen, Jane; Lund, Bente; Larsen, Erik Hoejkjaer

2006-01-01

Planning target volumes (PTV) in fractionated radiotherapy still have to be outlined with wide margins to the clinical target volume due to uncertainties arising from daily shift of the prostate position. A recently proposed new method of visualization of the prostate is based on insertion of a thermo-expandable Ni-Ti stent. The current study proposes a new detection algorithm for automated detection of the Ni-Ti stent in electronic portal images. The algorithm is based on the Ni-Ti stent having a cylindrical shape with a fixed diameter, which was used as the basis for an automated detection algorithm. The automated method uses enhancement of lines combined with a grayscale morphology operation that looks for enhanced pixels separated with a distance similar to the diameter of the stent. The images in this study are all from prostate cancer patients treated with radiotherapy in a previous study. Images of a stent inserted in a humanoid phantom demonstrated a localization accuracy of 0.4-0.7 mm which equals the pixel size in the image. The automated detection of the stent was compared to manual detection in 71 pairs of orthogonal images taken in nine patients. The algorithm was successful in 67 of 71 pairs of images. The method is fast, has a high success rate, good accuracy, and has a potential for unsupervised localization of the prostate before radiotherapy, which would enable automated repositioning before treatment and allow for the use of very tight PTV margins

Small Imaging Depth LIDAR and DCNN-Based Localization for Automated Guided Vehicle.

Science.gov (United States)

Ito, Seigo; Hiratsuka, Shigeyoshi; Ohta, Mitsuhiko; Matsubara, Hiroyuki; Ogawa, Masaru

2018-01-10

We present our third prototype sensor and a localization method for Automated Guided Vehicles (AGVs), for which small imaging LIght Detection and Ranging (LIDAR) and fusion-based localization are fundamentally important. Our small imaging LIDAR, named the Single-Photon Avalanche Diode (SPAD) LIDAR, uses a time-of-flight method and SPAD arrays. A SPAD is a highly sensitive photodetector capable of detecting at the single-photon level, and the SPAD LIDAR has two SPAD arrays on the same chip for detection of laser light and environmental light. Therefore, the SPAD LIDAR simultaneously outputs range image data and monocular image data with the same coordinate system and does not require external calibration among outputs. As AGVs travel both indoors and outdoors with vibration, this calibration-less structure is particularly useful for AGV applications. We also introduce a fusion-based localization method, named SPAD DCNN, which uses the SPAD LIDAR and employs a Deep Convolutional Neural Network (DCNN). SPAD DCNN can fuse the outputs of the SPAD LIDAR: range image data, monocular image data and peak intensity image data. The SPAD DCNN has two outputs: the regression result of the position of the SPAD LIDAR and the classification result of the existence of a target to be approached. Our third prototype sensor and the localization method are evaluated in an indoor environment by assuming various AGV trajectories. The results show that the sensor and localization method improve the localization accuracy.

A simple viability analysis for unicellular cyanobacteria using a new autofluorescence assay, automated microscopy, and ImageJ

Directory of Open Access Journals (Sweden)

Schulze Katja

2011-11-01

Full Text Available Abstract Background Currently established methods to identify viable and non-viable cells of cyanobacteria are either time-consuming (eg. plating or preparation-intensive (eg. fluorescent staining. In this paper we present a new and fast viability assay for unicellular cyanobacteria, which uses red chlorophyll fluorescence and an unspecific green autofluorescence for the differentiation of viable and non-viable cells without the need of sample preparation. Results The viability assay for unicellular cyanobacteria using red and green autofluorescence was established and validated for the model organism Synechocystis sp. PCC 6803. Both autofluorescence signals could be observed simultaneously allowing a direct classification of viable and non-viable cells. The results were confirmed by plating/colony count, absorption spectra and chlorophyll measurements. The use of an automated fluorescence microscope and a novel ImageJ based image analysis plugin allow a semi-automated analysis. Conclusions The new method simplifies the process of viability analysis and allows a quick and accurate analysis. Furthermore results indicate that a combination of the new assay with absorption spectra or chlorophyll concentration measurements allows the estimation of the vitality of cells.

Bioprocessing automation in cell therapy manufacturing: Outcomes of special interest group automation workshop.

Science.gov (United States)

Ball, Oliver; Robinson, Sarah; Bure, Kim; Brindley, David A; Mccall, David

2018-04-01

Phacilitate held a Special Interest Group workshop event in Edinburgh, UK, in May 2017. The event brought together leading stakeholders in the cell therapy bioprocessing field to identify present and future challenges and propose potential solutions to automation in cell therapy bioprocessing. Here, we review and summarize discussions from the event. Deep biological understanding of a product, its mechanism of action and indication pathogenesis underpin many factors relating to bioprocessing and automation. To fully exploit the opportunities of bioprocess automation, therapeutics developers must closely consider whether an automation strategy is applicable, how to design an 'automatable' bioprocess and how to implement process modifications with minimal disruption. Major decisions around bioprocess automation strategy should involve all relevant stakeholders; communication between technical and business strategy decision-makers is of particular importance. Developers should leverage automation to implement in-process testing, in turn applicable to process optimization, quality assurance (QA)/ quality control (QC), batch failure control, adaptive manufacturing and regulatory demands, but a lack of precedent and technical opportunities can complicate such efforts. Sparse standardization across product characterization, hardware components and software platforms is perceived to complicate efforts to implement automation. The use of advanced algorithmic approaches such as machine learning may have application to bioprocess and supply chain optimization. Automation can substantially de-risk the wider supply chain, including tracking and traceability, cryopreservation and thawing and logistics. The regulatory implications of automation are currently unclear because few hardware options exist and novel solutions require case-by-case validation, but automation can present attractive regulatory incentives. Copyright © 2018 International Society for Cellular Therapy

3-D image pre-processing algorithms for improved automated tracing of neuronal arbors.

Science.gov (United States)

Narayanaswamy, Arunachalam; Wang, Yu; Roysam, Badrinath

2011-09-01

The accuracy and reliability of automated neurite tracing systems is ultimately limited by image quality as reflected in the signal-to-noise ratio, contrast, and image variability. This paper describes a novel combination of image processing methods that operate on images of neurites captured by confocal and widefield microscopy, and produce synthetic images that are better suited to automated tracing. The algorithms are based on the curvelet transform (for denoising curvilinear structures and local orientation estimation), perceptual grouping by scalar voting (for elimination of non-tubular structures and improvement of neurite continuity while preserving branch points), adaptive focus detection, and depth estimation (for handling widefield images without deconvolution). The proposed methods are fast, and capable of handling large images. Their ability to handle images of unlimited size derives from automated tiling of large images along the lateral dimension, and processing of 3-D images one optical slice at a time. Their speed derives in part from the fact that the core computations are formulated in terms of the Fast Fourier Transform (FFT), and in part from parallel computation on multi-core computers. The methods are simple to apply to new images since they require very few adjustable parameters, all of which are intuitive. Examples of pre-processing DIADEM Challenge images are used to illustrate improved automated tracing resulting from our pre-processing methods.

21 CFR 864.5220 - Automated differential cell counter.

Science.gov (United States)

2010-04-01

... 21 Food and Drugs 8 2010-04-01 2010-04-01 false Automated differential cell counter. 864.5220 Section 864.5220 Food and Drugs FOOD AND DRUG ADMINISTRATION, DEPARTMENT OF HEALTH AND HUMAN SERVICES (CONTINUED) MEDICAL DEVICES HEMATOLOGY AND PATHOLOGY DEVICES Automated and Semi-Automated Hematology Devices...

21 CFR 864.5260 - Automated cell-locating device.

Science.gov (United States)

2010-04-01

... 21 Food and Drugs 8 2010-04-01 2010-04-01 false Automated cell-locating device. 864.5260 Section 864.5260 Food and Drugs FOOD AND DRUG ADMINISTRATION, DEPARTMENT OF HEALTH AND HUMAN SERVICES (CONTINUED) MEDICAL DEVICES HEMATOLOGY AND PATHOLOGY DEVICES Automated and Semi-Automated Hematology Devices...

Oxygen-controlled automated neural differentiation of mouse embryonic stem cells.

Science.gov (United States)

Mondragon-Teran, Paul; Tostoes, Rui; Mason, Chris; Lye, Gary J; Veraitch, Farlan S

2013-03-01

Automation and oxygen tension control are two tools that provide significant improvements to the reproducibility and efficiency of stem cell production processes. the aim of this study was to establish a novel automation platform capable of controlling oxygen tension during both the cell-culture and liquid-handling steps of neural differentiation processes. We built a bespoke automation platform, which enclosed a liquid-handling platform in a sterile, oxygen-controlled environment. An airtight connection was used to transfer cell culture plates to and from an automated oxygen-controlled incubator. Our results demonstrate that our system yielded comparable cell numbers, viabilities, metabolism profiles and differentiation efficiencies when compared with traditional manual processes. Interestingly, eliminating exposure to ambient conditions during the liquid-handling stage resulted in significant improvements in the yield of MAP2-positive neural cells, indicating that this level of control can improve differentiation processes. This article describes, for the first time, an automation platform capable of maintaining oxygen tension control during both the cell-culture and liquid-handling stages of a 2D embryonic stem cell differentiation process.

Automated Cell-Cutting for Cell Cloning

Science.gov (United States)

Ichikawa, Akihiko; Tanikawa, Tamio; Matsukawa, Kazutsugu; Takahashi, Seiya; Ohba, Kohtaro

We develop an automated cell-cutting technique for cell cloning. Animal cells softened by the cytochalasin treatment are injected into a microfluidic chip. The microfluidic chip contains two orthogonal channels: one microchannel is wide, used to transport cells, and generates the cutting flow; the other is thin and used for aspiration, fixing, and stretching of the cell. The injected cell is aspirated and stretched in the thin microchannel. Simultaneously, the volumes of the cell before and after aspiration are calculated; the volumes are used to calculate the fluid flow required to aspirate half the volume of the cell into the thin microchannel. Finally, we apply a high-speed flow in the orthogonal microchannel to bisect the cell. This paper reports the cutting process, the cutting system, and the results of the experiment.

Optical Inspection In Hostile Industrial Environments: Single-Sensor VS. Imaging Methods

Science.gov (United States)

Cielo, P.; Dufour, M.; Sokalski, A.

1988-11-01

On-line and unsupervised industrial inspection for quality control and process monitoring is increasingly required in the modern automated factory. Optical techniques are particularly well suited to industrial inspection in hostile environments because of their noncontact nature, fast response time and imaging capabilities. Optical sensors can be used for remote inspection of high temperature products or otherwise inaccessible parts, provided they are in a line-of-sight relation with the sensor. Moreover, optical sensors are much easier to adapt to a variety of part shapes, position or orientation and conveyor speeds as compared to contact-based sensors. This is an important requirement in a flexible automation environment. A number of choices are possible in the design of optical inspection systems. General-purpose two-dimensional (2-D) or three-dimensional (3-D) imaging techniques have advanced very rapidly in the last years thanks to a substantial research effort as well as to the availability of increasingly powerful and affordable hardware and software. Imaging can be realized using 2-D arrays or simpler one-dimensional (1-D) line-array detectors. Alternatively, dedicated single-spot sensors require a smaller amount of data processing and often lead to robust sensors which are particularly appropriate to on-line operation in hostile industrial environments. Many specialists now feel that dedicated sensors or clusters of sensors are often more effective for specific industrial automation and control tasks, at least in the short run. This paper will discuss optomechanical and electro-optical choices with reference to the design of a number of on-line inspection sensors which have been recently developed at our institute. Case studies will include real-time surface roughness evaluation on polymer cables extruded at high speed, surface characterization of hot-rolled or galvanized-steel sheets, temperature evaluation and pinhole detection in aluminum foil, multi

Microchip screening platform for single cell assessment of NK cell cytotoxicity

Directory of Open Access Journals (Sweden)

Karolin eGuldevall

2016-04-01

Full Text Available Here we report a screening platform for assessment of the cytotoxic potential of individual natural killer (NK cells within larger populations. Human primary NK cells were distributed across a silicon-glass microchip containing 32 400 individual microwells loaded with target cells. Through fluorescence screening and automated image analysis the numbers of NK and live or dead target cells in each well could be assessed at different time points after initial mixing. Cytotoxicity was also studied by time-lapse live-cell imaging in microwells quantifying the killing potential of individual NK cells. Although most resting NK cells (≈75% were non-cytotoxic against the leukemia cell line K562, some NK cells were able to kill several (≥3 target cells within the 12 hours long experiment. In addition, the screening approach was adapted to increase the chance to find and evaluate serial killing NK cells. Even if the cytotoxic potential varied between donors it was evident that a small fraction of highly cytotoxic NK cells were responsible for a substantial portion of the killing. We demonstrate multiple assays where our platform can be used to enumerate and characterize cytotoxic cells, such as NK or T cells. This approach could find use in clinical applications, e.g. in the selection of donors for stem cell transplantation or generation of highly specific and cytotoxic cells for adoptive immunotherapy.

Microchip Screening Platform for Single Cell Assessment of NK Cell Cytotoxicity

Science.gov (United States)

Guldevall, Karolin; Brandt, Ludwig; Forslund, Elin; Olofsson, Karl; Frisk, Thomas W.; Olofsson, Per E.; Gustafsson, Karin; Manneberg, Otto; Vanherberghen, Bruno; Brismar, Hjalmar; Kärre, Klas; Uhlin, Michael; Önfelt, Björn

2016-01-01

Here, we report a screening platform for assessment of the cytotoxic potential of individual natural killer (NK) cells within larger populations. Human primary NK cells were distributed across a silicon–glass microchip containing 32,400 individual microwells loaded with target cells. Through fluorescence screening and automated image analysis, the numbers of NK and live or dead target cells in each well could be assessed at different time points after initial mixing. Cytotoxicity was also studied by time-lapse live-cell imaging in microwells quantifying the killing potential of individual NK cells. Although most resting NK cells (≈75%) were non-cytotoxic against the leukemia cell line K562, some NK cells were able to kill several (≥3) target cells within the 12-h long experiment. In addition, the screening approach was adapted to increase the chance to find and evaluate serial killing NK cells. Even if the cytotoxic potential varied between donors, it was evident that a small fraction of highly cytotoxic NK cells were responsible for a substantial portion of the killing. We demonstrate multiple assays where our platform can be used to enumerate and characterize cytotoxic cells, such as NK or T cells. This approach could find use in clinical applications, e.g., in the selection of donors for stem cell transplantation or generation of highly specific and cytotoxic cells for adoptive immunotherapy. PMID:27092139

Automated processing for proton spectroscopic imaging using water reference deconvolution.

Science.gov (United States)

Maudsley, A A; Wu, Z; Meyerhoff, D J; Weiner, M W

1994-06-01

Automated formation of MR spectroscopic images (MRSI) is necessary before routine application of these methods is possible for in vivo studies; however, this task is complicated by the presence of spatially dependent instrumental distortions and the complex nature of the MR spectrum. A data processing method is presented for completely automated formation of in vivo proton spectroscopic images, and applied for analysis of human brain metabolites. This procedure uses the water reference deconvolution method (G. A. Morris, J. Magn. Reson. 80, 547(1988)) to correct for line shape distortions caused by instrumental and sample characteristics, followed by parametric spectral analysis. Results for automated image formation were found to compare favorably with operator dependent spectral integration methods. While the water reference deconvolution processing was found to provide good correction of spatially dependent resonance frequency shifts, it was found to be susceptible to errors for correction of line shape distortions. These occur due to differences between the water reference and the metabolite distributions.

Routinely automated production of 3'-deoxy-3'-[18F] fluorothymidine as a specific molecular imaging probe of tumor cell proliferation

International Nuclear Information System (INIS)

Wang Mingwei; Zhang Yingjian; Zhang Yongping

2011-01-01

This work was aimed at developing a routine for automated production of 3'-deoxy-3'-[ 18 F]fluorothymidine ( 18 F-FLT), a specific molecular imaging probe of tumor cell proliferation, using one-pot two-step strategy and an upgraded Explora GN module integrated with a semi-preparative HPLC system. Firstly, the nucleophilic [ 18 F] radiofluorination of precursor BDNT with activated 18 F ion was carried out at 120 degree C for 5 min to yield the labeled intermediate 18 F-BDFT. Secondly, the acidic hydrolysis of 18 F-BDFT was run at 110 degree C for 5 min to produce 18 F-FLT after addition of HCl, and 18 F-FLT was purified by HPLC. This automated production of 18 F-FLT is of fast, reliable and multi-run features, being completed within 65 min with radiochemical yield of 15%-25% (without decay correction). The quality control of 18 F-FLT was identical with the radiopharmaceutical requirements, especiallly the radiochemical purity of greater than 99% and high chemical purity and specific activity own to HPLC purification. (authors)

Automated daily quality control analysis for mammography in a multi-unit imaging center.

Science.gov (United States)

Sundell, Veli-Matti; Mäkelä, Teemu; Meaney, Alexander; Kaasalainen, Touko; Savolainen, Sauli

2018-01-01

Background The high requirements for mammography image quality necessitate a systematic quality assurance process. Digital imaging allows automation of the image quality analysis, which can potentially improve repeatability and objectivity compared to a visual evaluation made by the users. Purpose To develop an automatic image quality analysis software for daily mammography quality control in a multi-unit imaging center. Material and Methods An automated image quality analysis software using the discrete wavelet transform and multiresolution analysis was developed for the American College of Radiology accreditation phantom. The software was validated by analyzing 60 randomly selected phantom images from six mammography systems and 20 phantom images with different dose levels from one mammography system. The results were compared to a visual analysis made by four reviewers. Additionally, long-term image quality trends of a full-field digital mammography system and a computed radiography mammography system were investigated. Results The automated software produced feature detection levels comparable to visual analysis. The agreement was good in the case of fibers, while the software detected somewhat more microcalcifications and characteristic masses. Long-term follow-up via a quality assurance web portal demonstrated the feasibility of using the software for monitoring the performance of mammography systems in a multi-unit imaging center. Conclusion Automated image quality analysis enables monitoring the performance of digital mammography systems in an efficient, centralized manner.

Testing for nonrandom shape similarity between sister cells using automated shape comparison

Science.gov (United States)

Guo, Monica; Marshall, Wallace F.

2009-02-01

Several reports in the biological literature have indicated that when a living cell divides, the two daughter cells have a tendency to be mirror images of each other in terms of their overall cell shape. This phenomenon would be consistent with inheritance of spatial organization from mother cell to daughters. However the published data rely on a small number of examples that were visually chosen, raising potential concerns about inadvertent selection bias. We propose to revisit this issue using automated quantitative shape comparison methods which would have no contribution from the observer and which would allow statistical testing of similarity in large numbers of cells. In this report we describe a first order approach to the problem using rigid curve matching. Using test images, we compare a pointwise correspondence based distance metric with a chamfer matching strategy and find that the latter provides better correspondence and smaller distances between aligned curves, especially when we allow nonrigid deformation of the outlines in addition to rotation.

Automated identification of animal species in camera trap images

NARCIS (Netherlands)

Yu, X.; Wang, J.; Kays, R.; Jansen, P.A.; Wang, T.; Huang, T.

2013-01-01

Image sensors are increasingly being used in biodiversity monitoring, with each study generating many thousands or millions of pictures. Efficiently identifying the species captured by each image is a critical challenge for the advancement of this field. Here, we present an automated species

21 CFR 864.9285 - Automated cell-washing centrifuge for immuno-hematology.

Science.gov (United States)

2010-04-01

... 21 Food and Drugs 8 2010-04-01 2010-04-01 false Automated cell-washing centrifuge for immuno... Establishments That Manufacture Blood and Blood Products § 864.9285 Automated cell-washing centrifuge for immuno-hematology. (a) Identification. An automated cell-washing centrifuge for immuno-hematology is a device used...

Evaluation of automated image analysis software for the detection of diabetic retinopathy to reduce the ophthalmologists' workload.

Science.gov (United States)

Soto-Pedre, Enrique; Navea, Amparo; Millan, Saray; Hernaez-Ortega, Maria C; Morales, Jesús; Desco, Maria C; Pérez, Pablo

2015-02-01

To assess the safety and workload reduction of an automated 'disease/no disease' grading system for diabetic retinopathy (DR) within a systematic screening programme. Single 45° macular field image per eye was obtained from consecutive patients attending a regional primary care based DR screening programme in Valencia (Spain). The sensitivity and specificity of automated system operating as 'one or more than one microaneurysm detection for disease presence' grader were determined relative to a manual grading as gold standard. Data on age, gender and diabetes mellitus were also recorded. A total of 5278 patients with diabetes were screened. The median age and duration of diabetes was 69 years and 6.9 years, respectively. Estimated prevalence of DR was 15.6%. The software classified 43.9% of the patients as having no DR and 26.1% as having ungradable images. Detection of DR was achieved with 94.5% sensitivity (95% CI 92.6- 96.5) and 68.8% specificity (95%CI 67.2-70.4). The overall accuracy of the automated system was 72.5% (95%CI 71.1-73.9). The present retinal image processing algorithm that can act as prefilter to flag out images with pathological lesions can be implemented in practice. Our results suggest that it could be considered when implementing DR screening programmes. © 2014 Acta Ophthalmologica Scandinavica Foundation. Published by John Wiley & Sons Ltd.

CEST ANALYSIS: AUTOMATED CHANGE DETECTION FROM VERY-HIGH-RESOLUTION REMOTE SENSING IMAGES

Directory of Open Access Journals (Sweden)

M. Ehlers

2012-08-01

Full Text Available A fast detection, visualization and assessment of change in areas of crisis or catastrophes are important requirements for coordination and planning of help. Through the availability of new satellites and/or airborne sensors with very high spatial resolutions (e.g., WorldView, GeoEye new remote sensing data are available for a better detection, delineation and visualization of change. For automated change detection, a large number of algorithms has been proposed and developed. From previous studies, however, it is evident that to-date no single algorithm has the potential for being a reliable change detector for all possible scenarios. This paper introduces the Combined Edge Segment Texture (CEST analysis, a decision-tree based cooperative suite of algorithms for automated change detection that is especially designed for the generation of new satellites with very high spatial resolution. The method incorporates frequency based filtering, texture analysis, and image segmentation techniques. For the frequency analysis, different band pass filters can be applied to identify the relevant frequency information for change detection. After transforming the multitemporal images via a fast Fourier transform (FFT and applying the most suitable band pass filter, different methods are available to extract changed structures: differencing and correlation in the frequency domain and correlation and edge detection in the spatial domain. Best results are obtained using edge extraction. For the texture analysis, different 'Haralick' parameters can be calculated (e.g., energy, correlation, contrast, inverse distance moment with 'energy' so far providing the most accurate results. These algorithms are combined with a prior segmentation of the image data as well as with morphological operations for a final binary change result. A rule-based combination (CEST of the change algorithms is applied to calculate the probability of change for a particular location. CEST

In situ probing of cholesterol in astrocytes at the single-cell level using laser desorption ionization mass spectrometric imaging with colloidal silver.

Science.gov (United States)

Perdian, D C; Cha, Sangwon; Oh, Jisun; Sakaguchi, Donald S; Yeung, Edward S; Lee, Young Jin

2010-04-30

Mass spectrometric imaging has been utilized to localize individual astrocytes and to obtain cholesterol populations at the single-cell level in laser desorption ionization (LDI) with colloidal silver. The silver ion adduct of membrane-bound cholesterol was monitored to detect individual cells. Good correlation between mass spectrometric and optical images at different cell densities indicates the ability to perform single-cell studies of cholesterol abundance. The feasibility of quantification is confirmed by the agreement between the LDI-MS ion signals and the results from a traditional enzymatic fluorometric assay. We propose that this approach could be an effective tool to study chemical populations at the cellular level. Published in 2010 by John Wiley & Sons, Ltd.

Microbial Cell Imaging

Energy Technology Data Exchange (ETDEWEB)

Doktycz, Mitchel John [ORNL; Sullivan, Claretta [Eastern Virginia Medical School; Mortensen, Ninell P [ORNL; Allison, David P [ORNL

2011-01-01

Atomic force microscopy (AFM) is finding increasing application in a variety of fields including microbiology. Until the emergence of AFM, techniques for ivnestigating processes in single microbes were limited. From a biologist's perspective, the fact that AFM can be used to generate high-resolution images in buffers or media is its most appealing feature as live-cell imaging can be pursued. Imaging living cells by AFM allows dynamic biological events to be studied, at the nanoscale, in real time. Few areas of biological research have as much to gain as microbiology from the application of AFM. Whereas the scale of microbes places them near the limit of resolution for light microscopy. AFM is well suited for the study of structures on the order of a micron or less. Although electron microscopy techniques have been the standard for high-resolution imaging of microbes, AFM is quickly gaining favor for several reasons. First, fixatives that impair biological activity are not required. Second, AFM is capable of detecting forces in the pN range, and precise control of the force applied to the cantilever can be maintained. This combination facilitates the evaluation of physical characteristics of microbes. Third, rather than yielding the composite, statistical average of cell populations, as is the case with many biochemical assays, the behavior of single cells can be monitored. Despite the potential of AFM in microbiology, there are several limitations that must be considered. For example, the time required to record an image allows for the study of gross events such as cell division or membrane degradation from an antibiotic but precludes the evaluation of biological reactions and events that happen in just fractions of a second. Additionally, the AFM is a topographical tool and is restricted to imaging surfaces. Therefore, it cannot be used to look inside cells as with opticla and transmission electron microscopes. other practical considerations are the

An Automated Self-Learning Quantification System to Identify Visible Areas in Capsule Endoscopy Images.

Science.gov (United States)

Hashimoto, Shinichi; Ogihara, Hiroyuki; Suenaga, Masato; Fujita, Yusuke; Terai, Shuji; Hamamoto, Yoshihiko; Sakaida, Isao

2017-08-01

Visibility in capsule endoscopic images is presently evaluated through intermittent analysis of frames selected by a physician. It is thus subjective and not quantitative. A method to automatically quantify the visibility on capsule endoscopic images has not been reported. Generally, when designing automated image recognition programs, physicians must provide a training image; this process is called supervised learning. We aimed to develop a novel automated self-learning quantification system to identify visible areas on capsule endoscopic images. The technique was developed using 200 capsule endoscopic images retrospectively selected from each of three patients. The rate of detection of visible areas on capsule endoscopic images between a supervised learning program, using training images labeled by a physician, and our novel automated self-learning program, using unlabeled training images without intervention by a physician, was compared. The rate of detection of visible areas was equivalent for the supervised learning program and for our automatic self-learning program. The visible areas automatically identified by self-learning program correlated to the areas identified by an experienced physician. We developed a novel self-learning automated program to identify visible areas in capsule endoscopic images.

CMEIAS color segmentation: an improved computing technology to process color images for quantitative microbial ecology studies at single-cell resolution.

Science.gov (United States)

Gross, Colin A; Reddy, Chandan K; Dazzo, Frank B

2010-02-01

Quantitative microscopy and digital image analysis are underutilized in microbial ecology largely because of the laborious task to segment foreground object pixels from background, especially in complex color micrographs of environmental samples. In this paper, we describe an improved computing technology developed to alleviate this limitation. The system's uniqueness is its ability to edit digital images accurately when presented with the difficult yet commonplace challenge of removing background pixels whose three-dimensional color space overlaps the range that defines foreground objects. Image segmentation is accomplished by utilizing algorithms that address color and spatial relationships of user-selected foreground object pixels. Performance of the color segmentation algorithm evaluated on 26 complex micrographs at single pixel resolution had an overall pixel classification accuracy of 99+%. Several applications illustrate how this improved computing technology can successfully resolve numerous challenges of complex color segmentation in order to produce images from which quantitative information can be accurately extracted, thereby gain new perspectives on the in situ ecology of microorganisms. Examples include improvements in the quantitative analysis of (1) microbial abundance and phylotype diversity of single cells classified by their discriminating color within heterogeneous communities, (2) cell viability, (3) spatial relationships and intensity of bacterial gene expression involved in cellular communication between individual cells within rhizoplane biofilms, and (4) biofilm ecophysiology based on ribotype-differentiated radioactive substrate utilization. The stand-alone executable file plus user manual and tutorial images for this color segmentation computing application are freely available at http://cme.msu.edu/cmeias/ . This improved computing technology opens new opportunities of imaging applications where discriminating colors really matter most

A Highly Specific Gold Nanoprobe for Live-Cell Single-Molecule Imaging

Science.gov (United States)

Leduc, Cécile; Si, Satyabrata; Gautier, Jérémie; Soto-Ribeiro, Martinho; Wehrle-Haller, Bernhard; Gautreau, Alexis; Giannone, Grégory; Cognet, Laurent; Lounis, Brahim

2013-04-01

Single molecule tracking in live cells is the ultimate tool to study subcellular protein dynamics, but it is often limited by the probe size and photostability. Due to these issues, long-term tracking of proteins in confined and crowded environments, such as intracellular spaces, remains challenging. We have developed a novel optical probe consisting of 5-nm gold nanoparticles functionalized with a small fragment of camelid antibodies that recognize widely used GFPs with a very high affinity, which we call GFP-nanobodies. These small gold nanoparticles can be detected and tracked using photothermal imaging for arbitrarily long periods of time. Surface and intracellular GFP-proteins were effectively labeled even in very crowded environments such as adhesion sites and cytoskeletal structures both in vitro and in live cell cultures. These nanobody-coated gold nanoparticles are probes with unparalleled capabilities; small size, perfect photostability, high specificity, and versatility afforded by combination with the vast existing library of GFP-tagged proteins.

Automated breast segmentation in ultrasound computer tomography SAFT images

Science.gov (United States)

Hopp, T.; You, W.; Zapf, M.; Tan, W. Y.; Gemmeke, H.; Ruiter, N. V.

2017-03-01

Ultrasound Computer Tomography (USCT) is a promising new imaging system for breast cancer diagnosis. An essential step before further processing is to remove the water background from the reconstructed images. In this paper we present a fully-automated image segmentation method based on three-dimensional active contours. The active contour method is extended by applying gradient vector flow and encoding the USCT aperture characteristics as additional weighting terms. A surface detection algorithm based on a ray model is developed to initialize the active contour, which is iteratively deformed to capture the breast outline in USCT reflection images. The evaluation with synthetic data showed that the method is able to cope with noisy images, and is not influenced by the position of the breast and the presence of scattering objects within the breast. The proposed method was applied to 14 in-vivo images resulting in an average surface deviation from a manual segmentation of 2.7 mm. We conclude that automated segmentation of USCT reflection images is feasible and produces results comparable to a manual segmentation. By applying the proposed method, reproducible segmentation results can be obtained without manual interaction by an expert.

Enzymatic single-chain antibody tagging: a universal approach to targeted molecular imaging and cell homing in cardiovascular disease.

Science.gov (United States)

Ta, H T; Prabhu, S; Leitner, E; Jia, F; von Elverfeldt, D; Jackson, Katherine E; Heidt, T; Nair, A K N; Pearce, H; von Zur Muhlen, C; Wang, X; Peter, K; Hagemeyer, C E

2011-08-05

Antibody-targeted delivery of imaging agents can enhance the sensitivity and accuracy of current imaging techniques. Similarly, homing of effector cells to disease sites increases the efficacy of regenerative cell therapy while reducing the number of cells required. Currently, targeting can be achieved via chemical conjugation to specific antibodies, which typically results in the loss of antibody functionality and in severe cell damage. An ideal conjugation technique should ensure retention of antigen-binding activity and functionality of the targeted biological component. To develop a biochemically robust, highly reproducible, and site-specific coupling method using the Staphylococcus aureus sortase A enzyme for the conjugation of a single-chain antibody (scFv) to nanoparticles and cells for molecular imaging and cell homing in cardiovascular diseases. This scFv specifically binds to activated platelets, which play a pivotal role in thrombosis, atherosclerosis, and inflammation. The conjugation procedure involves chemical and enzyme-mediated coupling steps. The scFv was successfully conjugated to iron oxide particles (contrast agents for magnetic resonance imaging) and to model cells. Conjugation efficiency ranged between 50% and 70%, and bioactivity of the scFv after coupling was preserved. The targeting of scFv-coupled cells and nanoparticles to activated platelets was strong and specific as demonstrated in in vitro static adhesion assays, in a flow chamber system, in mouse intravital microscopy, and in in vivo magnetic resonance imaging of mouse carotid arteries. This unique biotechnological approach provides a versatile and broadly applicable tool for procuring targeted regenerative cell therapy and targeted molecular imaging in cardiovascular and inflammatory diseases and beyond.

Automated image-matching technique for comparative diagnosis of the liver on CT examination

International Nuclear Information System (INIS)

Okumura, Eiichiro; Sanada, Shigeru; Suzuki, Masayuki; Tsushima, Yoshito; Matsui, Osamu

2005-01-01

When interpreting enhanced computer tomography (CT) images of the upper abdomen, radiologists visually select a set of images of the same anatomical positions from two or more CT image series (i.e., non-enhanced and contrast-enhanced CT images at arterial and delayed phase) to depict and to characterize any abnormalities. The same process is also necessary to create subtraction images by computer. We have developed an automated image selection system using a template-matching technique that allows the recognition of image sets at the same anatomical position from two CT image series. Using the template-matching technique, we compared several anatomical structures in each CT image at the same anatomical position. As the position of the liver may shift according to respiratory movement, not only the shape of the liver but also the gallbladder and other prominent structures included in the CT images were compared to allow appropriate selection of a set of CT images. This novel technique was applied in 11 upper abdominal CT examinations. In CT images with a slice thickness of 7.0 or 7.5 mm, the percentage of image sets selected correctly by the automated procedure was 86.6±15.3% per case. In CT images with a slice thickness of 1.25 mm, the percentages of correct selection of image sets by the automated procedure were 79.4±12.4% (non-enhanced and arterial-phase CT images) and 86.4±10.1% (arterial- and delayed-phase CT images). This automated method is useful for assisting in interpreting CT images and in creating digital subtraction images. (author)

Subunits of highly Fluorescent Protein R-Phycoerythrin as Probes for Cell Imaging and Single-Molecule Detection

Energy Technology Data Exchange (ETDEWEB)

Isailovic, Dragan [Iowa State Univ., Ames, IA (United States)

2005-01-01

The purposes of our research were: (1) To characterize subunits of highly fluorescent protein R-Phycoerythrin (R-PE) and check their suitability for single-molecule detection (SMD) and cell imaging, (2) To extend the use of R-PE subunits through design of similar proteins that will be used as probes for microscopy and spectral imaging in a single cell, and (3) To demonstrate a high-throughput spectral imaging method that will rival spectral flow cytometry in the analysis of individual cells. We first demonstrated that R-PE subunits have spectroscopic and structural characteristics that make them suitable for SMD. Subunits were isolated from R-PE by high-performance liquid chromatography (HPLC) and detected as single molecules by total internal reflection fluorescence microscopy (TIRFM). In addition, R-PE subunits and their enzymatic digests were characterized by several separation and detection methods including HPLC, capillary electrophoresis, sodium dodecyl sulfate-polyacrilamide gel electrophoresis (SDS-PAGE) and HPLC-electrospray ionization mass spectrometry (ESI-MS). Favorable absorption and fluorescence of the R-PE subunits and digest peptides originate from phycoerythrobilin (PEB) and phycourobilin (PUB) chromophores that are covalently attached to cysteine residues. High absorption coefficients and strong fluorescence (even under denaturing conditions), broad excitation and emission fluorescence spectra in the visible region of electromagnetic spectrum, and relatively low molecular weights make these molecules suitable for use as fluorescence labels of biomolecules and cells. We further designed fluorescent proteins both in vitro and in vivo (in Escherichia coli) based on the highly specific attachment of PEB chromophore to genetically expressed apo-subunits of R-PE. In one example, apo-alpha and apo-beta R-PE subunits were cloned from red algae Polisiphonia boldii (P. boldii), and expressed in E. coli. Although expressed apo-subunits formed inclusion

Automated Registration of Multimodal Optic Disc Images: Clinical Assessment of Alignment Accuracy.

Science.gov (United States)

Ng, Wai Siene; Legg, Phil; Avadhanam, Venkat; Aye, Kyaw; Evans, Steffan H P; North, Rachel V; Marshall, Andrew D; Rosin, Paul; Morgan, James E

2016-04-01

To determine the accuracy of automated alignment algorithms for the registration of optic disc images obtained by 2 different modalities: fundus photography and scanning laser tomography. Images obtained with the Heidelberg Retina Tomograph II and paired photographic optic disc images of 135 eyes were analyzed. Three state-of-the-art automated registration techniques Regional Mutual Information, rigid Feature Neighbourhood Mutual Information (FNMI), and nonrigid FNMI (NRFNMI) were used to align these image pairs. Alignment of each composite picture was assessed on a 5-point grading scale: "Fail" (no alignment of vessels with no vessel contact), "Weak" (vessels have slight contact), "Good" (vessels with 50% contact), and "Excellent" (complete alignment). Custom software generated an image mosaic in which the modalities were interleaved as a series of alternate 5×5-pixel blocks. These were graded independently by 3 clinically experienced observers. A total of 810 image pairs were assessed. All 3 registration techniques achieved a score of "Good" or better in >95% of the image sets. NRFNMI had the highest percentage of "Excellent" (mean: 99.6%; range, 95.2% to 99.6%), followed by Regional Mutual Information (mean: 81.6%; range, 86.3% to 78.5%) and FNMI (mean: 73.1%; range, 85.2% to 54.4%). Automated registration of optic disc images by different modalities is a feasible option for clinical application. All 3 methods provided useful levels of alignment, but the NRFNMI technique consistently outperformed the others and is recommended as a practical approach to the automated registration of multimodal disc images.

Automated delineation of stroke lesions using brain CT images

Directory of Open Access Journals (Sweden)

Céline R. Gillebert

2014-01-01

Full Text Available Computed tomographic (CT images are widely used for the identification of abnormal brain tissue following infarct and hemorrhage in stroke. Manual lesion delineation is currently the standard approach, but is both time-consuming and operator-dependent. To address these issues, we present a method that can automatically delineate infarct and hemorrhage in stroke CT images. The key elements of this method are the accurate normalization of CT images from stroke patients into template space and the subsequent voxelwise comparison with a group of control CT images for defining areas with hypo- or hyper-intense signals. Our validation, using simulated and actual lesions, shows that our approach is effective in reconstructing lesions resulting from both infarct and hemorrhage and yields lesion maps spatially consistent with those produced manually by expert operators. A limitation is that, relative to manual delineation, there is reduced sensitivity of the automated method in regions close to the ventricles and the brain contours. However, the automated method presents a number of benefits in terms of offering significant time savings and the elimination of the inter-operator differences inherent to manual tracing approaches. These factors are relevant for the creation of large-scale lesion databases for neuropsychological research. The automated delineation of stroke lesions from CT scans may also enable longitudinal studies to quantify changes in damaged tissue in an objective and reproducible manner.

Study of automated segmentation of the cerebellum and brainstem on brain MR images

International Nuclear Information System (INIS)

Hayashi, Norio; Matsuura, Yukihiro; Sanada, Shigeru; Suzuki, Masayuki

2005-01-01

MR imaging is an important method for diagnosing abnormalities of the brain. This paper presents an automated method to segment the cerebellum and brainstem for brain MR images. MR images were obtained from 10 normal subjects (male 4, female 6; 22-75 years old, average 31.0 years) and 15 patients with brain atrophy (male 3, female 12; 62-85 years of age, average 76.0 years). The automated method consisted of the following four steps: segmentation of the brain on original images, detection of an upper plane of the cerebellum using the Hough transform, correction of the plane using three-dimensional (3D) information, and segmentation of the cerebellum and brainstem using the plane. The results indicated that the regions obtained by the automated method were visually similar to those obtained by a manual method. The average rates of coincidence between the automated method and manual method were 83.0±9.0% in normal subjects and 86.4±3.6% in patients. (author)

Using Single-Protein Tracking to Study Cell Migration.

Science.gov (United States)

Orré, Thomas; Mehidi, Amine; Massou, Sophie; Rossier, Olivier; Giannone, Grégory

2018-01-01

To get a complete understanding of cell migration, it is critical to study its orchestration at the molecular level. Since the recent developments in single-molecule imaging, it is now possible to study molecular phenomena at the single-molecule level inside living cells. In this chapter, we describe how such approaches have been and can be used to decipher molecular mechanisms involved in cell migration.

Validation of an automated counting procedure for phthalate-induced testicular multinucleated germ cells.

Science.gov (United States)

Spade, Daniel J; Bai, Cathy Yue; Lambright, Christy; Conley, Justin M; Boekelheide, Kim; Gray, L Earl

2018-06-15

In utero exposure to certain phthalate esters results in testicular toxicity, characterized at the tissue level by induction of multinucleated germ cells (MNGs) in rat, mouse, and human fetal testis. Phthalate exposures also result in a decrease in testicular testosterone in rats. The anti-androgenic effects of phthalates have been more thoroughly quantified than testicular pathology due to the significant time requirement associated with manual counting of MNGs on histological sections. An automated counting method was developed in ImageJ to quantify MNGs in digital images of hematoxylin-stained rat fetal testis tissue sections. Timed pregnant Sprague Dawley rats were exposed by daily oral gavage from gestation day 17 to 21 with one of eight phthalate test compounds or corn oil vehicle. Both the manual counting method and the automated image analysis method identified di-n-butyl phthalate, butyl benzyl phthalate, dipentyl phthalate, and di-(2-ethylhexyl) phthalate as positive for induction of MNGs. Dimethyl phthalate, diethyl phthalate, the brominated phthalate di-(2-ethylhexyl) tetrabromophthalate, and dioctyl terephthalate were negative. The correlation between automated and manual scoring metrics was high (r = 0.923). Results of MNG analysis were consistent with these compounds' anti-androgenic activities, which were confirmed in an ex vivo testosterone production assay. In conclusion, we have developed a reliable image analysis method that can be used to facilitate dose-response studies for the reproducible induction of MNGs by in utero phthalate exposure. Copyright © 2018 Elsevier B.V. All rights reserved.

An Imaging System for Automated Characteristic Length Measurement of Debrisat Fragments

Science.gov (United States)

Moraguez, Mathew; Patankar, Kunal; Fitz-Coy, Norman; Liou, J.-C.; Sorge, Marlon; Cowardin, Heather; Opiela, John; Krisko, Paula H.

2015-01-01

The debris fragments generated by DebriSat's hypervelocity impact test are currently being processed and characterized through an effort of NASA and USAF. The debris characteristics will be used to update satellite breakup models. In particular, the physical dimensions of the debris fragments must be measured to provide characteristic lengths for use in these models. Calipers and commercial 3D scanners were considered as measurement options, but an automated imaging system was ultimately developed to measure debris fragments. By automating the entire process, the measurement results are made repeatable and the human factor associated with calipers and 3D scanning is eliminated. Unlike using calipers to measure, the imaging system obtains non-contact measurements to avoid damaging delicate fragments. Furthermore, this fully automated measurement system minimizes fragment handling, which reduces the potential for fragment damage during the characterization process. In addition, the imaging system reduces the time required to determine the characteristic length of the debris fragment. In this way, the imaging system can measure the tens of thousands of DebriSat fragments at a rate of about six minutes per fragment, compared to hours per fragment in NASA's current 3D scanning measurement approach. The imaging system utilizes a space carving algorithm to generate a 3D point cloud of the article being measured and a custom developed algorithm then extracts the characteristic length from the point cloud. This paper describes the measurement process, results, challenges, and future work of the imaging system used for automated characteristic length measurement of DebriSat fragments.

Automated Computational Processing of 3-D MR Images of Mouse Brain for Phenotyping of Living Animals.

Science.gov (United States)

Medina, Christopher S; Manifold-Wheeler, Brett; Gonzales, Aaron; Bearer, Elaine L

2017-07-05

Magnetic resonance (MR) imaging provides a method to obtain anatomical information from the brain in vivo that is not typically available by optical imaging because of this organ's opacity. MR is nondestructive and obtains deep tissue contrast with 100-µm 3 voxel resolution or better. Manganese-enhanced MRI (MEMRI) may be used to observe axonal transport and localized neural activity in the living rodent and avian brain. Such enhancement enables researchers to investigate differences in functional circuitry or neuronal activity in images of brains of different animals. Moreover, once MR images of a number of animals are aligned into a single matrix, statistical analysis can be done comparing MR intensities between different multi-animal cohorts comprising individuals from different mouse strains or different transgenic animals, or at different time points after an experimental manipulation. Although preprocessing steps for such comparisons (including skull stripping and alignment) are automated for human imaging, no such automated processing has previously been readily available for mouse or other widely used experimental animals, and most investigators use in-house custom processing. This protocol describes a stepwise method to perform such preprocessing for mouse. © 2017 by John Wiley & Sons, Inc. Copyright © 2017 John Wiley & Sons, Inc.

Fully automated muscle quality assessment by Gabor filtering of second harmonic generation images

Science.gov (United States)

Paesen, Rik; Smolders, Sophie; Vega, José Manolo de Hoyos; Eijnde, Bert O.; Hansen, Dominique; Ameloot, Marcel

2016-02-01

Although structural changes on the sarcomere level of skeletal muscle are known to occur due to various pathologies, rigorous studies of the reduced sarcomere quality remain scarce. This can possibly be explained by the lack of an objective tool for analyzing and comparing sarcomere images across biological conditions. Recent developments in second harmonic generation (SHG) microscopy and increasing insight into the interpretation of sarcomere SHG intensity profiles have made SHG microscopy a valuable tool to study microstructural properties of sarcomeres. Typically, sarcomere integrity is analyzed by fitting a set of manually selected, one-dimensional SHG intensity profiles with a supramolecular SHG model. To circumvent this tedious manual selection step, we developed a fully automated image analysis procedure to map the sarcomere disorder for the entire image at once. The algorithm relies on a single-frequency wavelet-based Gabor approach and includes a newly developed normalization procedure allowing for unambiguous data interpretation. The method was validated by showing the correlation between the sarcomere disorder, quantified by the M-band size obtained from manually selected profiles, and the normalized Gabor value ranging from 0 to 1 for decreasing disorder. Finally, to elucidate the applicability of our newly developed protocol, Gabor analysis was used to study the effect of experimental autoimmune encephalomyelitis on the sarcomere regularity. We believe that the technique developed in this work holds great promise for high-throughput, unbiased, and automated image analysis to study sarcomere integrity by SHG microscopy.

A simple rapid process for semi-automated brain extraction from magnetic resonance images of the whole mouse head.

Science.gov (United States)

Delora, Adam; Gonzales, Aaron; Medina, Christopher S; Mitchell, Adam; Mohed, Abdul Faheem; Jacobs, Russell E; Bearer, Elaine L

2016-01-15

Magnetic resonance imaging (MRI) is a well-developed technique in neuroscience. Limitations in applying MRI to rodent models of neuropsychiatric disorders include the large number of animals required to achieve statistical significance, and the paucity of automation tools for the critical early step in processing, brain extraction, which prepares brain images for alignment and voxel-wise statistics. This novel timesaving automation of template-based brain extraction ("skull-stripping") is capable of quickly and reliably extracting the brain from large numbers of whole head images in a single step. The method is simple to install and requires minimal user interaction. This method is equally applicable to different types of MR images. Results were evaluated with Dice and Jacquard similarity indices and compared in 3D surface projections with other stripping approaches. Statistical comparisons demonstrate that individual variation of brain volumes are preserved. A downloadable software package not otherwise available for extraction of brains from whole head images is included here. This software tool increases speed, can be used with an atlas or a template from within the dataset, and produces masks that need little further refinement. Our new automation can be applied to any MR dataset, since the starting point is a template mask generated specifically for that dataset. The method reliably and rapidly extracts brain images from whole head images, rendering them useable for subsequent analytical processing. This software tool will accelerate the exploitation of mouse models for the investigation of human brain disorders by MRI. Copyright © 2015 Elsevier B.V. All rights reserved.

Tools for automating the imaging of zebrafish larvae.

Science.gov (United States)

Pulak, Rock

2016-03-01

The VAST BioImager system is a set of tools developed for zebrafish researchers who require the collection of images from a large number of 2-7 dpf zebrafish larvae. The VAST BioImager automates larval handling, positioning and orientation tasks. Color images at about 10 μm resolution are collected from the on-board camera of the system. If images of greater resolution and detail are required, this system is mounted on an upright microscope, such as a confocal or fluorescence microscope, to utilize their capabilities. The system loads a larvae, positions it in view of the camera, determines orientation using pattern recognition analysis, and then more precisely positions to user-defined orientation for optimal imaging of any desired tissue or organ system. Multiple images of the same larva can be collected. The specific part of each larva and the desired orientation and position is identified by the researcher and an experiment defining the settings and a series of steps can be saved and repeated for imaging of subsequent larvae. The system captures images, then ejects and loads another larva from either a bulk reservoir, a well of a 96 well plate using the LP Sampler, or individually targeted larvae from a Petri dish or other container using the VAST Pipettor. Alternative manual protocols for handling larvae for image collection are tedious and time consuming. The VAST BioImager automates these steps to allow for greater throughput of assays and screens requiring high-content image collection of zebrafish larvae such as might be used in drug discovery and toxicology studies. Copyright © 2015 The Author. Published by Elsevier Inc. All rights reserved.

Automated processing of X-ray images in medicine

International Nuclear Information System (INIS)

Babij, Ya.S.; B'yalyuk, Ya.O.; Yanovich, I.A.; Lysenko, A.V.

1991-01-01

Theoretical and practical achievements in application of computing technology means for processing of X-ray images in medicine were generalized. The scheme of the main directions and tasks of processing of X-ray images was given and analyzed. The principal problems appeared in automated processing of X-ray images were distinguished. It is shown that for interpretation of X-ray images it is expedient to introduce a notion of relative operating characteristic (ROC) of a roentgenologist. Every point on ROC curve determines the individual criteria of roentgenologist to put a positive diagnosis for definite situation

An Automated Algorithm for Identifying and Tracking Transverse Waves in Solar Images

Science.gov (United States)

Weberg, Micah J.; Morton, Richard J.; McLaughlin, James A.

2018-01-01

Recent instrumentation has demonstrated that the solar atmosphere supports omnipresent transverse waves, which could play a key role in energizing the solar corona. Large-scale studies are required in order to build up an understanding of the general properties of these transverse waves. To help facilitate this, we present an automated algorithm for identifying and tracking features in solar images and extracting the wave properties of any observed transverse oscillations. We test and calibrate our algorithm using a set of synthetic data, which includes noise and rotational effects. The results indicate an accuracy of 1%–2% for displacement amplitudes and 4%–10% for wave periods and velocity amplitudes. We also apply the algorithm to data from the Atmospheric Imaging Assembly on board the Solar Dynamics Observatory and find good agreement with previous studies. Of note, we find that 35%–41% of the observed plumes exhibit multiple wave signatures, which indicates either the superposition of waves or multiple independent wave packets observed at different times within a single structure. The automated methods described in this paper represent a significant improvement on the speed and quality of direct measurements of transverse waves within the solar atmosphere. This algorithm unlocks a wide range of statistical studies that were previously impractical.

High-speed cell recognition algorithm for ultrafast flow cytometer imaging system

Science.gov (United States)

Zhao, Wanyue; Wang, Chao; Chen, Hongwei; Chen, Minghua; Yang, Sigang

2018-04-01

An optical time-stretch flow imaging system enables high-throughput examination of cells/particles with unprecedented high speed and resolution. A significant amount of raw image data is produced. A high-speed cell recognition algorithm is, therefore, highly demanded to analyze large amounts of data efficiently. A high-speed cell recognition algorithm consisting of two-stage cascaded detection and Gaussian mixture model (GMM) classification is proposed. The first stage of detection extracts cell regions. The second stage integrates distance transform and the watershed algorithm to separate clustered cells. Finally, the cells detected are classified by GMM. We compared the performance of our algorithm with support vector machine. Results show that our algorithm increases the running speed by over 150% without sacrificing the recognition accuracy. This algorithm provides a promising solution for high-throughput and automated cell imaging and classification in the ultrafast flow cytometer imaging platform.

Automated image analysis of lateral lumber X-rays by a form model

International Nuclear Information System (INIS)

Mahnken, A.H.; Kohnen, M.; Steinberg, S.; Wein, B.B.; Guenther, R.W.

2001-01-01

Development of a software for fully automated image analysis of lateral lumbar spine X-rays. Material and method: Using the concept of active shape models, we developed a software that produces a form model of the lumbar spine from lateral lumbar spine radiographs and runs an automated image segmentation. This model is able to detect lumbar vertebrae automatically after the filtering of digitized X-ray images. The model was trained with 20 lateral lumbar spine radiographs with no pathological findings before we evaluated the software with 30 further X-ray images which were sorted by image quality ranging from one (best) to three (worst). There were 10 images for each quality. Results: Image recognition strongly depended on image quality. In group one 52 and in group two 51 out of 60 vertebral bodies including the sacrum were recognized, but in group three only 18 vertebral bodies were properly identified. Conclusion: Fully automated and reliable recognition of vertebral bodies from lateral spine radiographs using the concept of active shape models is possible. The precision of this technique is limited by the superposition of different structures. Further improvements are necessary. Therefore standardized image quality and enlargement of the training data set are required. (orig.) [de

Sampling strategies to capture single-cell heterogeneity

OpenAIRE

Satwik Rajaram; Louise E. Heinrich; John D. Gordan; Jayant Avva; Kathy M. Bonness; Agnieszka K. Witkiewicz; James S. Malter; Chloe E. Atreya; Robert S. Warren; Lani F. Wu; Steven J. Altschuler

2017-01-01

Advances in single-cell technologies have highlighted the prevalence and biological significance of cellular heterogeneity. A critical question is how to design experiments that faithfully capture the true range of heterogeneity from samples of cellular populations. Here, we develop a data-driven approach, illustrated in the context of image data, that estimates the sampling depth required for prospective investigations of single-cell heterogeneity from an existing collection of samples. ...

Cell motility dynamics: a novel segmentation algorithm to quantify multi-cellular bright field microscopy images.

Directory of Open Access Journals (Sweden)

Assaf Zaritsky

Full Text Available Confocal microscopy analysis of fluorescence and morphology is becoming the standard tool in cell biology and molecular imaging. Accurate quantification algorithms are required to enhance the understanding of different biological phenomena. We present a novel approach based on image-segmentation of multi-cellular regions in bright field images demonstrating enhanced quantitative analyses and better understanding of cell motility. We present MultiCellSeg, a segmentation algorithm to separate between multi-cellular and background regions for bright field images, which is based on classification of local patches within an image: a cascade of Support Vector Machines (SVMs is applied using basic image features. Post processing includes additional classification and graph-cut segmentation to reclassify erroneous regions and refine the segmentation. This approach leads to a parameter-free and robust algorithm. Comparison to an alternative algorithm on wound healing assay images demonstrates its superiority. The proposed approach was used to evaluate common cell migration models such as wound healing and scatter assay. It was applied to quantify the acceleration effect of Hepatocyte growth factor/scatter factor (HGF/SF on healing rate in a time lapse confocal microscopy wound healing assay and demonstrated that the healing rate is linear in both treated and untreated cells, and that HGF/SF accelerates the healing rate by approximately two-fold. A novel fully automated, accurate, zero-parameters method to classify and score scatter-assay images was developed and demonstrated that multi-cellular texture is an excellent descriptor to measure HGF/SF-induced cell scattering. We show that exploitation of textural information from differential interference contrast (DIC images on the multi-cellular level can prove beneficial for the analyses of wound healing and scatter assays. The proposed approach is generic and can be used alone or alongside traditional

Cell motility dynamics: a novel segmentation algorithm to quantify multi-cellular bright field microscopy images.

Science.gov (United States)

Zaritsky, Assaf; Natan, Sari; Horev, Judith; Hecht, Inbal; Wolf, Lior; Ben-Jacob, Eshel; Tsarfaty, Ilan

2011-01-01

Confocal microscopy analysis of fluorescence and morphology is becoming the standard tool in cell biology and molecular imaging. Accurate quantification algorithms are required to enhance the understanding of different biological phenomena. We present a novel approach based on image-segmentation of multi-cellular regions in bright field images demonstrating enhanced quantitative analyses and better understanding of cell motility. We present MultiCellSeg, a segmentation algorithm to separate between multi-cellular and background regions for bright field images, which is based on classification of local patches within an image: a cascade of Support Vector Machines (SVMs) is applied using basic image features. Post processing includes additional classification and graph-cut segmentation to reclassify erroneous regions and refine the segmentation. This approach leads to a parameter-free and robust algorithm. Comparison to an alternative algorithm on wound healing assay images demonstrates its superiority. The proposed approach was used to evaluate common cell migration models such as wound healing and scatter assay. It was applied to quantify the acceleration effect of Hepatocyte growth factor/scatter factor (HGF/SF) on healing rate in a time lapse confocal microscopy wound healing assay and demonstrated that the healing rate is linear in both treated and untreated cells, and that HGF/SF accelerates the healing rate by approximately two-fold. A novel fully automated, accurate, zero-parameters method to classify and score scatter-assay images was developed and demonstrated that multi-cellular texture is an excellent descriptor to measure HGF/SF-induced cell scattering. We show that exploitation of textural information from differential interference contrast (DIC) images on the multi-cellular level can prove beneficial for the analyses of wound healing and scatter assays. The proposed approach is generic and can be used alone or alongside traditional fluorescence single-cell

Automated local bright feature image analysis of nuclear proteindistribution identifies changes in tissue phenotype

Energy Technology Data Exchange (ETDEWEB)

Knowles, David; Sudar, Damir; Bator, Carol; Bissell, Mina

2006-02-01

The organization of nuclear proteins is linked to cell and tissue phenotypes. When cells arrest proliferation, undergo apoptosis, or differentiate, the distribution of nuclear proteins changes. Conversely, forced alteration of the distribution of nuclear proteins modifies cell phenotype. Immunostaining and fluorescence microscopy have been critical for such findings. However, there is an increasing need for quantitative analysis of nuclear protein distribution to decipher epigenetic relationships between nuclear structure and cell phenotype, and to unravel the mechanisms linking nuclear structure and function. We have developed imaging methods to quantify the distribution of fluorescently-stained nuclear protein NuMA in different mammary phenotypes obtained using three-dimensional cell culture. Automated image segmentation of DAPI-stained nuclei was generated to isolate thousands of nuclei from three-dimensional confocal images. Prominent features of fluorescently-stained NuMA were detected using a novel local bright feature analysis technique, and their normalized spatial density calculated as a function of the distance from the nuclear perimeter to its center. The results revealed marked changes in the distribution of the density of NuMA bright features as non-neoplastic cells underwent phenotypically normal acinar morphogenesis. In contrast, we did not detect any reorganization of NuMA during the formation of tumor nodules by malignant cells. Importantly, the analysis also discriminated proliferating non-neoplastic cells from proliferating malignant cells, suggesting that these imaging methods are capable of identifying alterations linked not only to the proliferation status but also to the malignant character of cells. We believe that this quantitative analysis will have additional applications for classifying normal and pathological tissues.

The influence of image setting on intracranial translucency measurement by manual and semi-automated system.

Science.gov (United States)

Zhen, Li; Yang, Xin; Ting, Yuen Ha; Chen, Min; Leung, Tak Yeung

2013-09-01

To investigate the agreement between manual and semi-automated system and the effect of different image settings on intracranial translucency (IT) measurement. A prospective study was conducted on 55 women carrying singleton pregnancy who attended first trimester Down syndrome screening. IT was measured both manually and by semi-automated system at the same default image setting. The IT measurements were then repeated with the post-processing changes in the image setting one at a time. The difference in IT measurements between the altered and the original images were assessed. Intracranial translucency was successfully measured on 55 images both manually and by semi-automated method. There was strong agreement in IT measurements between the two methods with a mean difference (manual minus semi-automated) of 0.011 mm (95% confidence interval--0.052 mm-0.094 mm). There were statistically significant variations in both manual and semi-automated IT measurement after changing the Gain and the Contrast. The greatest changes occurred when the Contrast was reduced to 1 (IT reduced by 0.591 mm in semi-automated; 0.565 mm in manual), followed by when the Gain was increased to 15 (IT reduced by 0.424 mm in semi-automated; 0.524 mm in manual). The image settings may affect IT identification and measurement. Increased Gain and reduced Contrast are the most influential factors and may cause under-measurement of IT. © 2013 John Wiley & Sons, Ltd.

The impact of air pollution on the level of micronuclei measured by automated image analysis

Czech Academy of Sciences Publication Activity Database

Rössnerová, Andrea; Špátová, Milada; Rossner, P.; Solanský, I.; Šrám, Radim

2009-01-01

Roč. 669, 1-2 (2009), s. 42-47 ISSN 0027-5107 R&D Projects: GA AV ČR 1QS500390506; GA MŠk 2B06088; GA MŠk 2B08005 Institutional research plan: CEZ:AV0Z50390512 Keywords : micronuclei * binucleated cells * automated image analysis Subject RIV: DN - Health Impact of the Environment Quality Impact factor: 3.556, year: 2009

Unravel lipid accumulation mechanism in oleaginous yeast through single cell systems biology study

Energy Technology Data Exchange (ETDEWEB)

Ding, Shiyou; Xiaoliang, Xie

2017-12-18

Replacement of petroleum with advanced biofuels is critical for environmental protection needs, sustainable and secure energy demands, and economic development. Bacteria, yeasts, and fungi can naturally synthesize fatty acids, isoprenoids, or polyalkanoates for energy storage, and therefore are currently explored for hydrocarbon fuel production. Oleaginous yeasts can accumulate high levels of lipids in the form of triacylglycerols (TAGs) when encountering stress conditions or imbalanced growth (e.g., growing under excess carbon sources and limited nitrogen conditions). Advantages of using oleaginous yeast as cell factories include short duplication time (< 1 hour), high yield of intracellular droplets, and easy scale-up for industrial production. Currently, various oleaginous yeasts (e.g., Yarrowia, Candida, Rhodotorulla, Rhodosporidium, Cryptococcus, Trichosporon, and Lipomyces) have been developed as potential advanced biofuel producers. Oleaginous yeast lipid production has two phases: 1) growth phase, where cells utilize the carbon and nitrogen source to build up biomass. And 2) lipid accumulation phase, where they convert carbon source in media into the storage lipid body. (i.e. a high carbon to nitrogen ratio leads to high lipid production). The lipid production varies dramatically when different sugar, e.g. glucose, xylose is used as carbon source. The efficient utilization of all monomeric sugars of hexoses and pentoses from various lignocellulosic biomass processing approaches is the key for economic lignocellulosic biofuel production. In this project, we explored lipid production in oleaginous yeast under different nitrogen and sugar conditions at the single-cell level. To understand the lipid production mechanism and identify genetic features responsive to lipid accumulation in the presence of pentose and nitrogen, we developed an automated chemical imaging and single-cell transcriptomics method to correlate the lipid accumulation with the

Aberration-free FTIR spectroscopic imaging of live cells in microfluidic devices.

Science.gov (United States)

Chan, K L Andrew; Kazarian, Sergei G

2013-07-21

The label-free, non-destructive chemical analysis offered by FTIR spectroscopic imaging is a very attractive and potentially powerful tool for studies of live biological cells. FTIR imaging of live cells is a challenging task, due to the fact that cells are cultured in an aqueous environment. While the synchrotron facility has proven to be a valuable tool for FTIR microspectroscopic studies of single live cells, we have demonstrated that high quality infrared spectra of single live cells using an ordinary Globar source can also be obtained by adding a pair of lenses to a common transmission liquid cell. The lenses, when placed on the transmission cell window, form pseudo hemispheres which removes the refraction of light and hence improve the imaging and spectral quality of the obtained data. This study demonstrates that infrared spectra of single live cells can be obtained without the focus shifting effect at different wavenumbers, caused by the chromatic aberration. Spectra of the single cells have confirmed that the measured spectral region remains in focus across the whole range, while spectra of the single cells measured without the lenses have shown some erroneous features as a result of the shift of focus. It has also been demonstrated that the addition of lenses can be applied to the imaging of cells in microfabricated devices. We have shown that it was not possible to obtain a focused image of an isolated cell in a droplet of DPBS in oil unless the lenses are applied. The use of the approach described herein allows for well focused images of single cells in DPBS droplets to be obtained.

Automated registration of multispectral MR vessel wall images of the carotid artery

Energy Technology Data Exchange (ETDEWEB)

Klooster, R. van ' t; Staring, M.; Reiber, J. H. C.; Lelieveldt, B. P. F.; Geest, R. J. van der, E-mail: rvdgeest@lumc.nl [Department of Radiology, Division of Image Processing, Leiden University Medical Center, 2300 RC Leiden (Netherlands); Klein, S. [Department of Radiology and Department of Medical Informatics, Biomedical Imaging Group Rotterdam, Erasmus MC, Rotterdam 3015 GE (Netherlands); Kwee, R. M.; Kooi, M. E. [Department of Radiology, Cardiovascular Research Institute Maastricht, Maastricht University Medical Center, Maastricht 6202 AZ (Netherlands)

2013-12-15

Purpose: Atherosclerosis is the primary cause of heart disease and stroke. The detailed assessment of atherosclerosis of the carotid artery requires high resolution imaging of the vessel wall using multiple MR sequences with different contrast weightings. These images allow manual or automated classification of plaque components inside the vessel wall. Automated classification requires all sequences to be in alignment, which is hampered by patient motion. In clinical practice, correction of this motion is performed manually. Previous studies applied automated image registration to correct for motion using only nondeformable transformation models and did not perform a detailed quantitative validation. The purpose of this study is to develop an automated accurate 3D registration method, and to extensively validate this method on a large set of patient data. In addition, the authors quantified patient motion during scanning to investigate the need for correction. Methods: MR imaging studies (1.5T, dedicated carotid surface coil, Philips) from 55 TIA/stroke patients with ipsilateral <70% carotid artery stenosis were randomly selected from a larger cohort. Five MR pulse sequences were acquired around the carotid bifurcation, each containing nine transverse slices: T1-weighted turbo field echo, time of flight, T2-weighted turbo spin-echo, and pre- and postcontrast T1-weighted turbo spin-echo images (T1W TSE). The images were manually segmented by delineating the lumen contour in each vessel wall sequence and were manually aligned by applying throughplane and inplane translations to the images. To find the optimal automatic image registration method, different masks, choice of the fixed image, different types of the mutual information image similarity metric, and transformation models including 3D deformable transformation models, were evaluated. Evaluation of the automatic registration results was performed by comparing the lumen segmentations of the fixed image and

Automated registration of multispectral MR vessel wall images of the carotid artery

International Nuclear Information System (INIS)

Klooster, R. van 't; Staring, M.; Reiber, J. H. C.; Lelieveldt, B. P. F.; Geest, R. J. van der; Klein, S.; Kwee, R. M.; Kooi, M. E.

2013-01-01

Purpose: Atherosclerosis is the primary cause of heart disease and stroke. The detailed assessment of atherosclerosis of the carotid artery requires high resolution imaging of the vessel wall using multiple MR sequences with different contrast weightings. These images allow manual or automated classification of plaque components inside the vessel wall. Automated classification requires all sequences to be in alignment, which is hampered by patient motion. In clinical practice, correction of this motion is performed manually. Previous studies applied automated image registration to correct for motion using only nondeformable transformation models and did not perform a detailed quantitative validation. The purpose of this study is to develop an automated accurate 3D registration method, and to extensively validate this method on a large set of patient data. In addition, the authors quantified patient motion during scanning to investigate the need for correction. Methods: MR imaging studies (1.5T, dedicated carotid surface coil, Philips) from 55 TIA/stroke patients with ipsilateral <70% carotid artery stenosis were randomly selected from a larger cohort. Five MR pulse sequences were acquired around the carotid bifurcation, each containing nine transverse slices: T1-weighted turbo field echo, time of flight, T2-weighted turbo spin-echo, and pre- and postcontrast T1-weighted turbo spin-echo images (T1W TSE). The images were manually segmented by delineating the lumen contour in each vessel wall sequence and were manually aligned by applying throughplane and inplane translations to the images. To find the optimal automatic image registration method, different masks, choice of the fixed image, different types of the mutual information image similarity metric, and transformation models including 3D deformable transformation models, were evaluated. Evaluation of the automatic registration results was performed by comparing the lumen segmentations of the fixed image and

Screening of subfertile men for testicular carcinoma in situ by an automated image analysis-based cytological test of the ejaculate

DEFF Research Database (Denmark)

Almstrup, K; Lippert, Marianne; Mogensen, Hanne O

2011-01-01

a slightly lower sensitivity (0.51), possibly because of obstruction. We conclude that this novel non-invasive test combining automated immunocytochemistry and advanced image analysis allows identification of TC at the CIS stage with a high specificity, but a negative test does not completely exclude CIS...... and detected in ejaculates with specific CIS markers. We have built a high throughput framework involving automated immunocytochemical staining, scanning microscopy and in silico image analysis allowing automated detection and grading of CIS-like stained objects in semen samples. In this study, 1175 ejaculates...... from 765 subfertile men were tested using this framework. In 5/765 (0.65%) cases, CIS-like cells were identified in the ejaculate. Three of these had bilateral testicular biopsies performed and CIS was histologically confirmed in two. In total, 63 bilateral testicular biopsy were performed...

Single-Cell mRNA-Seq Using the Fluidigm C1 System and Integrated Fluidics Circuits.

Science.gov (United States)

Gong, Haibiao; Do, Devin; Ramakrishnan, Ramesh

2018-01-01

Single-cell mRNA-seq is a valuable tool to dissect expression profiles and to understand the regulatory network of genes. Microfluidics is well suited for single-cell analysis owing both to the small volume of the reaction chambers and easiness of automation. Here we describe the workflow of single-cell mRNA-seq using C1 IFC, which can isolate and process up to 96 cells. Both on-chip procedure (lysis, reverse transcription, and preamplification PCR) and off-chip sequencing library preparation protocols are described. The workflow generates full-length mRNA information, which is more valuable compared to 3' end counting method for many applications.

Automation of 3D cell culture using chemically defined hydrogels.

Science.gov (United States)

Rimann, Markus; Angres, Brigitte; Patocchi-Tenzer, Isabel; Braum, Susanne; Graf-Hausner, Ursula

2014-04-01

Drug development relies on high-throughput screening involving cell-based assays. Most of the assays are still based on cells grown in monolayer rather than in three-dimensional (3D) formats, although cells behave more in vivo-like in 3D. To exemplify the adoption of 3D techniques in drug development, this project investigated the automation of a hydrogel-based 3D cell culture system using a liquid-handling robot. The hydrogel technology used offers high flexibility of gel design due to a modular composition of a polymer network and bioactive components. The cell inert degradation of the gel at the end of the culture period guaranteed the harmless isolation of live cells for further downstream processing. Human colon carcinoma cells HCT-116 were encapsulated and grown in these dextran-based hydrogels, thereby forming 3D multicellular spheroids. Viability and DNA content of the cells were shown to be similar in automated and manually produced hydrogels. Furthermore, cell treatment with toxic Taxol concentrations (100 nM) had the same effect on HCT-116 cell viability in manually and automated hydrogel preparations. Finally, a fully automated dose-response curve with the reference compound Taxol showed the potential of this hydrogel-based 3D cell culture system in advanced drug development.

Enhanced Automated Guidance System for Horizontal Auger Boring Based on Image Processing.

Science.gov (United States)

Wu, Lingling; Wen, Guojun; Wang, Yudan; Huang, Lei; Zhou, Jiang

2018-02-15

Horizontal auger boring (HAB) is a widely used trenchless technology for the high-accuracy installation of gravity or pressure pipelines on line and grade. Differing from other pipeline installations, HAB requires a more precise and automated guidance system for use in a practical project. This paper proposes an economic and enhanced automated optical guidance system, based on optimization research of light-emitting diode (LED) light target and five automated image processing bore-path deviation algorithms. An LED light target was optimized for many qualities, including light color, filter plate color, luminous intensity, and LED layout. The image preprocessing algorithm, direction location algorithm, angle measurement algorithm, deflection detection algorithm, and auto-focus algorithm, compiled in MATLAB, are used to automate image processing for deflection computing and judging. After multiple indoor experiments, this guidance system is applied in a project of hot water pipeline installation, with accuracy controlled within 2 mm in 48-m distance, providing accurate line and grade controls and verifying the feasibility and reliability of the guidance system.

Automated image segmentation using information theory

International Nuclear Information System (INIS)

Hibbard, L.S.

2001-01-01

Full text: Our development of automated contouring of CT images for RT planning is based on maximum a posteriori (MAP) analyses of region textures, edges, and prior shapes, and assumes stationary Gaussian distributions for voxel textures and contour shapes. Since models may not accurately represent image data, it would be advantageous to compute inferences without relying on models. The relative entropy (RE) from information theory can generate inferences based solely on the similarity of probability distributions. The entropy of a distribution of a random variable X is defined as -Σ x p(x)log 2 p(x) for all the values x which X may assume. The RE (Kullback-Liebler divergence) of two distributions p(X), q(X), over X is Σ x p(x)log 2 {p(x)/q(x)}. The RE is a kind of 'distance' between p,q, equaling zero when p=q and increasing as p,q are more different. Minimum-error MAP and likelihood ratio decision rules have RE equivalents: minimum error decisions obtain with functions of the differences between REs of compared distributions. One applied result is the contour ideally separating two regions is that which maximizes the relative entropy of the two regions' intensities. A program was developed that automatically contours the outlines of patients in stereotactic headframes, a situation most often requiring manual drawing. The relative entropy of intensities inside the contour (patient) versus outside (background) was maximized by conjugate gradient descent over the space of parameters of a deformable contour. shows the computed segmentation of a patient from headframe backgrounds. This program is particularly useful for preparing images for multimodal image fusion. Relative entropy and allied measures of distribution similarity provide automated contouring criteria that do not depend on statistical models of image data. This approach should have wide utility in medical image segmentation applications. Copyright (2001) Australasian College of Physical Scientists and

21 CFR 864.5240 - Automated blood cell diluting apparatus.

Science.gov (United States)

2010-04-01

... 21 Food and Drugs 8 2010-04-01 2010-04-01 false Automated blood cell diluting apparatus. 864.5240 Section 864.5240 Food and Drugs FOOD AND DRUG ADMINISTRATION, DEPARTMENT OF HEALTH AND HUMAN SERVICES (CONTINUED) MEDICAL DEVICES HEMATOLOGY AND PATHOLOGY DEVICES Automated and Semi-Automated Hematology Devices...

Quantification of the heterogeneity of prognostic cellular biomarkers in ewing sarcoma using automated image and random survival forest analysis.

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Claudia Bühnemann

Full Text Available Driven by genomic somatic variation, tumour tissues are typically heterogeneous, yet unbiased quantitative methods are rarely used to analyse heterogeneity at the protein level. Motivated by this problem, we developed automated image segmentation of images of multiple biomarkers in Ewing sarcoma to generate distributions of biomarkers between and within tumour cells. We further integrate high dimensional data with patient clinical outcomes utilising random survival forest (RSF machine learning. Using material from cohorts of genetically diagnosed Ewing sarcoma with EWSR1 chromosomal translocations, confocal images of tissue microarrays were segmented with level sets and watershed algorithms. Each cell nucleus and cytoplasm were identified in relation to DAPI and CD99, respectively, and protein biomarkers (e.g. Ki67, pS6, Foxo3a, EGR1, MAPK localised relative to nuclear and cytoplasmic regions of each cell in order to generate image feature distributions. The image distribution features were analysed with RSF in relation to known overall patient survival from three separate cohorts (185 informative cases. Variation in pre-analytical processing resulted in elimination of a high number of non-informative images that had poor DAPI localisation or biomarker preservation (67 cases, 36%. The distribution of image features for biomarkers in the remaining high quality material (118 cases, 104 features per case were analysed by RSF with feature selection, and performance assessed using internal cross-validation, rather than a separate validation cohort. A prognostic classifier for Ewing sarcoma with low cross-validation error rates (0.36 was comprised of multiple features, including the Ki67 proliferative marker and a sub-population of cells with low cytoplasmic/nuclear ratio of CD99. Through elimination of bias, the evaluation of high-dimensionality biomarker distribution within cell populations of a tumour using random forest analysis in quality

Quantification of the heterogeneity of prognostic cellular biomarkers in ewing sarcoma using automated image and random survival forest analysis.

Science.gov (United States)

Bühnemann, Claudia; Li, Simon; Yu, Haiyue; Branford White, Harriet; Schäfer, Karl L; Llombart-Bosch, Antonio; Machado, Isidro; Picci, Piero; Hogendoorn, Pancras C W; Athanasou, Nicholas A; Noble, J Alison; Hassan, A Bassim

2014-01-01

Driven by genomic somatic variation, tumour tissues are typically heterogeneous, yet unbiased quantitative methods are rarely used to analyse heterogeneity at the protein level. Motivated by this problem, we developed automated image segmentation of images of multiple biomarkers in Ewing sarcoma to generate distributions of biomarkers between and within tumour cells. We further integrate high dimensional data with patient clinical outcomes utilising random survival forest (RSF) machine learning. Using material from cohorts of genetically diagnosed Ewing sarcoma with EWSR1 chromosomal translocations, confocal images of tissue microarrays were segmented with level sets and watershed algorithms. Each cell nucleus and cytoplasm were identified in relation to DAPI and CD99, respectively, and protein biomarkers (e.g. Ki67, pS6, Foxo3a, EGR1, MAPK) localised relative to nuclear and cytoplasmic regions of each cell in order to generate image feature distributions. The image distribution features were analysed with RSF in relation to known overall patient survival from three separate cohorts (185 informative cases). Variation in pre-analytical processing resulted in elimination of a high number of non-informative images that had poor DAPI localisation or biomarker preservation (67 cases, 36%). The distribution of image features for biomarkers in the remaining high quality material (118 cases, 104 features per case) were analysed by RSF with feature selection, and performance assessed using internal cross-validation, rather than a separate validation cohort. A prognostic classifier for Ewing sarcoma with low cross-validation error rates (0.36) was comprised of multiple features, including the Ki67 proliferative marker and a sub-population of cells with low cytoplasmic/nuclear ratio of CD99. Through elimination of bias, the evaluation of high-dimensionality biomarker distribution within cell populations of a tumour using random forest analysis in quality controlled tumour

Automated microscopy for high-content RNAi screening

Science.gov (United States)

2010-01-01

Fluorescence microscopy is one of the most powerful tools to investigate complex cellular processes such as cell division, cell motility, or intracellular trafficking. The availability of RNA interference (RNAi) technology and automated microscopy has opened the possibility to perform cellular imaging in functional genomics and other large-scale applications. Although imaging often dramatically increases the content of a screening assay, it poses new challenges to achieve accurate quantitative annotation and therefore needs to be carefully adjusted to the specific needs of individual screening applications. In this review, we discuss principles of assay design, large-scale RNAi, microscope automation, and computational data analysis. We highlight strategies for imaging-based RNAi screening adapted to different library and assay designs. PMID:20176920

3D high- and super-resolution imaging using single-objective SPIM.

Science.gov (United States)

Galland, Remi; Grenci, Gianluca; Aravind, Ajay; Viasnoff, Virgile; Studer, Vincent; Sibarita, Jean-Baptiste

2015-07-01

Single-objective selective-plane illumination microscopy (soSPIM) is achieved with micromirrored cavities combined with a laser beam-steering unit installed on a standard inverted microscope. The illumination and detection are done through the same objective. soSPIM can be used with standard sample preparations and features high background rejection and efficient photon collection, allowing for 3D single-molecule-based super-resolution imaging of whole cells or cell aggregates. Using larger mirrors enabled us to broaden the capabilities of our system to image Drosophila embryos.

Automated extraction of radiation dose information from CT dose report images.

Science.gov (United States)

Li, Xinhua; Zhang, Da; Liu, Bob

2011-06-01

The purpose of this article is to describe the development of an automated tool for retrieving texts from CT dose report images. Optical character recognition was adopted to perform text recognitions of CT dose report images. The developed tool is able to automate the process of analyzing multiple CT examinations, including text recognition, parsing, error correction, and exporting data to spreadsheets. The results were precise for total dose-length product (DLP) and were about 95% accurate for CT dose index and DLP of scanned series.

Usefulness of automated biopsy guns in image-guided biopsy

International Nuclear Information System (INIS)

Lee, Jung Hyung; Rhee, Chang Soo; Lee, Sung Moon; Kim, Hong; Woo, Sung Ku; Suh, Soo Jhi

1994-01-01

To evaluate the usefulness of automated biopsy guns in image-guided biopsy of lung, liver, pancreas and other organs. Using automated biopsy devices, 160 biopsies of variable anatomic sites were performed: Biopsies were performed under ultrasonographic(US) guidance in 95 and computed tomographic (CT) guidance in 65. We retrospectively analyzed histologic results and complications. Specimens were adequate for histopathologic diagnosis in 143 of the 160 patients(89.4%)-Diagnostic tissue was obtained in 130 (81.3%), suggestive tissue obtained in 13(8.1%), and non-diagnostic tissue was obtained in 14(8.7%). Inadequate tissue was obtained in only 3(1.9%). There was no statistically significant difference between US-guided and CT-guided percutaneous biopsy. There was no occurrence of significant complication. We have experienced mild complications in only 5 patients-2 hematuria and 2 hematochezia in transrectal prostatic biopsy, and 1 minimal pneumothorax in CT-guided percutaneous lung biopsy. All of them were resolved spontaneously. The image-guided biopsy using the automated biopsy gun was a simple, safe and accurate method of obtaining adequate specimen for the histopathologic diagnosis

Usefulness of automated biopsy guns in image-guided biopsy

Energy Technology Data Exchange (ETDEWEB)

Lee, Jung Hyung; Rhee, Chang Soo; Lee, Sung Moon; Kim, Hong; Woo, Sung Ku; Suh, Soo Jhi [School of Medicine, Keimyung University, Daegu (Korea, Republic of)

1994-12-15

To evaluate the usefulness of automated biopsy guns in image-guided biopsy of lung, liver, pancreas and other organs. Using automated biopsy devices, 160 biopsies of variable anatomic sites were performed: Biopsies were performed under ultrasonographic(US) guidance in 95 and computed tomographic (CT) guidance in 65. We retrospectively analyzed histologic results and complications. Specimens were adequate for histopathologic diagnosis in 143 of the 160 patients(89.4%)-Diagnostic tissue was obtained in 130 (81.3%), suggestive tissue obtained in 13(8.1%), and non-diagnostic tissue was obtained in 14(8.7%). Inadequate tissue was obtained in only 3(1.9%). There was no statistically significant difference between US-guided and CT-guided percutaneous biopsy. There was no occurrence of significant complication. We have experienced mild complications in only 5 patients-2 hematuria and 2 hematochezia in transrectal prostatic biopsy, and 1 minimal pneumothorax in CT-guided percutaneous lung biopsy. All of them were resolved spontaneously. The image-guided biopsy using the automated biopsy gun was a simple, safe and accurate method of obtaining adequate specimen for the histopathologic diagnosis.

Automated quantitative cytological analysis using portable microfluidic microscopy.

Science.gov (United States)

Jagannadh, Veerendra Kalyan; Murthy, Rashmi Sreeramachandra; Srinivasan, Rajesh; Gorthi, Sai Siva

2016-06-01

In this article, a portable microfluidic microscopy based approach for automated cytological investigations is presented. Inexpensive optical and electronic components have been used to construct a simple microfluidic microscopy system. In contrast to the conventional slide-based methods, the presented method employs microfluidics to enable automated sample handling and image acquisition. The approach involves the use of simple in-suspension staining and automated image acquisition to enable quantitative cytological analysis of samples. The applicability of the presented approach to research in cellular biology is shown by performing an automated cell viability assessment on a given population of yeast cells. Further, the relevance of the presented approach to clinical diagnosis and prognosis has been demonstrated by performing detection and differential assessment of malaria infection in a given sample. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Gigapixel imaging with microlens arrays

Science.gov (United States)

Orth, Antony; Schonbrun, Ethan

2016-03-01

A crucial part of the drug discovery process involves imaging the response of thousands of cell cultures to candidate drugs. Quantitative parameters from these "high content screens", such as protein expression and cell morphology, are extracted from fluorescence and brightfield micrographs. Due to the sheer number of cells that need to imaged for adequate statistics, the imaging time itself is a major bottleneck. Automated microscopes image small fields-of-view (FOVs) serially, which are then stitched together to form gigapixel-scale mosaics. We have developed a microscopy architecture that reduces mechanical overhead of traditional large field-of-view by parallelizing the image capture process. Instead of a single objective lens imaging FOVs one by one, we employ a microlens array for continuous photon capture, resulting in a 3-fold throughput increase. In this contribution, we present the design and imaging results of this microscopy architecture in three different contrast modes: multichannel fluorescence, hyperspectral fluorescence and brightfield.

Functional topography of single cortical cells: an intracellular approach combined with optical imaging.

Science.gov (United States)

Buzás, P; Eysel, U T; Kisvárday, Z F

1998-11-01

Pyramidal cells mediating long-range corticocortical connections have been assumed to play an important role in visual perceptual mechanisms [C.D. Gilbert, Horizontal integration and cortical dynamics, Neuron 9 (1992) 1-13]. However, no information is available as yet on the specificity of individual pyramidal cells with respect to functional maps, e.g., orientation map. Here, we show a combination of techniques with which the functional topography of single pyramidal neurons can be explored in utmost detail. To this end, we used optical imaging of intrinsic signals followed by intracellular recording and staining with biocytin in vivo. The axonal and dendritic trees of the labelled neurons were reconstructed in three dimensions and aligned with corresponding functional orientation maps. The results indicate that, contrary to the sharp orientation tuning of neurons shown by the recorded spike activity, the efferent connections (axon terminal distribution) of the same pyramidal cells were found to terminate at a much broader range of orientations. Copyright 1998 Elsevier Science B.V.

Digital image analysis applied to industrial nondestructive evaluation and automated parts assembly

International Nuclear Information System (INIS)

Janney, D.H.; Kruger, R.P.

1979-01-01

Many ideas of image enhancement and analysis are relevant to the needs of the nondestructive testing engineer. These ideas not only aid the engineer in the performance of his current responsibilities, they also open to him new areas of industrial development and automation which are logical extensions of classical testing problems. The paper begins with a tutorial on the fundamentals of computerized image enhancement as applied to nondestructive testing, then progresses through pattern recognition and automated inspection to automated, or robotic, assembly procedures. It is believed that such procedures are cost-effective in many instances, and are but the logical extension of those techniques now commonly used, but often limited to analysis of data from quality-assurance images. Many references are given in order to help the reader who wishes to pursue a given idea further

Semi-automated digital measurement as the method of choice for beta cell mass analysis.

Directory of Open Access Journals (Sweden)

Violette Coppens

Full Text Available Pancreas injury by partial duct ligation (PDL activates beta cell differentiation and proliferation in adult mouse pancreas but remains controversial regarding the anticipated increase in beta cell volume. Several reports unable to show beta cell volume augmentation in PDL pancreas used automated digital image analysis software. We hypothesized that fully automatic beta cell morphometry without manual micrograph artifact remediation introduces bias and therefore might be responsible for reported discrepancies and controversy. However, our present results prove that standard digital image processing with automatic thresholding is sufficiently robust albeit less sensitive and less adequate to demonstrate a significant increase in beta cell volume in PDL versus Sham-operated pancreas. We therefore conclude that other confounding factors such as quality of surgery, selection of samples based on relative abundance of the transcription factor Neurogenin 3 (Ngn3 and tissue processing give rise to inter-laboratory inconsistencies in beta cell volume quantification in PDL pancreas.

Fourier Transform Near Infrared Microspectroscopy, Infrared Chemical Imaging, High-Resolution Nuclear Magnetic Resonance and Fluorescence Microspectroscopy Detection of Single Cancer Cells and Single Viral Particles

CERN Document Server

Baianu,I C; Hofmann, N E; Korban, S S; Lozano, P; You, T

2004-01-01

Single Cancer Cells from Human tumors are being detected and imaged by Fourier Transform Infrared (FT-IR), Fourier Transform Near Infrared (FT-NIR)Hyperspectral Imaging and Fluorescence Correlation Microspectroscopy. The first FT-NIR chemical, microscopic images of biological systems approaching one micron resolution are here reported. Chemical images obtained by FT-NIR and FT-IR Microspectroscopy are also presented for oil in soybean seeds and somatic embryos under physiological conditions. FT-NIR spectra of oil and proteins were obtained for volumes as small as two cubic microns. Related, HR-NMR analyses of oil contents in somatic embryos as well as 99% accurate calibrations are also presented here with nanoliter precision. Such high-resolution, 400 MHz H-1 NMR analyses allowed the selection of mutagenized embryos with higher oil content (e.g. >~20%) compared to the average levels in non-mutagenized control embryos. Moreover, developmental changes in single soybean seeds and/or somatic embryos may be monito...

Automated magnification calibration in transmission electron microscopy using Fourier analysis of replica images

International Nuclear Information System (INIS)

Laak, Jeroen A.W.M. van der; Dijkman, Henry B.P.M.; Pahlplatz, Martin M.M.

2006-01-01

The magnification factor in transmission electron microscopy is not very precise, hampering for instance quantitative analysis of specimens. Calibration of the magnification is usually performed interactively using replica specimens, containing line or grating patterns with known spacing. In the present study, a procedure is described for automated magnification calibration using digital images of a line replica. This procedure is based on analysis of the power spectrum of Fourier transformed replica images, and is compared to interactive measurement in the same images. Images were used with magnification ranging from 1,000x to 200,000x. The automated procedure deviated on average 0.10% from interactive measurements. Especially for catalase replicas, the coefficient of variation of automated measurement was considerably smaller (average 0.28%) compared to that of interactive measurement (average 3.5%). In conclusion, calibration of the magnification in digital images from transmission electron microscopy may be performed automatically, using the procedure presented here, with high precision and accuracy

Automated cell counts on CSF samples: A multicenter performance evaluation of the GloCyte system.

Science.gov (United States)

Hod, E A; Brugnara, C; Pilichowska, M; Sandhaus, L M; Luu, H S; Forest, S K; Netterwald, J C; Reynafarje, G M; Kratz, A

2018-02-01

Automated cell counters have replaced manual enumeration of cells in blood and most body fluids. However, due to the unreliability of automated methods at very low cell counts, most laboratories continue to perform labor-intensive manual counts on many or all cerebrospinal fluid (CSF) samples. This multicenter clinical trial investigated if the GloCyte System (Advanced Instruments, Norwood, MA), a recently FDA-approved automated cell counter, which concentrates and enumerates red blood cells (RBCs) and total nucleated cells (TNCs), is sufficiently accurate and precise at very low cell counts to replace all manual CSF counts. The GloCyte System concentrates CSF and stains RBCs with fluorochrome-labeled antibodies and TNCs with nucleic acid dyes. RBCs and TNCs are then counted by digital image analysis. Residual adult and pediatric CSF samples obtained for clinical analysis at five different medical centers were used for the study. Cell counts were performed by the manual hemocytometer method and with the GloCyte System following the same protocol at all sites. The limits of the blank, detection, and quantitation, as well as precision and accuracy of the GloCyte, were determined. The GloCyte detected as few as 1 TNC/μL and 1 RBC/μL, and reliably counted as low as 3 TNCs/μL and 2 RBCs/μL. The total coefficient of variation was less than 20%. Comparison with cell counts obtained with a hemocytometer showed good correlation (>97%) between the GloCyte and the hemocytometer, including at very low cell counts. The GloCyte instrument is a precise, accurate, and stable system to obtain red cell and nucleated cell counts in CSF samples. It allows for the automated enumeration of even very low cell numbers, which is crucial for CSF analysis. These results suggest that GloCyte is an acceptable alternative to the manual method for all CSF samples, including those with normal cell counts. © 2017 John Wiley & Sons Ltd.

Automated detection of fundus photographic red lesions in diabetic retinopathy.

Science.gov (United States)

Larsen, Michael; Godt, Jannik; Larsen, Nicolai; Lund-Andersen, Henrik; Sjølie, Anne Katrin; Agardh, Elisabet; Kalm, Helle; Grunkin, Michael; Owens, David R

2003-02-01

To compare a fundus image-analysis algorithm for automated detection of hemorrhages and microaneurysms with visual detection of retinopathy in patients with diabetes. Four hundred fundus photographs (35-mm color transparencies) were obtained in 200 eyes of 100 patients with diabetes who were randomly selected from the Welsh Community Diabetic Retinopathy Study. A gold standard reference was defined by classifying each patient as having or not having diabetic retinopathy based on overall visual grading of the digitized transparencies. A single-lesion visual grading was made independently, comprising meticulous outlining of all single lesions in all photographs and used to develop the automated red lesion detection system. A comparison of visual and automated single-lesion detection in replicating the overall visual grading was then performed. Automated red lesion detection demonstrated a specificity of 71.4% and a resulting sensitivity of 96.7% in detecting diabetic retinopathy when applied at a tentative threshold setting for use in diabetic retinopathy screening. The accuracy of 79% could be raised to 85% by adjustment of a single user-supplied parameter determining the balance between the screening priorities, for which a considerable range of options was demonstrated by the receiver-operating characteristic (area under the curve 90.3%). The agreement of automated lesion detection with overall visual grading (0.659) was comparable to the mean agreement of six ophthalmologists (0.648). Detection of diabetic retinopathy by automated detection of single fundus lesions can be achieved with a performance comparable to that of experienced ophthalmologists. The results warrant further investigation of automated fundus image analysis as a tool for diabetic retinopathy screening.

Protein Expression Analyses at the Single Cell Level

Directory of Open Access Journals (Sweden)

Masae Ohno

2014-09-01

Full Text Available The central dogma of molecular biology explains how genetic information is converted into its end product, proteins, which are responsible for the phenotypic state of the cell. Along with the protein type, the phenotypic state depends on the protein copy number. Therefore, quantification of the protein expression in a single cell is critical for quantitative characterization of the phenotypic states. Protein expression is typically a dynamic and stochastic phenomenon that cannot be well described by standard experimental methods. As an alternative, fluorescence imaging is being explored for the study of protein expression, because of its high sensitivity and high throughput. Here we review key recent progresses in fluorescence imaging-based methods and discuss their application to proteome analysis at the single cell level.

Chimenea and other tools: Automated imaging of multi-epoch radio-synthesis data with CASA

Science.gov (United States)

Staley, T. D.; Anderson, G. E.

2015-11-01

In preparing the way for the Square Kilometre Array and its pathfinders, there is a pressing need to begin probing the transient sky in a fully robotic fashion using the current generation of radio telescopes. Effective exploitation of such surveys requires a largely automated data-reduction process. This paper introduces an end-to-end automated reduction pipeline, AMIsurvey, used for calibrating and imaging data from the Arcminute Microkelvin Imager Large Array. AMIsurvey makes use of several component libraries which have been packaged separately for open-source release. The most scientifically significant of these is chimenea, which implements a telescope-agnostic algorithm for automated imaging of pre-calibrated multi-epoch radio-synthesis data, of the sort typically acquired for transient surveys or follow-up. The algorithm aims to improve upon standard imaging pipelines by utilizing iterative RMS-estimation and automated source-detection to avoid so called 'Clean-bias', and makes use of CASA subroutines for the underlying image-synthesis operations. At a lower level, AMIsurvey relies upon two libraries, drive-ami and drive-casa, built to allow use of mature radio-astronomy software packages from within Python scripts. While targeted at automated imaging, the drive-casa interface can also be used to automate interaction with any of the CASA subroutines from a generic Python process. Additionally, these packages may be of wider technical interest beyond radio-astronomy, since they demonstrate use of the Python library pexpect to emulate terminal interaction with an external process. This approach allows for rapid development of a Python interface to any legacy or externally-maintained pipeline which accepts command-line input, without requiring alterations to the original code.

Automated Identification and Localization of Hematopoietic Stem Cells in 3D Intravital Microscopy Data

Directory of Open Access Journals (Sweden)

Reema A. Khorshed

2015-07-01

Full Text Available Measuring three-dimensional (3D localization of hematopoietic stem cells (HSCs within the bone marrow microenvironment using intravital microscopy is a rapidly expanding research theme. This approach holds the key to understanding the detail of HSC-niche interactions, which are critical for appropriate stem cell function. Due to the complex tissue architecture of the bone marrow and to the progressive introduction of scattering and signal loss at increasing imaging depths, there is no ready-made software to handle efficient segmentation and unbiased analysis of the data. To address this, we developed an automated image analysis tool that simplifies and standardizes the biological interpretation of 3D HSC microenvironment images. The algorithm identifies HSCs and measures their localization relative to surrounding osteoblast cells and bone collagen. We demonstrate here the effectiveness, consistency, and accuracy of the proposed approach compared to current manual analysis and its wider applicability to analyze other 3D bone marrow components.

Inertial Microfluidic Cell Stretcher (iMCS): Fully Automated, High-Throughput, and Near Real-Time Cell Mechanotyping.

Science.gov (United States)

Deng, Yanxiang; Davis, Steven P; Yang, Fan; Paulsen, Kevin S; Kumar, Maneesh; Sinnott DeVaux, Rebecca; Wang, Xianhui; Conklin, Douglas S; Oberai, Assad; Herschkowitz, Jason I; Chung, Aram J

2017-07-01

Mechanical biomarkers associated with cytoskeletal structures have been reported as powerful label-free cell state identifiers. In order to measure cell mechanical properties, traditional biophysical (e.g., atomic force microscopy, micropipette aspiration, optical stretchers) and microfluidic approaches were mainly employed; however, they critically suffer from low-throughput, low-sensitivity, and/or time-consuming and labor-intensive processes, not allowing techniques to be practically used for cell biology research applications. Here, a novel inertial microfluidic cell stretcher (iMCS) capable of characterizing large populations of single-cell deformability near real-time is presented. The platform inertially controls cell positions in microchannels and deforms cells upon collision at a T-junction with large strain. The cell elongation motions are recorded, and thousands of cell deformability information is visualized near real-time similar to traditional flow cytometry. With a full automation, the entire cell mechanotyping process runs without any human intervention, realizing a user friendly and robust operation. Through iMCS, distinct cell stiffness changes in breast cancer progression and epithelial mesenchymal transition are reported, and the use of the platform for rapid cancer drug discovery is shown as well. The platform returns large populations of single-cell quantitative mechanical properties (e.g., shear modulus) on-the-fly with high statistical significances, enabling actual usages in clinical and biophysical studies. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Impact of image segmentation on high-content screening data quality for SK-BR-3 cells

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Li Yizheng

2007-09-01

Full Text Available Abstract Background High content screening (HCS is a powerful method for the exploration of cellular signalling and morphology that is rapidly being adopted in cancer research. HCS uses automated microscopy to collect images of cultured cells. The images are subjected to segmentation algorithms to identify cellular structures and quantitate their morphology, for hundreds to millions of individual cells. However, image analysis may be imperfect, especially for "HCS-unfriendly" cell lines whose morphology is not well handled by current image segmentation algorithms. We asked if segmentation errors were common for a clinically relevant cell line, if such errors had measurable effects on the data, and if HCS data could be improved by automated identification of well-segmented cells. Results Cases of poor cell body segmentation occurred frequently for the SK-BR-3 cell line. We trained classifiers to identify SK-BR-3 cells that were well segmented. On an independent test set created by human review of cell images, our optimal support-vector machine classifier identified well-segmented cells with 81% accuracy. The dose responses of morphological features were measurably different in well- and poorly-segmented populations. Elimination of the poorly-segmented cell population increased the purity of DNA content distributions, while appropriately retaining biological heterogeneity, and simultaneously increasing our ability to resolve specific morphological changes in perturbed cells. Conclusion Image segmentation has a measurable impact on HCS data. The application of a multivariate shape-based filter to identify well-segmented cells improved HCS data quality for an HCS-unfriendly cell line, and could be a valuable post-processing step for some HCS datasets.

An automated live imaging platform for studying merozoite egress-invasion in malaria cultures.

Science.gov (United States)

Crick, Alex J; Tiffert, Teresa; Shah, Sheel M; Kotar, Jurij; Lew, Virgilio L; Cicuta, Pietro

2013-03-05

Most cases of severe and fatal malaria are caused by the intraerythrocytic asexual reproduction cycle of Plasmodium falciparum. One of the most intriguing and least understood stages in this cycle is the brief preinvasion period during which dynamic merozoite-red-cell interactions align the merozoite apex in preparation for penetration. Studies of the molecular mechanisms involved in this process face formidable technical challenges, requiring multiple observations of merozoite egress-invasion sequences in live cultures under controlled experimental conditions, using high-resolution microscopy and a variety of fluorescent imaging tools. Here we describe a first successful step in the development of a fully automated, robotic imaging platform to enable such studies. Schizont-enriched live cultures of P. falciparum were set up on an inverted stage microscope with software-controlled motorized functions. By applying a variety of imaging filters and selection criteria, we identified infected red cells that were likely to rupture imminently, and recorded their coordinates. We developed a video-image analysis to detect and automatically record merozoite egress events in 100% of the 40 egress-invasion sequences recorded in this study. We observed a substantial polymorphism of the dynamic condition of pre-egress infected cells, probably reflecting asynchronies in the diversity of confluent processes leading to merozoite release. Copyright © 2013 Biophysical Society. Published by Elsevier Inc. All rights reserved.

Automated local bright feature image analysis of nuclear protein distribution identifies changes in tissue phenotype

International Nuclear Information System (INIS)

Knowles, David; Sudar, Damir; Bator, Carol; Bissell, Mina

2006-01-01

The organization of nuclear proteins is linked to cell and tissue phenotypes. When cells arrest proliferation, undergo apoptosis, or differentiate, the distribution of nuclear proteins changes. Conversely, forced alteration of the distribution of nuclear proteins modifies cell phenotype. Immunostaining and fluorescence microscopy have been critical for such findings. However, there is an increasing need for quantitative analysis of nuclear protein distribution to decipher epigenetic relationships between nuclear structure and cell phenotype, and to unravel the mechanisms linking nuclear structure and function. We have developed imaging methods to quantify the distribution of fluorescently-stained nuclear protein NuMA in different mammary phenotypes obtained using three-dimensional cell culture. Automated image segmentation of DAPI-stained nuclei was generated to isolate thousands of nuclei from three-dimensional confocal images. Prominent features of fluorescently-stained NuMA were detected using a novel local bright feature analysis technique, and their normalized spatial density calculated as a function of the distance from the nuclear perimeter to its center. The results revealed marked changes in the distribution of the density of NuMA bright features as non-neoplastic cells underwent phenotypically normal acinar morphogenesis. In contrast, we did not detect any reorganization of NuMA during the formation of tumor nodules by malignant cells. Importantly, the analysis also discriminated proliferating non-neoplastic cells from proliferating malignant cells, suggesting that these imaging methods are capable of identifying alterations linked not only to the proliferation status but also to the malignant character of cells. We believe that this quantitative analysis will have additional applications for classifying normal and pathological tissues

Automated, feature-based image alignment for high-resolution imaging mass spectrometry of large biological samples

NARCIS (Netherlands)

Broersen, A.; Liere, van R.; Altelaar, A.F.M.; Heeren, R.M.A.; McDonnell, L.A.

2008-01-01

High-resolution imaging mass spectrometry of large biological samples is the goal of several research groups. In mosaic imaging, the most common method, the large sample is divided into a mosaic of small areas that are then analyzed with high resolution. Here we present an automated alignment

Fully Automated On-Chip Imaging Flow Cytometry System with Disposable Contamination-Free Plastic Re-Cultivation Chip

Directory of Open Access Journals (Sweden)

Tomoyuki Kaneko

2011-06-01

Full Text Available We have developed a novel imaging cytometry system using a poly(methyl methacrylate (PMMA based microfluidic chip. The system was contamination-free, because sample suspensions contacted only with a flammable PMMA chip and no other component of the system. The transparency and low-fluorescence of PMMA was suitable for microscopic imaging of cells flowing through microchannels on the chip. Sample particles flowing through microchannels on the chip were discriminated by an image-recognition unit with a high-speed camera in real time at the rate of 200 event/s, e.g., microparticles 2.5 μm and 3.0 μm in diameter were differentiated with an error rate of less than 2%. Desired cells were separated automatically from other cells by electrophoretic or dielectrophoretic force one by one with a separation efficiency of 90%. Cells in suspension with fluorescent dye were separated using the same kind of microfluidic chip. Sample of 5 μL with 1 × 106 particle/mL was processed within 40 min. Separated cells could be cultured on the microfluidic chip without contamination. The whole operation of sample handling was automated using 3D micropipetting system. These results showed that the novel imaging flow cytometry system is practically applicable for biological research and clinical diagnostics.

Partial Red Blood Cell Exchange in Children and Young Patients with Sickle Cell Disease: Manual Versus Automated Procedure.

Science.gov (United States)

Escobar, Carlos; Moniz, Marta; Nunes, Pedro; Abadesso, Clara; Ferreira, Teresa; Barra, António; Lichtner, Anabela; Loureiro, Helena; Dias, Alexandra; Almeida, Helena

2017-10-31

The benefits of manual versus automated red blood cell exchange have rarely been documented and studies in young sickle cell disease patients are scarce. We aim to describe and compare our experience in these two procedures. Young patients (≤ 21 years old) who underwent manual- or automated-red blood cell exchange for prevention or treatment of sickle cell disease complications were included. Clinical, technical and hematological data were prospectively recorded and analyzed. Ninety-four red blood cell exchange sessions were performed over a period of 68 months, including 57 manual and 37 automated, 63 for chronic complications prevention, 30 for acute complications and one in the pre-operative setting. Mean decrease in sickle hemoglobin levels was higher in automated-red blood cell exchange (p exchange and access alarm on automated-red blood cell exchange. No major complication or alloimunization was recorded. Automated-red blood cell exchange decreased sickle hemoglobin levels more efficiently than manual procedure in the setting of acute and chronic complications of sickle cell disease, with minor technical concerns mainly due to vascular access. The threshold of sickle hemoglobin should be individualized for clinical and hematological goals. In our cohort of young patients, the need for an acceptable venous access was a limiting factor, but iron-overload was avoided. Automated red blood cell exchange is safe and well tolerated. It permits a higher sickle hemoglobin removal efficacy, better volume status control and iron-overload avoidance.

Single Molecule and Nanoparticle Imaging in Biophysical, Surface, and Photocatalysis Studies

Energy Technology Data Exchange (ETDEWEB)

Ha, Ji Won [Iowa State Univ., Ames, IA (United States)

2013-01-01

A differential interference contrast (DIC) polarization anisotropy is reported that was successfully used for rotational tracking of gold nanorods attached onto a kinesin-driven microtubule. A dual-wavelength detection of single gold nanorods rotating on a live cell membrane is described. Both transverse and longitudinal surface plasmon resonance (SPR) modes were used for tracking the rotational motions during a fast dynamic process under a DIC microscope. A novel method is presented to determine the full three-dimensional (3D) orientation of single plasmonic gold nanorods rotating on live cell membranes by combining DIC polarization anisotropy with an image pattern recognition technique. Polarization- and wavelength-sensitive DIC microscopy imaging of 2- m long gold nanowires as optical probes in biological studies is reported. A new method is demonstrated to track 3D orientation of single gold nanorods supported on a gold film without angular degeneracy. The idea is to use the interaction (or coupling) of gold nanorods with gold film, yielding characteristic scattering patterns such as a doughnut shape. Imaging of photocatalytic activity, polarity and selectivity on single Au-CdS hybrid nanocatalysts using a high-resolution superlocalization fluorescence imaging technique is described.

Automated striatal uptake analysis of 18F-FDOPA PET images applied to Parkinson's disease patients

International Nuclear Information System (INIS)

Chang Icheng; Lue Kunhan; Hsieh Hungjen; Liu Shuhsin; Kao, Chinhao K.

2011-01-01

6-[ 18 F]Fluoro-L-DOPA (FDOPA) is a radiopharmaceutical valuable for assessing the presynaptic dopaminergic function when used with positron emission tomography (PET). More specifically, the striatal-to-occipital ratio (SOR) of FDOPA uptake images has been extensively used as a quantitative parameter in these PET studies. Our aim was to develop an easy, automated method capable of performing objective analysis of SOR in FDOPA PET images of Parkinson's disease (PD) patients. Brain images from FDOPA PET studies of 21 patients with PD and 6 healthy subjects were included in our automated striatal analyses. Images of each individual were spatially normalized into an FDOPA template. Subsequently, the image slice with the highest level of basal ganglia activity was chosen among the series of normalized images. Also, the immediate preceding and following slices of the chosen image were then selected. Finally, the summation of these three images was used to quantify and calculate the SOR values. The results obtained by automated analysis were compared with manual analysis by a trained and experienced image processing technologist. The SOR values obtained from the automated analysis had a good agreement and high correlation with manual analysis. The differences in caudate, putamen, and striatum were -0.023, -0.029, and -0.025, respectively; correlation coefficients 0.961, 0.957, and 0.972, respectively. We have successfully developed a method for automated striatal uptake analysis of FDOPA PET images. There was no significant difference between the SOR values obtained from this method and using manual analysis. Yet it is an unbiased time-saving and cost-effective program and easy to implement on a personal computer. (author)

A multivariate shape quantification approach for sickle red blood cell in patient-specific microscopy image data

Science.gov (United States)

Xu, Mengjia; Yang, Jinzhu; Zhao, Hong

2017-07-01

The morphological change of red blood cells(RBCs) plays an important role in revealing the biomechanical and biorheological characteristics of RBCs. Aiming to extract the shape indices for the sickle RBCs, an automated ex-vivo RBC shape quantification method is proposed. First, single RBC regions (ROIs) are extracted from raw microscopy image via an automatic hierarchical ROI extraction method. Second, an improved random walk method is used to detect the RBC outline. Finally, three types of RBC shape factors are calculated based on the elliptical fitting RBC contour. Experiments indicate that the proposed method can accurately segment the RBCs from the microscopy images with low contrast and prevent the disturbance of artifacts. Moreover, it can provide an efficient shape quantification means for diverse RBC shapes in a batch manner.

Bright photoactivatable fluorophores for single-molecule imaging.

Science.gov (United States)

Grimm, Jonathan B; English, Brian P; Choi, Heejun; Muthusamy, Anand K; Mehl, Brian P; Dong, Peng; Brown, Timothy A; Lippincott-Schwartz, Jennifer; Liu, Zhe; Lionnet, Timothée; Lavis, Luke D

2016-12-01

Small-molecule fluorophores are important tools for advanced imaging experiments. We previously reported a general method to improve small, cell-permeable fluorophores which resulted in the azetidine-containing 'Janelia Fluor' (JF) dyes. Here, we refine and extend the utility of these dyes by synthesizing photoactivatable derivatives that are compatible with live-cell labeling strategies. Once activated, these derived compounds retain the superior brightness and photostability of the JF dyes, enabling improved single-particle tracking and facile localization microscopy experiments.

Quantification of Eosinophilic Granule Protein Deposition in Biopsies of Inflammatory Skin Diseases by Automated Image Analysis of Highly Sensitive Immunostaining

Directory of Open Access Journals (Sweden)

Peter Kiehl

1999-01-01

Full Text Available Eosinophilic granulocytes are major effector cells in inflammation. Extracellular deposition of toxic eosinophilic granule proteins (EGPs, but not the presence of intact eosinophils, is crucial for their functional effect in situ. As even recent morphometric approaches to quantify the involvement of eosinophils in inflammation have been only based on cell counting, we developed a new method for the cell‐independent quantification of EGPs by image analysis of immunostaining. Highly sensitive, automated immunohistochemistry was done on paraffin sections of inflammatory skin diseases with 4 different primary antibodies against EGPs. Image analysis of immunostaining was performed by colour translation, linear combination and automated thresholding. Using strictly standardized protocols, the assay was proven to be specific and accurate concerning segmentation in 8916 fields of 520 sections, well reproducible in repeated measurements and reliable over 16 weeks observation time. The method may be valuable for the cell‐independent segmentation of immunostaining in other applications as well.

Automated analysis of high-content microscopy data with deep learning.

Science.gov (United States)

Kraus, Oren Z; Grys, Ben T; Ba, Jimmy; Chong, Yolanda; Frey, Brendan J; Boone, Charles; Andrews, Brenda J

2017-04-18

Existing computational pipelines for quantitative analysis of high-content microscopy data rely on traditional machine learning approaches that fail to accurately classify more than a single dataset without substantial tuning and training, requiring extensive analysis. Here, we demonstrate that the application of deep learning to biological image data can overcome the pitfalls associated with conventional machine learning classifiers. Using a deep convolutional neural network (DeepLoc) to analyze yeast cell images, we show improved performance over traditional approaches in the automated classification of protein subcellular localization. We also demonstrate the ability of DeepLoc to classify highly divergent image sets, including images of pheromone-arrested cells with abnormal cellular morphology, as well as images generated in different genetic backgrounds and in different laboratories. We offer an open-source implementation that enables updating DeepLoc on new microscopy datasets. This study highlights deep learning as an important tool for the expedited analysis of high-content microscopy data. © 2017 The Authors. Published under the terms of the CC BY 4.0 license.

Live Cell Imaging of Bacillus subtilis and Streptococcus pneumoniae using Automated Time-lapse Microscopy

NARCIS (Netherlands)

Jong, Imke G. de; Beilharz, Katrin; Kuipers, Oscar P.; Veening, Jan-Willem

2011-01-01

During the last few years scientists became increasingly aware that average data obtained from microbial population based experiments are not representative of the behavior, status or phenotype of single cells. Due to this new insight the number of single cell studies rises continuously. However,

Automated Quantification of Hematopoietic Cell – Stromal Cell Interactions in Histological Images of Undecalcified Bone

Science.gov (United States)

Zehentmeier, Sandra; Cseresnyes, Zoltan; Escribano Navarro, Juan; Niesner, Raluca A.; Hauser, Anja E.

2015-01-01

Confocal microscopy is the method of choice for the analysis of localization of multiple cell types within complex tissues such as the bone marrow. However, the analysis and quantification of cellular localization is difficult, as in many cases it relies on manual counting, thus bearing the risk of introducing a rater-dependent bias and reducing interrater reliability. Moreover, it is often difficult to judge whether the co-localization between two cells results from random positioning, especially when cell types differ strongly in the frequency of their occurrence. Here, a method for unbiased quantification of cellular co-localization in the bone marrow is introduced. The protocol describes the sample preparation used to obtain histological sections of whole murine long bones including the bone marrow, as well as the staining protocol and the acquisition of high-resolution images. An analysis workflow spanning from the recognition of hematopoietic and non-hematopoietic cell types in 2-dimensional (2D) bone marrow images to the quantification of the direct contacts between those cells is presented. This also includes a neighborhood analysis, to obtain information about the cellular microenvironment surrounding a certain cell type. In order to evaluate whether co-localization of two cell types is the mere result of random cell positioning or reflects preferential associations between the cells, a simulation tool which is suitable for testing this hypothesis in the case of hematopoietic as well as stromal cells, is used. This approach is not limited to the bone marrow, and can be extended to other tissues to permit reproducible, quantitative analysis of histological data. PMID:25938636

High-Speed Single Quantum Dot Imaging of Artificial Lipids in Live Cells Reveal Partial Hop Diffusion

DEFF Research Database (Denmark)

Lagerholm, B. Christoffer; Clausen, Mathias P.; Christensen, Eva Arnspang

2010-01-01

-81). These findings have yet to be independently confirmed. In this work, we show that high-speed single particle tracking with quantum dots(QDs)and using a standard wide-field fluorescence microscope and an EMCCD is possible at image acquisition rates of up to ~2000 Hz with an image integration time of ~0.5 msec....... The spatial precision in these experiments is ~40 nm (as determined from the standard deviation of repeated position measurements of an immobile QD on a cell). Using this system, we further show that an artificial lipid, biotin-cap-DPPE, inserted in a mouse embryo fibroblast (MEF), labeled with sAv-QD655...

Analyzing cell fate control by cytokines through continuous single cell biochemistry.

Science.gov (United States)

Rieger, Michael A; Schroeder, Timm

2009-10-01

Cytokines are important regulators of cell fates with high clinical and commercial relevance. However, despite decades of intense academic and industrial research, it proved surprisingly difficult to describe the biological functions of cytokines in a precise and comprehensive manner. The exact analysis of cytokine biology is complicated by the fact that individual cytokines control many different cell fates and activate a multitude of intracellular signaling pathways. Moreover, although activating different molecular programs, different cytokines can be redundant in their biological effects. In addition, cytokines with different biological effects can activate overlapping signaling pathways. This prospect article will outline the necessity of continuous single cell biochemistry to unravel the biological functions of molecular cytokine signaling. It focuses on potentials and limitations of recent technical developments in fluorescent time-lapse imaging and single cell tracking allowing constant long-term observation of molecules and behavior of single cells. (c) 2009 Wiley-Liss, Inc.

Automated multiscale morphometry of muscle disease from second harmonic generation microscopy using tensor-based image processing.

Science.gov (United States)

Garbe, Christoph S; Buttgereit, Andreas; Schürmann, Sebastian; Friedrich, Oliver

2012-01-01

Practically, all chronic diseases are characterized by tissue remodeling that alters organ and cellular function through changes to normal organ architecture. Some morphometric alterations become irreversible and account for disease progression even on cellular levels. Early diagnostics to categorize tissue alterations, as well as monitoring progression or remission of disturbed cytoarchitecture upon treatment in the same individual, are a new emerging field. They strongly challenge spatial resolution and require advanced imaging techniques and strategies for detecting morphological changes. We use a combined second harmonic generation (SHG) microscopy and automated image processing approach to quantify morphology in an animal model of inherited Duchenne muscular dystrophy (mdx mouse) with age. Multiphoton XYZ image stacks from tissue slices reveal vast morphological deviation in muscles from old mdx mice at different scales of cytoskeleton architecture: cell calibers are irregular, myofibrils within cells are twisted, and sarcomere lattice disruptions (detected as "verniers") are larger in number compared to samples from healthy mice. In young mdx mice, such alterations are only minor. The boundary-tensor approach, adapted and optimized for SHG data, is a suitable approach to allow quick quantitative morphometry in whole tissue slices. The overall detection performance of the automated algorithm compares very well with manual "by eye" detection, the latter being time consuming and prone to subjective errors. Our algorithm outperfoms manual detection by time with similar reliability. This approach will be an important prerequisite for the implementation of a clinical image databases to diagnose and monitor specific morphological alterations in chronic (muscle) diseases. © 2011 IEEE

Live Cell in Vitro and in Vivo Imaging Applications: Accelerating Drug Discovery

Directory of Open Access Journals (Sweden)

Neil O Carragher

2011-04-01

Full Text Available Dynamic regulation of specific molecular processes and cellular phenotypes in live cell systems reveal unique insights into cell fate and drug pharmacology that are not gained from traditional fixed endpoint assays. Recent advances in microscopic imaging platform technology combined with the development of novel optical biosensors and sophisticated image analysis solutions have increased the scope of live cell imaging applications in drug discovery. We highlight recent literature examples where live cell imaging has uncovered novel insight into biological mechanism or drug mode-of-action. We survey distinct types of optical biosensors and associated analytical methods for monitoring molecular dynamics, in vitro and in vivo. We describe the recent expansion of live cell imaging into automated target validation and drug screening activities through the development of dedicated brightfield and fluorescence kinetic imaging platforms. We provide specific examples of how temporal profiling of phenotypic response signatures using such kinetic imaging platforms can increase the value of in vitro high-content screening. Finally, we offer a prospective view of how further application and development of live cell imaging technology and reagents can accelerate preclinical lead optimization cycles and enhance the in vitro to in vivo translation of drug candidates.

Automated, non-linear registration between 3-dimensional brain map and medical head image

International Nuclear Information System (INIS)

Mizuta, Shinobu; Urayama, Shin-ichi; Zoroofi, R.A.; Uyama, Chikao

1998-01-01

In this paper, we propose an automated, non-linear registration method between 3-dimensional medical head image and brain map in order to efficiently extract the regions of interest. In our method, input 3-dimensional image is registered into a reference image extracted from a brain map. The problems to be solved are automated, non-linear image matching procedure, and cost function which represents the similarity between two images. Non-linear matching is carried out by dividing the input image into connected partial regions, transforming the partial regions preserving connectivity among the adjacent images, evaluating the image similarity between the transformed regions of the input image and the correspondent regions of the reference image, and iteratively searching the optimal transformation of the partial regions. In order to measure the voxelwise similarity of multi-modal images, a cost function is introduced, which is based on the mutual information. Some experiments using MR images presented the effectiveness of the proposed method. (author)

Automated quantification of proliferation with automated hot-spot selection in phosphohistone H3/MART1 dual-stained stage I/II melanoma.

Science.gov (United States)

Nielsen, Patricia Switten; Riber-Hansen, Rikke; Schmidt, Henrik; Steiniche, Torben

2016-04-09

Staging of melanoma includes quantification of a proliferation index, i.e., presumed melanocytic mitoses of H&E stains are counted manually in hot spots. Yet, its reproducibility and prognostic impact increases by immunohistochemical dual staining for phosphohistone H3 (PHH3) and MART1, which also may enable fully automated quantification by image analysis. To ensure manageable workloads and repeatable measurements in modern pathology, the study aimed to present an automated quantification of proliferation with automated hot-spot selection in PHH3/MART1-stained melanomas. Formalin-fixed, paraffin-embedded tissue from 153 consecutive stage I/II melanoma patients was immunohistochemically dual-stained for PHH3 and MART1. Whole slide images were captured, and the number of PHH3/MART1-positive cells was manually and automatically counted in the global tumor area and in a manually and automatically selected hot spot, i.e., a fixed 1-mm(2) square. Bland-Altman plots and hypothesis tests compared manual and automated procedures, and the Cox proportional hazards model established their prognostic impact. The mean difference between manual and automated global counts was 2.9 cells/mm(2) (P = 0.0071) and 0.23 cells per hot spot (P = 0.96) for automated counts in manually and automatically selected hot spots. In 77 % of cases, manual and automated hot spots overlapped. Fully manual hot-spot counts yielded the highest prognostic performance with an adjusted hazard ratio of 5.5 (95 % CI, 1.3-24, P = 0.024) as opposed to 1.3 (95 % CI, 0.61-2.9, P = 0.47) for automated counts with automated hot spots. The automated index and automated hot-spot selection were highly correlated to their manual counterpart, but altogether their prognostic impact was noticeably reduced. Because correct recognition of only one PHH3/MART1-positive cell seems important, extremely high sensitivity and specificity of the algorithm is required for prognostic purposes. Thus, automated

Automatic Blastomere Recognition from a Single Embryo Image

Directory of Open Access Journals (Sweden)

Yun Tian

2014-01-01

Full Text Available The number of blastomeres of human day 3 embryos is one of the most important criteria for evaluating embryo viability. However, due to the transparency and overlap of blastomeres, it is a challenge to recognize blastomeres automatically using a single embryo image. This study proposes an approach based on least square curve fitting (LSCF for automatic blastomere recognition from a single image. First, combining edge detection, deletion of multiple connected points, and dilation and erosion, an effective preprocessing method was designed to obtain part of blastomere edges that were singly connected. Next, an automatic recognition method for blastomeres was proposed using least square circle fitting. This algorithm was tested on 381 embryo microscopic images obtained from the eight-cell period, and the results were compared with those provided by experts. Embryos were recognized with a 0 error rate occupancy of 21.59%, and the ratio of embryos in which the false recognition number was less than or equal to 2 was 83.16%. This experiment demonstrated that our method could efficiently and rapidly recognize the number of blastomeres from a single embryo image without the need to reconstruct the three-dimensional model of the blastomeres first; this method is simple and efficient.

Development of image analysis software for quantification of viable cells in microchips.

Science.gov (United States)

Georg, Maximilian; Fernández-Cabada, Tamara; Bourguignon, Natalia; Karp, Paola; Peñaherrera, Ana B; Helguera, Gustavo; Lerner, Betiana; Pérez, Maximiliano S; Mertelsmann, Roland

2018-01-01

Over the past few years, image analysis has emerged as a powerful tool for analyzing various cell biology parameters in an unprecedented and highly specific manner. The amount of data that is generated requires automated methods for the processing and analysis of all the resulting information. The software available so far are suitable for the processing of fluorescence and phase contrast images, but often do not provide good results from transmission light microscopy images, due to the intrinsic variation of the acquisition of images technique itself (adjustment of brightness / contrast, for instance) and the variability between image acquisition introduced by operators / equipment. In this contribution, it has been presented an image processing software, Python based image analysis for cell growth (PIACG), that is able to calculate the total area of the well occupied by cells with fusiform and rounded morphology in response to different concentrations of fetal bovine serum in microfluidic chips, from microscopy images in transmission light, in a highly efficient way.

Single cell electroporation for longitudinal imaging of synaptic structure and function in the adult mouse neocortex in vivo

Directory of Open Access Journals (Sweden)

Stephane ePages

2015-04-01

Full Text Available Longitudinal imaging studies of neuronal structures in vivo have revealed rich dynamics in dendritic spines and axonal boutons. Spines and boutons are considered to be proxies for synapses. This implies that synapses display similar dynamics. However, spines and boutons do not always bear synapses, some may contain more than one, and dendritic shaft synapses have no clear structural proxies. In addition, synaptic strength is not always accurately revealed by just the size of these structures. Structural and functional dynamics of synapses could be studied more reliably using fluorescent synaptic proteins as markers for size and function. These proteins are often large and possibly interfere with circuit development, which renders them less suitable for conventional transfection or transgenesis methods such as viral vectors, in utero electroporation and germline transgenesis. Single cell electroporation has been shown to be a potential alternative for transfection of recombinant fluorescent proteins in adult cortical neurons. Here we provide proof of principle for the use of single cell electroporation to express and subsequently image fluorescently tagged synaptic proteins over days to weeks in vivo.

SU-E-I-94: Automated Image Quality Assessment of Radiographic Systems Using An Anthropomorphic Phantom

International Nuclear Information System (INIS)

Wells, J; Wilson, J; Zhang, Y; Samei, E; Ravin, Carl E.

2014-01-01

Purpose: In a large, academic medical center, consistent radiographic imaging performance is difficult to routinely monitor and maintain, especially for a fleet consisting of multiple vendors, models, software versions, and numerous imaging protocols. Thus, an automated image quality control methodology has been implemented using routine image quality assessment with a physical, stylized anthropomorphic chest phantom. Methods: The “Duke” Phantom (Digital Phantom 07-646, Supertech, Elkhart, IN) was imaged twice on each of 13 radiographic units from a variety of vendors at 13 primary care clinics. The first acquisition used the clinical PA chest protocol to acquire the post-processed “FOR PRESENTATION” image. The second image was acquired without an antiscatter grid followed by collection of the “FOR PROCESSING” image. Manual CNR measurements were made from the largest and thickest contrast-detail inserts in the lung, heart, and abdominal regions of the phantom in each image. An automated image registration algorithm was used to estimate the CNR of the same insert using similar ROIs. Automated measurements were then compared to the manual measurements. Results: Automatic and manual CNR measurements obtained from “FOR PRESENTATION” images had average percent differences of 0.42%±5.18%, −3.44%±4.85%, and 1.04%±3.15% in the lung, heart, and abdominal regions, respectively; measurements obtained from “FOR PROCESSING” images had average percent differences of -0.63%±6.66%, −0.97%±3.92%, and −0.53%±4.18%, respectively. The maximum absolute difference in CNR was 15.78%, 10.89%, and 8.73% in the respective regions. In addition to CNR assessment of the largest and thickest contrast-detail inserts, the automated method also provided CNR estimates for all 75 contrast-detail inserts in each phantom image. Conclusion: Automated analysis of a radiographic phantom has been shown to be a fast, robust, and objective means for assessing radiographic

Towards automated diffraction tomography. Part II-Cell parameter determination

International Nuclear Information System (INIS)

Kolb, U.; Gorelik, T.; Otten, M.T.

2008-01-01

Automated diffraction tomography (ADT) allows the collection of three-dimensional (3d) diffraction data sets from crystals down to a size of only few nanometres. Imaging is done in STEM mode, and diffraction data are collected with quasi-parallel beam nanoelectron diffraction (NED). Here, we present a set of developed processing steps necessary for automatic unit-cell parameter determination from the collected 3d diffraction data. Cell parameter determination is done via extraction of peak positions from a recorded data set (called the data reduction path) followed by subsequent cluster analysis of difference vectors. The procedure of lattice parameter determination is presented in detail for a beam-sensitive organic material. Independently, we demonstrate a potential (called the full integration path) based on 3d reconstruction of the reciprocal space visualising special structural features of materials such as partial disorder. Furthermore, we describe new features implemented into the acquisition part

Dissecting Transcriptional Heterogeneity in Pluripotency: Single Cell Analysis of Mouse Embryonic Stem Cells.

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Guedes, Ana M V; Henrique, Domingos; Abranches, Elsa

2016-01-01

Mouse Embryonic Stem cells (mESCs) show heterogeneous and dynamic expression of important pluripotency regulatory factors. Single-cell analysis has revealed the existence of cell-to-cell variability in the expression of individual genes in mESCs. Understanding how these heterogeneities are regulated and what their functional consequences are is crucial to obtain a more comprehensive view of the pluripotent state.In this chapter we describe how to analyze transcriptional heterogeneity by monitoring gene expression of Nanog, Oct4, and Sox2, using single-molecule RNA FISH in single mESCs grown in different cell culture medium. We describe in detail all the steps involved in the protocol, from RNA detection to image acquisition and processing, as well as exploratory data analysis.

78 FR 53466 - Modification of Two National Customs Automation Program (NCAP) Tests Concerning Automated...

Science.gov (United States)

2013-08-29

... Customs Automation Program (NCAP) Tests Concerning Automated Commercial Environment (ACE) Document Image... National Customs Automation Program (NCAP) tests concerning document imaging, known as the Document Image... the National Customs Automation Program (NCAP) tests concerning document imaging, known as the...

Digital transplantation pathology: combining whole slide imaging, multiplex staining and automated image analysis.

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Isse, K; Lesniak, A; Grama, K; Roysam, B; Minervini, M I; Demetris, A J

2012-01-01

Conventional histopathology is the gold standard for allograft monitoring, but its value proposition is increasingly questioned. "-Omics" analysis of tissues, peripheral blood and fluids and targeted serologic studies provide mechanistic insights into allograft injury not currently provided by conventional histology. Microscopic biopsy analysis, however, provides valuable and unique information: (a) spatial-temporal relationships; (b) rare events/cells; (c) complex structural context; and (d) integration into a "systems" model. Nevertheless, except for immunostaining, no transformative advancements have "modernized" routine microscopy in over 100 years. Pathologists now team with hardware and software engineers to exploit remarkable developments in digital imaging, nanoparticle multiplex staining, and computational image analysis software to bridge the traditional histology-global "-omic" analyses gap. Included are side-by-side comparisons, objective biopsy finding quantification, multiplexing, automated image analysis, and electronic data and resource sharing. Current utilization for teaching, quality assurance, conferencing, consultations, research and clinical trials is evolving toward implementation for low-volume, high-complexity clinical services like transplantation pathology. Cost, complexities of implementation, fluid/evolving standards, and unsettled medical/legal and regulatory issues remain as challenges. Regardless, challenges will be overcome and these technologies will enable transplant pathologists to increase information extraction from tissue specimens and contribute to cross-platform biomarker discovery for improved outcomes. ©Copyright 2011 The American Society of Transplantation and the American Society of Transplant Surgeons.

Segmentation and classification of cell cycle phases in fluorescence imaging.

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Ersoy, Ilker; Bunyak, Filiz; Chagin, Vadim; Cardoso, M Christina; Palaniappan, Kannappan

2009-01-01

Current chemical biology methods for studying spatiotemporal correlation between biochemical networks and cell cycle phase progression in live-cells typically use fluorescence-based imaging of fusion proteins. Stable cell lines expressing fluorescently tagged protein GFP-PCNA produce rich, dynamically varying sub-cellular foci patterns characterizing the cell cycle phases, including the progress during the S-phase. Variable fluorescence patterns, drastic changes in SNR, shape and position changes and abundance of touching cells require sophisticated algorithms for reliable automatic segmentation and cell cycle classification. We extend the recently proposed graph partitioning active contours (GPAC) for fluorescence-based nucleus segmentation using regional density functions and dramatically improve its efficiency, making it scalable for high content microscopy imaging. We utilize surface shape properties of GFP-PCNA intensity field to obtain descriptors of foci patterns and perform automated cell cycle phase classification, and give quantitative performance by comparing our results to manually labeled data.

Automated sub-5 nm image registration in integrated correlative fluorescence and electron microscopy using cathodoluminescence pointers

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Haring, Martijn T.; Liv, Nalan; Zonnevylle, A. Christiaan; Narvaez, Angela C.; Voortman, Lenard M.; Kruit, Pieter; Hoogenboom, Jacob P.

2017-03-01

In the biological sciences, data from fluorescence and electron microscopy is correlated to allow fluorescence biomolecule identification within the cellular ultrastructure and/or ultrastructural analysis following live-cell imaging. High-accuracy (sub-100 nm) image overlay requires the addition of fiducial markers, which makes overlay accuracy dependent on the number of fiducials present in the region of interest. Here, we report an automated method for light-electron image overlay at high accuracy, i.e. below 5 nm. Our method relies on direct visualization of the electron beam position in the fluorescence detection channel using cathodoluminescence pointers. We show that image overlay using cathodoluminescence pointers corrects for image distortions, is independent of user interpretation, and does not require fiducials, allowing image correlation with molecular precision anywhere on a sample.

Spatial and temporal single-cell volume estimation by a fluorescence imaging technique with application to astrocytes in primary culture

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Khatibi, Siamak; Allansson, Louise; Gustavsson, Tomas; Blomstrand, Fredrik; Hansson, Elisabeth; Olsson, Torsten

1999-05-01

Cell volume changes are often associated with important physiological and pathological processes in the cell. These changes may be the means by which the cell interacts with its surrounding. Astroglial cells change their volume and shape under several circumstances that affect the central nervous system. Following an incidence of brain damage, such as a stroke or a traumatic brain injury, one of the first events seen is swelling of the astroglial cells. In order to study this and other similar phenomena, it is desirable to develop technical instrumentation and analysis methods capable of detecting and characterizing dynamic cell shape changes in a quantitative and robust way. We have developed a technique to monitor and to quantify the spatial and temporal volume changes in a single cell in primary culture. The technique is based on two- and three-dimensional fluorescence imaging. The temporal information is obtained from a sequence of microscope images, which are analyzed in real time. The spatial data is collected in a sequence of images from the microscope, which is automatically focused up and down through the specimen. The analysis of spatial data is performed off-line and consists of photobleaching compensation, focus restoration, filtering, segmentation and spatial volume estimation.

An objective method to optimize the MR sequence set for plaque classification in carotid vessel wall images using automated image segmentation.

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Ronald van 't Klooster

Full Text Available A typical MR imaging protocol to study the status of atherosclerosis in the carotid artery consists of the application of multiple MR sequences. Since scanner time is limited, a balance has to be reached between the duration of the applied MR protocol and the quantity and quality of the resulting images which are needed to assess the disease. In this study an objective method to optimize the MR sequence set for classification of soft plaque in vessel wall images of the carotid artery using automated image segmentation was developed. The automated method employs statistical pattern recognition techniques and was developed based on an extensive set of MR contrast weightings and corresponding manual segmentations of the vessel wall and soft plaque components, which were validated by histological sections. Evaluation of the results from nine contrast weightings showed the tradeoff between scan duration and automated image segmentation performance. For our dataset the best segmentation performance was achieved by selecting five contrast weightings. Similar performance was achieved with a set of three contrast weightings, which resulted in a reduction of scan time by more than 60%. The presented approach can help others to optimize MR imaging protocols by investigating the tradeoff between scan duration and automated image segmentation performance possibly leading to shorter scanning times and better image interpretation. This approach can potentially also be applied to other research fields focusing on different diseases and anatomical regions.

Micro-PIXE for the quantitative imaging of chemical elements in single cells

International Nuclear Information System (INIS)

Ortega, R.

2013-01-01

Full text: The knowledge of the intracellular distribution of biological relevant metals is important to understand their mechanisms of action in cells, either for physiological, toxicological or pathological processes. However, the direct detection of trace metals in single cells is a challenging task that requires sophisticated analytical developments. The aim of this seminar will be to present the recent achievements in this field using micro-PIXE analysis. The combination of micro-PIXE with RBS (Rutherford Backscattering Spectrometry) and STIM (Scanning Transmission lon Microscopy) allows the quantitative determination of trace metal content within sub-cellular compartments. The application of STlM analysis will be more specifically highlighted as it provides high spatial resolution imaging (<200 nm) and excellent mass sensitivity (<0.1 ng). Application of the STIM-PIXE-RBS methodology is absolutely needed when organic mass loss appears during PIXE-RBS irradiation. This combination of STIM-PIXE-RBS provides fully quantitative determination of trace element content, expressed in μg/g, which is a quite unique capability for micro-PIXE compared to other micro-analytical methods such as the electron and synchrotron X-ray fluorescence or the techniques based on mass spectrometry. Examples of micro-PIXE studies for subcellular imaging of trace elements in the various fields of interest will be presented such as metal-based toxicology, pharmacology, and neuro degeneration [1] R. Ortega, G. Devés, A. Carmona. J. R. Soc. Interface, 6, (2009) S649-S658. (author)

Clinical utility of an automated instrument for gram staining single slides.

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Baron, Ellen Jo; Mix, Samantha; Moradi, Wais

2010-06-01

Gram stains of 87 different clinical samples were prepared by the laboratory's conventional methods (automated or manual) and by a new single-slide-type automated staining instrument, GG&B AGS-1000. Gram stains from either heat- or methanol-fixed slides stained with the new instrument were easy to interpret, and results were essentially the same as those from the methanol-fixed slides prepared as a part of the routine workflow. This instrument is well suited to a rapid-response laboratory where Gram stain requests are commonly received on a stat basis.

Information management for high content live cell imaging

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White Michael RH

2009-07-01

Full Text Available Abstract Background High content live cell imaging experiments are able to track the cellular localisation of labelled proteins in multiple live cells over a time course. Experiments using high content live cell imaging will generate multiple large datasets that are often stored in an ad-hoc manner. This hinders identification of previously gathered data that may be relevant to current analyses. Whilst solutions exist for managing image data, they are primarily concerned with storage and retrieval of the images themselves and not the data derived from the images. There is therefore a requirement for an information management solution that facilitates the indexing of experimental metadata and results of high content live cell imaging experiments. Results We have designed and implemented a data model and information management solution for the data gathered through high content live cell imaging experiments. Many of the experiments to be stored measure the translocation of fluorescently labelled proteins from cytoplasm to nucleus in individual cells. The functionality of this database has been enhanced by the addition of an algorithm that automatically annotates results of these experiments with the timings of translocations and periods of any oscillatory translocations as they are uploaded to the repository. Testing has shown the algorithm to perform well with a variety of previously unseen data. Conclusion Our repository is a fully functional example of how high throughput imaging data may be effectively indexed and managed to address the requirements of end users. By implementing the automated analysis of experimental results, we have provided a clear impetus for individuals to ensure that their data forms part of that which is stored in the repository. Although focused on imaging, the solution provided is sufficiently generic to be applied to other functional proteomics and genomics experiments. The software is available from: fhttp://code.google.com/p/livecellim/

Imaging large cohorts of single ion channels and their activity

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Katia eHiersemenzel

2013-09-01

Full Text Available As calcium is the most important signaling molecule in neurons and secretory cells, amongst many other cell types, it follows that an understanding of calcium channels and their regulation of exocytosis is of vital importance. Calcium imaging using calcium dyes such as Fluo3, or FRET-based dyes that have been used widely has provided invaluable information, which combined with modeling has estimated the sub-types of channels responsible for triggering the exocytotic machinery as well as inferences about the relative distances away from vesicle fusion sites these molecules adopt. Importantly, new super-resolution microscopy techniques, combined with novel Ca2+ indicators and imaginative imaging approaches can now define directly the nanoscale locations of very large cohorts of single channel molecules in relation to single vesicles. With combinations of these techniques the activity of individual channels can be visualized and quantified using novel Ca2+ indicators. Fluorescently labeled specific channel toxins can also be used to localize endogenous assembled channel tetramers. Fluorescence lifetime imaging microscopy and other single-photon-resolution spectroscopic approaches offer the possibility to quantify protein-protein interactions between populations of channels and the SNARE protein machinery for the first time. Together with simultaneous electrophysiology, this battery of quantitative imaging techniques has the potential to provide unprecedented detail describing the locations, dynamic behaviours, interactions and conductance activities of many thousands of channel molecules and vesicles in living cells.

CellSegm - a MATLAB toolbox for high-throughput 3D cell segmentation

Science.gov (United States)

2013-01-01

The application of fluorescence microscopy in cell biology often generates a huge amount of imaging data. Automated whole cell segmentation of such data enables the detection and analysis of individual cells, where a manual delineation is often time consuming, or practically not feasible. Furthermore, compared to manual analysis, automation normally has a higher degree of reproducibility. CellSegm, the software presented in this work, is a Matlab based command line software toolbox providing an automated whole cell segmentation of images showing surface stained cells, acquired by fluorescence microscopy. It has options for both fully automated and semi-automated cell segmentation. Major algorithmic steps are: (i) smoothing, (ii) Hessian-based ridge enhancement, (iii) marker-controlled watershed segmentation, and (iv) feature-based classfication of cell candidates. Using a wide selection of image recordings and code snippets, we demonstrate that CellSegm has the ability to detect various types of surface stained cells in 3D. After detection and outlining of individual cells, the cell candidates can be subject to software based analysis, specified and programmed by the end-user, or they can be analyzed by other software tools. A segmentation of tissue samples with appropriate characteristics is also shown to be resolvable in CellSegm. The command-line interface of CellSegm facilitates scripting of the separate tools, all implemented in Matlab, offering a high degree of flexibility and tailored workflows for the end-user. The modularity and scripting capabilities of CellSegm enable automated workflows and quantitative analysis of microscopic data, suited for high-throughput image based screening. PMID:23938087

Single cell time-lapse analysis reveals that podoplanin enhances cell survival and colony formation capacity of squamous cell carcinoma cells.

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Miyashita, Tomoyuki; Higuchi, Youichi; Kojima, Motohiro; Ochiai, Atsushi; Ishii, Genichiro

2017-01-06

Tumor initiating cells (TICs) are characterized by high clonal expansion capacity. We previously reported that podoplanin is a TIC-specific marker for the human squamous cell carcinoma cell line A431. The aim of this study is to explore the molecular mechanism underlying the high clonal expansion potential of podoplanin-positive A431cells using Fucci imaging. Single podoplanin-positive cells created large colonies at a significantly higher frequency than single podoplanin-negative cells, whereas no difference was observed between the two types of cells with respect to cell cycle status. Conversely, the cell death ratio of progenies derived from podoplanin-positive single cell was significantly lower than that of cells derived from podoplanin-negative cells. Single A431 cells, whose podoplanin expression was suppressed by RNA interference, exhibited increased cell death ratios and decreased frequency of large colony forming. Moreover, the frequency of large colony forming decreased significantly when podoplanin-positive single cells was treated with a ROCK (Rho-associated coiled-coil kinase) inhibitor, whereas no difference was observed in single podoplanin-negative cells. Our current study cleared that high clonal expansion capacity of podoplanin-positive TICs populations was the result of reduced cell death by podoplanin-mediated signaling. Therefore, podoplanin activity may be a therapeutic target in the treatment of squamous cell carcinomas.

The Automation and Exoplanet Orbital Characterization from the Gemini Planet Imager Exoplanet Survey

Science.gov (United States)

Jinfei Wang, Jason; Graham, James; Perrin, Marshall; Pueyo, Laurent; Savransky, Dmitry; Kalas, Paul; arriaga, Pauline; Chilcote, Jeffrey K.; De Rosa, Robert J.; Ruffio, Jean-Baptiste; Sivaramakrishnan, Anand; Gemini Planet Imager Exoplanet Survey Collaboration

2018-01-01

The Gemini Planet Imager (GPI) Exoplanet Survey (GPIES) is a multi-year 600-star survey to discover and characterize young Jovian exoplanets and their planet forming environments. For large surveys like GPIES, it is critical to have a uniform dataset processed with the latest techniques and calibrations. I will describe the GPI Data Cruncher, an automated data processing framework that is able to generate fully reduced data minutes after the data are taken and can also reprocess the entire campaign in a single day on a supercomputer. The Data Cruncher integrates into a larger automated data processing infrastructure which syncs, logs, and displays the data. I will discuss the benefits of the GPIES data infrastructure, including optimizing observing strategies, finding planets, characterizing instrument performance, and constraining giant planet occurrence. I will also discuss my work in characterizing the exoplanets we have imaged in GPIES through monitoring their orbits. Using advanced data processing algorithms and GPI's precise astrometric calibration, I will show that GPI can achieve one milliarcsecond astrometry on the extensively-studied planet Beta Pic b. With GPI, we can confidently rule out a possible transit of Beta Pic b, but have precise timings on a Hill sphere transit, and I will discuss efforts to search for transiting circumplanetary material this year. I will also discuss the orbital monitoring of other exoplanets as part of GPIES.

Single-cell nanotoxicity assays of superparamagnetic iron oxide nanoparticles.

Science.gov (United States)

Eustaquio, Trisha; Leary, James F

2012-01-01

Properly evaluating the nanotoxicity of nanoparticles involves much more than bulk-cell assays of cell death by necrosis. Cells exposed to nanoparticles may undergo repairable oxidative stress and DNA damage or be induced into apoptosis. Exposure to nanoparticles may cause the cells to alter their proliferation or differentiation or their cell-cell signaling with neighboring cells in a tissue. Nanoparticles are usually more toxic to some cell subpopulations than others, and toxicity often varies with cell cycle. All of these facts dictate that any nanotoxicity assay must be at the single-cell level and must try whenever feasible and reasonable to include many of these other factors. Focusing on one type of quantitative measure of nanotoxicity, we describe flow and scanning image cytometry approaches to measuring nanotoxicity at the single-cell level by using a commonly used assay for distinguishing between necrotic and apoptotic causes of cell death by one type of nanoparticle. Flow cytometry is fast and quantitative, provided that the cells can be prepared into a single-cell suspension for analysis. But when cells cannot be put into suspension without altering nanotoxicity results, or if morphology, attachment, and stain location are important, a scanning image cytometry approach must be used. Both methods are described with application to a particular type of nanoparticle, a superparamagnetic iron oxide nanoparticle (SPION), as an example of how these assays may be applied to the more general problem of determining the effects of nanomaterial exposure to living cells.

Single-cell technologies to study the immune system.

Science.gov (United States)

Proserpio, Valentina; Mahata, Bidesh

2016-02-01

The immune system is composed of a variety of cells that act in a coordinated fashion to protect the organism against a multitude of different pathogens. The great variability of existing pathogens corresponds to a similar high heterogeneity of the immune cells. The study of individual immune cells, the fundamental unit of immunity, has recently transformed from a qualitative microscopic imaging to a nearly complete quantitative transcriptomic analysis. This shift has been driven by the rapid development of multiple single-cell technologies. These new advances are expected to boost the detection of less frequent cell types and transient or intermediate cell states. They will highlight the individuality of each single cell and greatly expand the resolution of current available classifications and differentiation trajectories. In this review we discuss the recent advancement and application of single-cell technologies, their limitations and future applications to study the immune system. © 2015 The Authors. Immunology Published by John Wiley & Sons Ltd.

SU-G-206-01: A Fully Automated CT Tool to Facilitate Phantom Image QA for Quantitative Imaging in Clinical Trials

International Nuclear Information System (INIS)

Wahi-Anwar, M; Lo, P; Kim, H; Brown, M; McNitt-Gray, M

2016-01-01

Purpose: The use of Quantitative Imaging (QI) methods in Clinical Trials requires both verification of adherence to a specified protocol and an assessment of scanner performance under that protocol, which are currently accomplished manually. This work introduces automated phantom identification and image QA measure extraction towards a fully-automated CT phantom QA system to perform these functions and facilitate the use of Quantitative Imaging methods in clinical trials. Methods: This study used a retrospective cohort of CT phantom scans from existing clinical trial protocols - totaling 84 phantoms, across 3 phantom types using various scanners and protocols. The QA system identifies the input phantom scan through an ensemble of threshold-based classifiers. Each classifier - corresponding to a phantom type - contains a template slice, which is compared to the input scan on a slice-by-slice basis, resulting in slice-wise similarity metric values for each slice compared. Pre-trained thresholds (established from a training set of phantom images matching the template type) are used to filter the similarity distribution, and the slice with the most optimal local mean similarity, with local neighboring slices meeting the threshold requirement, is chosen as the classifier’s matched slice (if it existed). The classifier with the matched slice possessing the most optimal local mean similarity is then chosen as the ensemble’s best matching slice. If the best matching slice exists, image QA algorithm and ROIs corresponding to the matching classifier extracted the image QA measures. Results: Automated phantom identification performed with 84.5% accuracy and 88.8% sensitivity on 84 phantoms. Automated image quality measurements (following standard protocol) on identified water phantoms (n=35) matched user QA decisions with 100% accuracy. Conclusion: We provide a fullyautomated CT phantom QA system consistent with manual QA performance. Further work will include parallel

SU-G-206-01: A Fully Automated CT Tool to Facilitate Phantom Image QA for Quantitative Imaging in Clinical Trials

Energy Technology Data Exchange (ETDEWEB)

Wahi-Anwar, M; Lo, P; Kim, H; Brown, M; McNitt-Gray, M [UCLA Radiological Sciences, Los Angeles, CA (United States)

2016-06-15

Purpose: The use of Quantitative Imaging (QI) methods in Clinical Trials requires both verification of adherence to a specified protocol and an assessment of scanner performance under that protocol, which are currently accomplished manually. This work introduces automated phantom identification and image QA measure extraction towards a fully-automated CT phantom QA system to perform these functions and facilitate the use of Quantitative Imaging methods in clinical trials. Methods: This study used a retrospective cohort of CT phantom scans from existing clinical trial protocols - totaling 84 phantoms, across 3 phantom types using various scanners and protocols. The QA system identifies the input phantom scan through an ensemble of threshold-based classifiers. Each classifier - corresponding to a phantom type - contains a template slice, which is compared to the input scan on a slice-by-slice basis, resulting in slice-wise similarity metric values for each slice compared. Pre-trained thresholds (established from a training set of phantom images matching the template type) are used to filter the similarity distribution, and the slice with the most optimal local mean similarity, with local neighboring slices meeting the threshold requirement, is chosen as the classifier’s matched slice (if it existed). The classifier with the matched slice possessing the most optimal local mean similarity is then chosen as the ensemble’s best matching slice. If the best matching slice exists, image QA algorithm and ROIs corresponding to the matching classifier extracted the image QA measures. Results: Automated phantom identification performed with 84.5% accuracy and 88.8% sensitivity on 84 phantoms. Automated image quality measurements (following standard protocol) on identified water phantoms (n=35) matched user QA decisions with 100% accuracy. Conclusion: We provide a fullyautomated CT phantom QA system consistent with manual QA performance. Further work will include parallel

Automated computation of arbor densities: a step toward identifying neuronal cell types

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Uygar eSümbül

2014-11-01

Full Text Available The shape and position of a neuron convey information regarding its molecular and functional identity. The identification of cell types from structure, a classic method, relies on the time-consuming step of arbor tracing. However, as genetic tools and imaging methods make data-driven approaches to neuronal circuit analysis feasible, the need for automated processing increases. Here, we first establish that mouse retinal ganglion cell types can be as precise about distributing their arbor volumes across the inner plexiform layer as they are about distributing the skeletons of the arbors. Then, we describe an automated approach to computing the spatial distribution of the dendritic arbors, or arbor density, with respect to a global depth coordinate based on this observation. Our method involves three-dimensional reconstruction of neuronal arbors by a supervised machine learning algorithm, post-processing of the enhanced stacks to remove somata and isolate the neuron of interest, and registration of neurons to each other using automatically detected arbors of the starburst amacrine interneurons as fiducial markers. In principle, this method could be generalizable to other structures of the CNS, provided that they allow sparse labeling of the cells and contain a reliable axis of spatial reference.

Image reconstruction of dynamic infrared single-pixel imaging system

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Tong, Qi; Jiang, Yilin; Wang, Haiyan; Guo, Limin

2018-03-01

Single-pixel imaging technique has recently received much attention. Most of the current single-pixel imaging is aimed at relatively static targets or the imaging system is fixed, which is limited by the number of measurements received through the single detector. In this paper, we proposed a novel dynamic compressive imaging method to solve the imaging problem, where exists imaging system motion behavior, for the infrared (IR) rosette scanning system. The relationship between adjacent target images and scene is analyzed under different system movement scenarios. These relationships are used to build dynamic compressive imaging models. Simulation results demonstrate that the proposed method can improve the reconstruction quality of IR image and enhance the contrast between the target and the background in the presence of system movement.

Semantic focusing allows fully automated single-layer slide scanning of cervical cytology slides.

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Bernd Lahrmann

Full Text Available Liquid-based cytology (LBC in conjunction with Whole-Slide Imaging (WSI enables the objective and sensitive and quantitative evaluation of biomarkers in cytology. However, the complex three-dimensional distribution of cells on LBC slides requires manual focusing, long scanning-times, and multi-layer scanning. Here, we present a solution that overcomes these limitations in two steps: first, we make sure that focus points are only set on cells. Secondly, we check the total slide focus quality. From a first analysis we detected that superficial dust can be separated from the cell layer (thin layer of cells on the glass slide itself. Then we analyzed 2,295 individual focus points from 51 LBC slides stained for p16 and Ki67. Using the number of edges in a focus point image, specific color values and size-inclusion filters, focus points detecting cells could be distinguished from focus points on artifacts (accuracy 98.6%. Sharpness as total focus quality of a virtual LBC slide is computed from 5 sharpness features. We trained a multi-parameter SVM classifier on 1,600 images. On an independent validation set of 3,232 cell images we achieved an accuracy of 94.8% for classifying images as focused. Our results show that single-layer scanning of LBC slides is possible and how it can be achieved. We assembled focus point analysis and sharpness classification into a fully automatic, iterative workflow, free of user intervention, which performs repetitive slide scanning as necessary. On 400 LBC slides we achieved a scanning-time of 13.9±10.1 min with 29.1±15.5 focus points. In summary, the integration of semantic focus information into whole-slide imaging allows automatic high-quality imaging of LBC slides and subsequent biomarker analysis.

Semantic focusing allows fully automated single-layer slide scanning of cervical cytology slides.

Science.gov (United States)

Lahrmann, Bernd; Valous, Nektarios A; Eisenmann, Urs; Wentzensen, Nicolas; Grabe, Niels

2013-01-01

Liquid-based cytology (LBC) in conjunction with Whole-Slide Imaging (WSI) enables the objective and sensitive and quantitative evaluation of biomarkers in cytology. However, the complex three-dimensional distribution of cells on LBC slides requires manual focusing, long scanning-times, and multi-layer scanning. Here, we present a solution that overcomes these limitations in two steps: first, we make sure that focus points are only set on cells. Secondly, we check the total slide focus quality. From a first analysis we detected that superficial dust can be separated from the cell layer (thin layer of cells on the glass slide) itself. Then we analyzed 2,295 individual focus points from 51 LBC slides stained for p16 and Ki67. Using the number of edges in a focus point image, specific color values and size-inclusion filters, focus points detecting cells could be distinguished from focus points on artifacts (accuracy 98.6%). Sharpness as total focus quality of a virtual LBC slide is computed from 5 sharpness features. We trained a multi-parameter SVM classifier on 1,600 images. On an independent validation set of 3,232 cell images we achieved an accuracy of 94.8% for classifying images as focused. Our results show that single-layer scanning of LBC slides is possible and how it can be achieved. We assembled focus point analysis and sharpness classification into a fully automatic, iterative workflow, free of user intervention, which performs repetitive slide scanning as necessary. On 400 LBC slides we achieved a scanning-time of 13.9±10.1 min with 29.1±15.5 focus points. In summary, the integration of semantic focus information into whole-slide imaging allows automatic high-quality imaging of LBC slides and subsequent biomarker analysis.