151
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Wang X, Zheng B, Zhang RR, Li S, Chen X, Mulvihill JJ, Lu X, Pang H, Liu H. Automated analysis of fluorescent in situ hybridization (FISH) labeled genetic biomarkers in assisting cervical cancer diagnosis. Technol Cancer Res Treat 2010; 9:231-42. [PMID: 20441233 DOI: 10.1177/153303461000900302] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
The numerical and/or structural deviation of some chromosomes (i.e., monosomy and _polysomy of chromosomes 3 and X) are routinely used as positive genetic biomarkers to diagnose cervical cancer and predict the disease progression. Among the available diagnostic methods to analyze the aneusomy of chromosomes 3 and X, fluorescence in situ hybridization (FISH) technology has demonstrated significant advantages in assisting clinicians to more accurately detect and diagnose cervical carcinoma at an early stage, in particular for the women at a high risk for progression of low-grade and high-grade squamous intra-epithelium lesions (LSIL and HSIL). In order to increase the diagnostic accuracy, consistency, and efficiency from that of manual FISH analysis, this study aims to develop and test an automated FISH analysis method that includes a two-stage scheme. In the first stage, an interactive multiple-threshold algorithm is utilized to segment potential interphase nuclei candidates distributed in different intensity levels and a rule-based classifier is implemented to identify analyzable interphase cells. In the second stage, FISH labeled biomarker spots of chromosomes 3 and X are segmented by a top-hat transform. The independent FISH spots are then detected by a knowledge-based classifier, which enables recognition of the splitting and stringy FISH signals. Finally, the ratio of abnormal interphase cells with numerical changes of chromosomes 3 and X is calculated to detect positive cases. The experimental results of four test cases showed high agreement of FISH analysis results between the automated scheme and the cytogeneticist's analysis including 92.7% to 98.7% agreement in cell segmentation and 4.4% to 11.0% difference in cell classification. This preliminary study demonstrates the feasibility of potentially applying the automatic FISH analysis method to expedite the screening and detecting cervical cancer at an early stage.
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Affiliation(s)
- Xingwei Wang
- Department of Radiology, University of Pittsburgh Medical Center, Pittsburgh, PA, 15213, USA
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152
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Yan XK, Zhang N, Chen Y, Du J, Cao ZS, Zhou PK. Splitting of cell clumps in cytokinesis-blocked micronucleus images: application to improve the recognition ability of binucleated lymphocytes. Cytometry A 2010; 77:783-9. [PMID: 20653018 DOI: 10.1002/cyto.a.20926] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
The cytokinesis-blocked micronucleus (CBMN) test in human peripheral blood lymphocytes is an extensively used method for biomonitoring, mutagenicity testing, and especially radiation biodosimeter. Automated analysis of CBMN by image processing can provide faster and more reliable results with minimized subjective micronucleus (MN) identification than the time-consuming and tiresome manual scoring. Splitting of the clumps, i.e. overlapping lymphocytes, overlapping nuclei, and combined overlapping of nucleus and MN, remains an unsolved problem in the automated analysis of CBMN images, because ignoring them will dramatically decrease the recognition ability of binucleated cells and directly affect the statistical validation of MN frequency. We present a novel algorithm for splitting these clumps in CBMN images based on improved watershed transform. Experimental results show that this algorithm has succeeded in correctly splitting the clumps composed of nuclei or lymphocytes as well as combined clumps of MN and nucleus. This presented algorithm is valid for the splitting of clumps in CBMN images, and may also be adopted for other clumps where splitting of overlapping regions is required.
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Affiliation(s)
- Xue-Kun Yan
- Department of Radiation Toxicology and Oncology, Beijing Institute of Radiation Medicine, Beijing, China 100850
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153
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Yu W, Lee HK, Hariharan S, Bu W, Ahmed S. Evolving generalized Voronoi diagrams for accurate cellular image segmentation. Cytometry A 2010; 77:379-86. [PMID: 20169588 DOI: 10.1002/cyto.a.20876] [Citation(s) in RCA: 46] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
Analyzing cellular morphologies on a cell-by-cell basis is vital for drug discovery, cell biology, and many other biological studies. Interactions between cells in their culture environments cause cells to touch each other in acquired microscopy images. Because of this phenomenon, cell segmentation is a challenging task, especially when the cells are of similar brightness and of highly variable shapes. The concept of topological dependence and the maximum common boundary (MCB) algorithm are presented in our previous work (Yu et al., Cytometry Part A 2009;75A:289-297). However, the MCB algorithm suffers a few shortcomings, such as low computational efficiency and difficulties in generalizing to higher dimensions. To overcome these limitations, we present the evolving generalized Voronoi diagram (EGVD) algorithm. Utilizing image intensity and geometric information, EGVD preserves topological dependence easily in both 2D and 3D images, such that touching cells can be segmented satisfactorily. A systematic comparison with other methods demonstrates that EGVD is accurate and much more efficient.
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Affiliation(s)
- Weimiao Yu
- Bioinformatics Institute (BII), 30 Biopolis Street, #07-01, Matrix, Singapore 138671.
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154
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Forero MG, Pennack JA, Hidalgo A. DeadEasy neurons: automatic counting of HB9 neuronal nuclei in Drosophila. Cytometry A 2010; 77:371-8. [PMID: 20162534 DOI: 10.1002/cyto.a.20877] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
Research into the genetic basis of nervous system development and neurodegenerative diseases requires counting neurons to find out the extent of neurogenesis or neuronal loss. Drosophila is a widely used model organism for in vivo studies. However, counting neurons throughout the nervous system of the intact animal is humanly unfeasible. Automatic methods for cell counting in intact Drosophila are desirable. Here, we show a method called DeadEasy Neurons to count the number of neurons stained with anti-HB9 antibodies in Drosophila embryos. DeadEasy Neurons employs image filtering and mathematical morphology techniques in 2D and 3D, followed by identification of nuclei in 3D based on minimum volume, to count automatically the number of HB9 neurons in vivo. The resultant method has been validated for Drosophila embryos and we show here how it can be used to address biological questions. Counting neurons with DeadEasy is very fast, extremely accurate, and objective, and it enables analyses otherwise humanly unmanageable. DeadEasy Neurons can be modified by the user for other applications, and it will be freely available as an ImageJ plug-in. DeadEasy Neurons will be of interest to the microscopy, image processing, Drosophila, neurobiology, and biomedical communities.
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Affiliation(s)
- Manuel G Forero
- NeuroDevelopment Group, University of Birmingham, Birmingham, United Kingdom
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155
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Huang Y, Zhou X, Miao B, Lipinski M, Zhang Y, Li F, Degterev A, Yuan J, Hu G, Wong STC. A computational framework for studying neuron morphology from in vitro high content neuron-based screening. J Neurosci Methods 2010; 190:299-309. [PMID: 20580743 DOI: 10.1016/j.jneumeth.2010.05.012] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2009] [Revised: 05/11/2010] [Accepted: 05/16/2010] [Indexed: 10/19/2022]
Abstract
High content neuron image processing is considered as an important method for quantitative neurobiological studies. The main goal of analysis in this paper is to provide automatic image processing approaches to process neuron images for studying neuron mechanism in high content screening. In the nuclei channel, all nuclei are segmented and detected by applying the gradient vector field based watershed. Then the neuronal nuclei are selected based on the soma region detected in neurite channel. In neurite images, we propose a novel neurite centerline extraction approach using the improved line-pixel detection technique. The proposed neurite tracing method can detect the curvilinear structure more accurately compared with the current existing methods. An interface called NeuriteIQ based on the proposed algorithms is developed finally for better application in high content screening.
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Affiliation(s)
- Yue Huang
- Methodist Hospital Research Institute, Radiology Department, Houston, TX 77030, USA
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156
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Campana M, Maury B, Dutreix M, Peyriéras N, Sarti A. Methods toward in vivo measurement of zebrafish epithelial and deep cell proliferation. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2010; 98:103-117. [PMID: 19781805 DOI: 10.1016/j.cmpb.2009.08.008] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/04/2009] [Revised: 07/23/2009] [Accepted: 08/24/2009] [Indexed: 05/28/2023]
Abstract
We present a strategy for automatic classification and density estimation of epithelial enveloping layer (EVL) and deep layer (DEL) cells, throughout zebrafish early embryonic stages. Automatic cells classification provides the bases to measure the variability of relevant parameters, such as cells density, in different classes of cells and is finalized to the estimation of effectiveness and selectivity of anticancer drug in vivo. We aim at approaching these measurements through epithelial/deep cells classification, epithelial area and thickness measurement, and density estimation from scattered points. Our procedure is based on Minimal Surfaces, Otsu clustering, Delaunay Triangulation, and Within-R cloud of points density estimation approaches. In this paper, we investigated whether the distance between nuclei and epithelial surface is sufficient to discriminate epithelial cells from deep cells. Comparisons of different density estimators, experimental results, and extensively accuracy measurements are included.
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Affiliation(s)
- Matteo Campana
- Department of Electronics, Computer Sciences and Systems, Bologna University, Bologna, Italy.
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157
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Chaudhury KN, Puspoki Z, Munoz-Barrutia A, Sage D, Unser M. Fast detection of cells using a continuously scalable Mexican-hat-like template. 2010 IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING: FROM NANO TO MACRO 2010. [DOI: 10.1109/isbi.2010.5490229] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/19/2023]
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158
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Zanella C, Campana M, Rizzi B, Melani C, Sanguinetti G, Bourgine P, Mikula K, Peyrieras N, Sarti A. Cells segmentation from 3-D confocal images of early zebrafish embryogenesis. IEEE TRANSACTIONS ON IMAGE PROCESSING : A PUBLICATION OF THE IEEE SIGNAL PROCESSING SOCIETY 2010; 19:770-781. [PMID: 19955038 DOI: 10.1109/tip.2009.2033629] [Citation(s) in RCA: 37] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/26/2023]
Abstract
We designed a strategy for extracting the shapes of cell membranes and nuclei from time lapse confocal images taken throughout early zebrafish embryogenesis using a partial-differential-equation-based segmentation. This segmentation step is a prerequisite for an accurate quantitative analysis of cell morphodynamics during embryogenesis and it is the basis for an integrated understanding of biological processes. The segmentation of embryonic cells requires live zebrafish embryos fluorescently labeled to highlight sub-cellular structures and designing specific algorithms by adapting classical methods to image features. Our strategy includes the following steps: the signal-to-noise ratio is first improved by an edge-preserving filtering, then the cell shape is reconstructed applying a fully automated algorithm based on a generalized version of the Subjective Surfaces technique. Finally we present a procedure for the algorithm validation either from the accuracy and the robustness perspective.
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159
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Shutin D, Zlobinskaya O. Application of information-theoretic measures to quantitative analysis of immunofluorescent microscope imaging. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2010; 97:114-129. [PMID: 19570589 DOI: 10.1016/j.cmpb.2009.05.009] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/22/2008] [Revised: 04/23/2009] [Accepted: 05/25/2009] [Indexed: 05/28/2023]
Abstract
The goal of this contribution is to apply model-based information-theoretic measures to the quantification of relative differences between immunofluorescent signals. Several models for approximating the empirical fluorescence intensity distributions are considered, namely Gaussian, Gamma, Beta, and kernel densities. As a distance measure the Hellinger distance and the Kullback-Leibler divergence are considered. For the Gaussian, Gamma, and Beta models the closed-form expressions for evaluating the distance as a function of the model parameters are obtained. The advantages of the proposed quantification framework as compared to simple mean-based approaches are analyzed with numerical simulations. Two biological experiments are also considered. The first is the functional analysis of the p8 subunit of the TFIIH complex responsible for a rare hereditary multi-system disorder--trichothiodystrophy group A (TTD-A). In the second experiment the proposed methods are applied to assess the UV-induced DNA lesion repair rate. A good agreement between our in vivo results and those obtained with an alternative in vitro measurement is established. We believe that the computational simplicity and the effectiveness of the proposed quantification procedure will make it very attractive for different analysis tasks in functional proteomics, as well as in high-content screening.
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Affiliation(s)
- Dmitriy Shutin
- Signal Processing and Speech Communication Laboratory, Graz University of Technology, Inffeldgasse 12, A-8010 Graz, Austria.
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160
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Cui Y, Tan Y, Zhao B, Liberman L, Parbhu R, Kaplan J, Theodoulou M, Hudis C, Schwartz LH. Malignant lesion segmentation in contrast-enhanced breast MR images based on the marker-controlled watershed. Med Phys 2010; 36:4359-69. [PMID: 19928066 DOI: 10.1118/1.3213514] [Citation(s) in RCA: 36] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Abstract
Breast tumor volume measured on MRI has been used to assess response to neoadjuvant chemotherapy. However, accurate and reproducible delineation of breast lesions can be challenging, since the lesions may have complicated topological structures and heterogeneous intensity distributions. In this article, the authors present an advanced computerized method to semiautomatically segment tumor volumes on T1-weighted, contrast-enhanced breast MRI. The method starts with manual selection of a region of interest (ROI) that contains the lesion to be segmented in a single image, followed by automated separation of the lesion volume from its surrounding breast parenchyma by using a unique combination of the image processing techniques including Gaussian mixture modeling and a marker-controlled watershed transform. Explicitly, the Gaussian mixture modeling is applied to an intensity histogram of the pixels inside the ROI to distinguish the tumor class from other tissues. Based on the ROI and the intensity distribution of the tumor, internal and external markers are determined and the tumor contour is delineated using the marker-controlled watershed transform. To obtain the tumor volume, the segmented tumor in one slice is propagated to the adjacent slice to form an ROI in that slice. The marker-controlled watershed segmentation is then used again to obtain a tumor contour in the propagated slice. This procedure is terminated when there is no lesion in an adjacent slice. To reduce measurement variations possibly caused by the manual selection of the ROI, the segmentation result is refined based on an automatically determined ROI based on the segmented volume. The algorithm was applied to 13 patients with breast cancer, prospectively accrued prior to beginning neoadjuvant chemotherapy. Each patient had two MRI scans, a baseline MRI examination prior to commencing neoadjuvant chemotherapy and a 1 week follow-up after receiving the first dose of neoadjuvant chemotherapy. Blinded to the computer segmentation results, two experienced radiologists manually delineated all tumors independently. The computer results were then compared with the manually generated results using the volume overlap ratio, defined as the intersection of the computer- and radiologist-generated tumor volumes divided by the union of the two. The algorithm reached overall overlap ratios of 62.6% +/- 9.1% and 61.0% +/- 11.3% in comparison to the two manual segmentation results, respectively. The overall overlap ratio between the two radiologists' manual segmentations was 64.3% +/- 10.4%. Preliminary results suggest that the proposed algorithm is a promising method for assisting in tumor volume measurement in contrast-enhanced breast MRI.
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Affiliation(s)
- Yunfeng Cui
- Department of Radiology, Memorial Sloan-Kettering Cancer Center, 1275 York Avenue, New York, New York 10065, USA.
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161
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Al-Kofahi Y, Lassoued W, Lee W, Roysam B. Improved automatic detection and segmentation of cell nuclei in histopathology images. IEEE Trans Biomed Eng 2009; 57:841-52. [PMID: 19884070 DOI: 10.1109/tbme.2009.2035102] [Citation(s) in RCA: 322] [Impact Index Per Article: 21.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
Automatic segmentation of cell nuclei is an essential step in image cytometry and histometry. Despite substantial progress, there is a need to improve accuracy, speed, level of automation, and adaptability to new applications. This paper presents a robust and accurate novel method for segmenting cell nuclei using a combination of ideas. The image foreground is extracted automatically using a graph-cuts-based binarization. Next, nuclear seed points are detected by a novel method combining multiscale Laplacian-of-Gaussian filtering constrained by distance-map-based adaptive scale selection. These points are used to perform an initial segmentation that is refined using a second graph-cuts-based algorithm incorporating the method of alpha expansions and graph coloring to reduce computational complexity. Nuclear segmentation results were manually validated over 25 representative images (15 in vitro images and 10 in vivo images, containing more than 7400 nuclei) drawn from diverse cancer histopathology studies, and four types of segmentation errors were investigated. The overall accuracy of the proposed segmentation algorithm exceeded 86%. The accuracy was found to exceed 94% when only over- and undersegmentation errors were considered. The confounding image characteristics that led to most detection/segmentation errors were high cell density, high degree of clustering, poor image contrast and noisy background, damaged/irregular nuclei, and poor edge information. We present an efficient semiautomated approach to editing automated segmentation results that requires two mouse clicks per operation.
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Affiliation(s)
- Yousef Al-Kofahi
- Department of Electrical, Computer and Systems Engineering (ECSE), Rensselaer Polytechnic Institute, Troy, NY 12180, USA.
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162
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Tsui M, Xie T, Orth JD, Carpenter AE, Rudnicki S, Kim S, Shamu CE, Mitchison TJ. An intermittent live cell imaging screen for siRNA enhancers and suppressors of a kinesin-5 inhibitor. PLoS One 2009; 4:e7339. [PMID: 19802393 PMCID: PMC2752188 DOI: 10.1371/journal.pone.0007339] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2009] [Accepted: 08/22/2009] [Indexed: 12/03/2022] Open
Abstract
Kinesin-5 (also known as Eg5, KSP and Kif11) is required for assembly of a bipolar mitotic spindle. Small molecule inhibitors of Kinesin-5, developed as potential anti-cancer drugs, arrest cell in mitosis and promote apoptosis of cancer cells. We performed a genome-wide siRNA screen for enhancers and suppressors of a Kinesin-5 inhibitor in human cells to elucidate cellular responses, and thus identify factors that might predict drug sensitivity in cancers. Because the drug's actions play out over several days, we developed an intermittent imaging screen. Live HeLa cells expressing GFP-tagged histone H2B were imaged at 0, 24 and 48 hours after drug addition, and images were analyzed using open-source software that incorporates machine learning. This screen effectively identified siRNAs that caused increased mitotic arrest at low drug concentrations (enhancers), and vice versa (suppressors), and we report siRNAs that caused both effects. We then classified the effect of siRNAs for 15 genes where 3 or 4 out of 4 siRNA oligos tested were suppressors as assessed by time lapse imaging, and by testing for suppression of mitotic arrest in taxol and nocodazole. This identified 4 phenotypic classes of drug suppressors, which included known and novel genes. Our methodology should be applicable to other screens, and the suppressor and enhancer genes we identified may open new lines of research into mitosis and checkpoint biology.
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Affiliation(s)
- Melody Tsui
- Department of Systems Biology, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Tiao Xie
- Department of Systems Biology, Harvard Medical School, Boston, Massachusetts, United States of America
| | - James D. Orth
- Department of Systems Biology, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Anne E. Carpenter
- Broad Institute of Harvard and MIT, Cambridge, Massachusetts, United States of America
| | - Stewart Rudnicki
- ICCB-Longwood Screening Facility, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Suejong Kim
- Department of Systems Biology, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Caroline E. Shamu
- Department of Systems Biology, Harvard Medical School, Boston, Massachusetts, United States of America
- ICCB-Longwood Screening Facility, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Timothy J. Mitchison
- Department of Systems Biology, Harvard Medical School, Boston, Massachusetts, United States of America
- * E-mail:
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163
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Jeong MR, Ko BC, Nam JY. Overlapping nuclei segmentation based on Bayesian networks and stepwise merging strategy. J Microsc 2009; 235:188-98. [PMID: 19659912 DOI: 10.1111/j.1365-2818.2009.03199.x] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
This paper presents a new approach to the segmentation of fluorescence in situ hybridization images. First, to segment the cell nuclei from the background, a threshold is estimated using a Gaussian mixture model and maximizing the likelihood function of the grey values for the cell images. After the nuclei segmentation, the overlapping and isolated nuclei are classified to facilitate a more accurate nuclei analysis. To do this, the morphological features of the nuclei, such their compactness, smoothness and moments, are extracted from training data to generate three probability distribution functions that are then applied to a Bayesian network as evidence. Following the nuclei classification, the overlapping nuclei are segmented into isolated nuclei using an intensity gradient transform and watershed algorithm. A new stepwise merging strategy is also proposed to merge fragments into a major nucleus. Experimental results using fluorescence in situ hybridization images confirm that the proposed system produced better segmentation results when compared to previous methods, because of the nuclei classification before separating the overlapping nuclei.
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Affiliation(s)
- M-R Jeong
- Department of Computer Engineering, Keimyung University, 1000 Shindang-dong Dalseo-gu, Daegu, 704-701, Korea
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164
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Huang Y, Sun X, Hu G. An automatic integrated approach for stained neuron detection in studying neuron migration. Microsc Res Tech 2009; 73:109-18. [PMID: 19697431 DOI: 10.1002/jemt.20762] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Neurons that come to populate the six-layered cerebral cortex are born deep within the developing brain in the surface of the embryonic cerebral ventricles. It is very important to detect these neurons for studying histogenesis of the brain and abnormal migration that had been linked to cognitive deficits, mental retardation, and motor disorders. The visualization of labeled cells in brain sections was performed by immunocytochemical examination and its image data were documented to microscopic pictures. Based on the fact, automatic accurate neurons labeling is prerequisite instead of time-consuming manual labeling. In this article, a fully automated image processing approach is proposed to detect all the stained neurons in microscopic images. First of all, dark stained neurons are achieved by thresholding in blue channel of image. And then a modified fuzzy c-means clustering method, called alternative fuzzy c-means is applied to achieve higher classification accuracy in extracting constraint factor. Finally, watershed based on gradient vector flow is employed to the constraint factor image to segment all the neurons, including clustered neurons. The results demonstrate that the proposed method can be a useful tool in neuron image analysis.
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Affiliation(s)
- Yue Huang
- Biomedical Engineering Department, Medical School, Tsinghua University, Beijing 100084, China
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165
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Yu W, Lee HK, Hariharan S, Bu W, Ahmed S. Quantitative neurite outgrowth measurement based on image segmentation with topological dependence. Cytometry A 2009; 75:289-97. [PMID: 18951464 DOI: 10.1002/cyto.a.20664] [Citation(s) in RCA: 51] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
The study of neuronal morphology and neurite outgrowth has been enhanced by the combination of imaging informatics and high content screening, in which thousands of images are acquired using robotic fluorescent microscopy. To understand the process of neurite outgrowth in the context of neuroregeneration, we used mouse neuroblastoma N1E115 as our model neuronal cell. Six-thousand cellular images of four different culture conditions were acquired with two-channel widefield fluorescent microscopy. We developed a software package called NeuronCyto. It is a fully automatic solution for neurite length measurement and complexity analysis. A novel approach based on topological analysis is presented to segment cells. The detected nuclei were used as references to initialize the level set function. Merging and splitting of cells segments were prevented using dynamic watershed lines based on the constraint of topological dependence. A tracing algorithm was developed to automatically trace neurites and measure their lengths quantitatively on a cell-by-cell basis. NeuronCyto analyzes three important biologically relevant features, which are the length, branching complexity, and number of neurites. The application of NeuronCyto on the experiments of Toca-1 and serum starvation show that the transfection of Toca-1 cDNA induces longer neurites with more complexities than serum starvation.
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Affiliation(s)
- Weimiao Yu
- Imaging Informatics Division, Bioinformatics Institute (BII), Matrix, Singapore.
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166
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Wu HS, Bitar M, Burstein D, Ramer M, Gil J. Envelope Mapping Algorithm and Its Applications in Enhancement and Segmentation of Pancreatic Cell Images. J Imaging Sci Technol 2009. [DOI: 10.2352/j.imagingsci.technol.2009.53.3.030501] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/01/2022]
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167
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Hodneland E, Bukoreshtliev NV, Eichler TW, Tai XC, Gurke S, Lundervold A, Gerdes HH. A unified framework for automated 3-d segmentation of surface-stained living cells and a comprehensive segmentation evaluation. IEEE TRANSACTIONS ON MEDICAL IMAGING 2009; 28:720-738. [PMID: 19131295 DOI: 10.1109/tmi.2008.2011522] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/27/2023]
Abstract
This work presents a unified framework for whole cell segmentation of surface stained living cells from 3-D data sets of fluorescent images. Every step of the process is described, image acquisition, prefiltering, ridge enhancement, cell segmentation, and a segmentation evaluation. The segmentation results from two different automated approaches for segmentation are compared to manual segmentation of the same data using a rigorous evaluation scheme. This revealed that combination of the respective cell types with the most suitable microscopy method resulted in high success rates up to 97%. The described approach permits to automatically perform a statistical analysis of various parameters from living cells.
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Affiliation(s)
- Erlend Hodneland
- Department of Biomedicine, University of Bergen, 5009 Bergen, Norway.
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168
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Abstract
Oversegmentation is a tough problem in the morphological watershed segmentation of irregular-shaped binary particles, which is usually caused by spurious minima in the inverse distance transform. The position relationship between two objects is clear, according to the value of overlap parameter defined in the paper, and an adaptive algorithm is presented to depress oversegmentation by building the criterion to merge the spurious local minima. Some particle images are provided to validate the performance of the proposed method.
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Affiliation(s)
- H Q Sun
- Department of Military Oceanography, Dalian Naval Academy, Dalian 116018, China.
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169
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Oberlaender M, Dercksen VJ, Egger R, Gensel M, Sakmann B, Hege HC. Automated three-dimensional detection and counting of neuron somata. J Neurosci Methods 2009; 180:147-60. [PMID: 19427542 DOI: 10.1016/j.jneumeth.2009.03.008] [Citation(s) in RCA: 48] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2008] [Revised: 03/06/2009] [Accepted: 03/09/2009] [Indexed: 11/28/2022]
Abstract
We present a novel approach for automated detection of neuron somata. A three-step processing pipeline is described on the example of confocal image stacks of NeuN-stained neurons from rat somato-sensory cortex. It results in a set of position landmarks, representing the midpoints of all neuron somata. In the first step, foreground and background pixels are identified, resulting in a binary image. It is based on local thresholding and compensates for imaging and staining artifacts. Once this pre-processing guarantees a standard image quality, clusters of touching neurons are separated in the second step, using a marker-based watershed approach. A model-based algorithm completes the pipeline. It assumes a dominant neuron population with Gaussian distributed volumes within one microscopic field of view. Remaining larger objects are hence split or treated as a second neuron type. A variation of the processing pipeline is presented, showing that our method can also be used for co-localization of neurons in multi-channel images. As an example, we process 2-channel stacks of NeuN-stained somata, labeling all neurons, counterstained with GAD67, labeling GABAergic interneurons, using an adapted pre-processing step for the second channel. The automatically generated landmark sets are compared to manually placed counterparts. A comparison yields that the deviation in landmark position is negligible and that the difference between the numbers of manually and automatically counted neurons is less than 4%. In consequence, this novel approach for neuron counting is a reliable and objective alternative to manual detection.
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Affiliation(s)
- Marcel Oberlaender
- Max Planck Institute of Neurobiology, Group "Cortical Column in silico", Am Klopferspitz 18, Martinsried 82152, Germany.
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170
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Jierong Cheng, Rajapakse J. Segmentation of Clustered Nuclei With Shape Markers and Marking Function. IEEE Trans Biomed Eng 2009; 56:741-8. [DOI: 10.1109/tbme.2008.2008635] [Citation(s) in RCA: 226] [Impact Index Per Article: 15.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
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171
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Márquez JA. Enhancing watershed segmentation of touching and weakly-connected features in biomedical images. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2009; 2008:3095-8. [PMID: 19163361 DOI: 10.1109/iembs.2008.4649858] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
We report how to improve and control watershed segmentation of touching features in optical-microscope images of immunochemically stained cells and in three-dimensional (3D) reconstruction of weak-connected components in transmission electron microscopy images from ultra-thin slices of compact chromatin clumps, from rat lymphocytes. Our approach includes image processing of the distance-transform domain, and a discrete-boundary formulation of morphological operators, to speed up the 3D watershed segmentation. The adjustment for connectivity criteria, as well as other tuning parameters, come from the Nyquist sampling criterion, applied to spatial resolution, and are obtained from biological considerations, such as the average size of a normal cell. We also combined both enhancements in 3D and present the mathematical background as well as visual results.
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Affiliation(s)
- Jorge A Márquez
- Center of Applied Science and Technological Development, National Autonomous University of Mexico, México D.F., México.
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172
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173
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A Two-Phase Segmentation of Cell Nuclei Using Fast Level Set-Like Algorithms. IMAGE ANALYSIS 2009. [DOI: 10.1007/978-3-642-02230-2_40] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
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174
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Hodneland E, Tai XC, Gerdes HH. Four-Color Theorem and Level Set Methods for Watershed Segmentation. Int J Comput Vis 2008. [DOI: 10.1007/s11263-008-0199-4] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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175
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Fenistein D, Lenseigne B, Christophe T, Brodin P, Genovesio A. A fast, fully automated cell segmentation algorithm for high-throughput and high-content screening. Cytometry A 2008; 73:958-64. [PMID: 18752283 DOI: 10.1002/cyto.a.20627] [Citation(s) in RCA: 37] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
High-throughput, high-content screening (HT-HCS) of large compound libraries for drug discovery imposes new constraints on image analysis algorithms. Time and robustness are paramount while accuracy is intrinsically statistical. In this article, a fast and fully automated algorithm for cell segmentation is proposed. The algorithm is based on a strong attachment to the data that provide robustness and have been validated on the HT-HCS of large compound libraries and different biological assays. We present the algorithm and its performance, a description of its advantages and limitations, and a discussion of its range of application.
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Affiliation(s)
- D Fenistein
- Image Mining Group, Institut Pasteur Korea, Hawolgok-dong, Seongbuk-gu, Seoul 136-791, Korea.
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176
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Li G, Liu T, Nie J, Guo L, Chen J, Zhu J, Xia W, Mara A, Holley S, Wong STC. Segmentation of touching cell nuclei using gradient flow tracking. J Microsc 2008; 231:47-58. [PMID: 18638189 DOI: 10.1111/j.1365-2818.2008.02016.x] [Citation(s) in RCA: 39] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Reliable cell nuclei segmentation is an important yet unresolved problem in biological imaging studies. This paper presents a novel computerized method for robust cell nuclei segmentation based on gradient flow tracking. This method is composed of three key steps: (1) generate a diffused gradient vector flow field; (2) perform a gradient flow tracking procedure to attract points to the basin of a sink; and (3) separate the image into small regions, each containing one nucleus and nearby peripheral background, and perform local adaptive thresholding in each small region to extract the cell nucleus from the background. To show the generality of the proposed method, we report the validation and experimental results using microscopic image data sets from three research labs, with both over-segmentation and under-segmentation rates below 3%. In particular, this method is able to segment closely juxtaposed or clustered cell nuclei, with high sensitivity and specificity in different situations.
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Affiliation(s)
- G Li
- School of Automation, Northwestern Polytechnic University, Xi'an, China
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177
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Mosaliganti K, Cooper L, Sharp R, Machiraju R, Leone G, Huang K, Saltz J. Reconstruction of cellular biological structures from optical microscopy data. IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 2008; 14:863-876. [PMID: 18467760 DOI: 10.1109/tvcg.2008.30] [Citation(s) in RCA: 13] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/26/2023]
Abstract
Developments in optical microscopy imaging have generated large high-resolution data sets that have spurred medical researchers to conduct investigations into mechanisms of disease, including cancer at cellular and subcellular levels. The work reported here demonstrates that a suitable methodology can be conceived that isolates modality-dependent effects from the larger segmentation task and that 3D reconstructions can be cognizant of shapes as evident in the available 2D planar images. In the current realization, a method based on active geodesic contours is first deployed to counter the ambiguity that exists in separating overlapping cells on the image plane. Later, another segmentation effort based on a variant of Voronoi tessellations improves the delineation of the cell boundaries using a Bayesian formulation. In the next stage, the cells are interpolated across the third dimension thereby mitigating the poor structural correlation that exists in that dimension. We deploy our methods on three separate data sets obtained from light, confocal, and phase-contrast microscopy and validate the results appropriately.
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Affiliation(s)
- Kishore Mosaliganti
- Department of Computer Science and Engineering, Ohio State University, Columbus, OH 43210, USA.
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178
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Chen C, Li H, Zhou X, Wong STC. Constraint factor graph cut-based active contour method for automated cellular image segmentation in RNAi screening. J Microsc 2008; 230:177-91. [PMID: 18445146 DOI: 10.1111/j.1365-2818.2008.01974.x] [Citation(s) in RCA: 26] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Abstract
Image-based, high throughput genome-wide RNA interference (RNAi) experiments are increasingly carried out to facilitate the understanding of gene functions in intricate biological processes. Automated screening of such experiments generates a large number of images with great variations in image quality, which makes manual analysis unreasonably time-consuming. Therefore, effective techniques for automatic image analysis are urgently needed, in which segmentation is one of the most important steps. This paper proposes a fully automatic method for cells segmentation in genome-wide RNAi screening images. The method consists of two steps: nuclei and cytoplasm segmentation. Nuclei are extracted and labelled to initialize cytoplasm segmentation. Since the quality of RNAi image is rather poor, a novel scale-adaptive steerable filter is designed to enhance the image in order to extract long and thin protrusions on the spiky cells. Then, constraint factor GCBAC method and morphological algorithms are combined to be an integrated method to segment tight clustered cells. Compared with the results obtained by using seeded watershed and the ground truth, that is, manual labelling results by experts in RNAi screening data, our method achieves higher accuracy. Compared with active contour methods, our method consumes much less time. The positive results indicate that the proposed method can be applied in automatic image analysis of multi-channel image screening data.
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Affiliation(s)
- C Chen
- Department of EEIS, University of Science and Technology of China, Hefei, PR China
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179
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Ko B, Seo M, Nam JY. Microscopic cell nuclei segmentation based on adaptive attention window. J Digit Imaging 2008; 22:259-74. [PMID: 18560941 DOI: 10.1007/s10278-008-9129-9] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2008] [Revised: 04/16/2008] [Accepted: 04/24/2008] [Indexed: 10/21/2022] Open
Abstract
This paper presents an adaptive attention window (AAW)-based microscopic cell nuclei segmentation method. For semantic AAW detection, a luminance map is used to create an initial attention window, which is then reduced close to the size of the real region of interest (ROI) using a quad-tree. The purpose of the AAW is to facilitate background removal and reduce the ROI segmentation processing time. Region segmentation is performed within the AAW, followed by region clustering and removal to produce segmentation of only ROIs. Experimental results demonstrate that the proposed method can efficiently segment one or more ROIs and produce similar segmentation results to human perception. In future work, the proposed method will be used for supporting a region-based medical image retrieval system that can generate a combined feature vector of segmented ROIs based on extraction and patient data.
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Affiliation(s)
- ByoungChul Ko
- Shindang-dong Dalseo-gu, Department of Computer Engineering, Keimyung University, Daegu, South Korea.
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180
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Karvelis PS, Tzallas AT, Fotiadis DI, Georgiou I. A multichannel watershed-based segmentation method for multispectral chromosome classification. IEEE TRANSACTIONS ON MEDICAL IMAGING 2008; 27:697-708. [PMID: 18450542 DOI: 10.1109/tmi.2008.916962] [Citation(s) in RCA: 15] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/26/2023]
Abstract
Multiplex fluorescent in situ hybridization (M-FISH) is a recently developed chromosome imaging technique where each chromosome class appears to have a distinct color. This technique not only facilitates the detection of subtle chromosomal aberrations but also makes the analysis of chromosome images easier; both for human inspection and computerized analysis. In this paper, a novel method for segmentation and classification of M-FISH chromosome images is presented. The segmentation is based on the multichannel watershed transform in order to define regions of similar spatial and spectral characteristics. Then, a Bayes classifier, task-specific on region classification, is applied. Our method consists of four basic steps: 1) computation of the gradient magnitude of the image, 2) application of the watershed transform to decompose the image into a set of homogenous regions, 3) classification of each region, and 4) merging of similar adjacent regions. The method is evaluated using a publicly available chromosome image database and the obtained overall accuracy is 82.4%. By introducing the classification of each watershed region, the proposed method achieves substantially better results compared to other methods at a lower computational cost. The combination of the multichannel segmentation and the region-based classification is found to improve the overall classification accuracy compared to pixel-by-pixel approaches.
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Affiliation(s)
- P S Karvelis
- Department of Computer Science, University of Ioannina, 45110 Ioannina, Greece.
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181
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Gurcan MN, Pan T, Shimada H, Saltz J. Image analysis for neuroblastoma classification: segmentation of cell nuclei. CONFERENCE PROCEEDINGS : ... ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL CONFERENCE 2008; 2006:4844-7. [PMID: 17947119 DOI: 10.1109/iembs.2006.260837] [Citation(s) in RCA: 63] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Neuroblastoma is a childhood cancer of the nervous system. Current prognostic classification of this disease partly relies on morphological characteristics of the cells from H&E-stained images. In this work, an automated cell nuclei segmentation method is developed. This method employs morphological top-hat by reconstruction algorithm coupled with hysteresis thresholding to both detect and segment the cell nuclei. Accuracy of the automated cell nuclei segmentation algorithm is measured by comparing its outputs to manual segmentation. The average segmentation accuracy is 90.24+/-5.14%
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Affiliation(s)
- Metin N Gurcan
- Biomed. Informatics Dept., Ohio State Univ., Columbus, OH 43210, USA.
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182
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Liu T, Li G, Nie J, Tarokh A, Zhou X, Guo L, Malicki J, Xia W, Wong STC. An automated method for cell detection in zebrafish. Neuroinformatics 2008; 6:5-21. [PMID: 18288618 DOI: 10.1007/s12021-007-9005-7] [Citation(s) in RCA: 23] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2007] [Accepted: 11/02/2007] [Indexed: 01/01/2023]
Abstract
Quantification of cells is a critical step towards the assessment of cell fate in neurological disease or developmental models. Here, we present a novel cell detection method for the automatic quantification of zebrafish neuronal cells, including primary motor neurons, Rohon-Beard neurons, and retinal cells. Our method consists of four steps. First, a diffused gradient vector field is produced. Subsequently, the orientations and magnitude information of diffused gradients are accumulated, and a response image is computed. In the third step, we perform non-maximum suppression on the response image and identify the detection candidates. In the fourth and final step the detected objects are grouped into clusters based on their color information. Using five different datasets depicting zebrafish cells, we show that our method consistently displays high sensitivity and specificity of over 95%. Our results demonstrate the general applicability of this method to different data samples, including nuclear staining, immunohistochemistry, and cell death detection.
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Affiliation(s)
- Tianming Liu
- The Center for Biomedical Informatics, The Methodist Hospital Research Institute, Houston, TX, USA
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183
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Automated quantification of nuclear immunohistochemical markers with different complexity. Histochem Cell Biol 2008; 129:379-87. [DOI: 10.1007/s00418-007-0368-5] [Citation(s) in RCA: 27] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/03/2007] [Indexed: 10/22/2022]
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184
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Automated Discrimination of Pathological Regions in Tissue Images: Unsupervised Clustering vs. Supervised SVM Classification. ACTA ACUST UNITED AC 2008. [DOI: 10.1007/978-3-540-92219-3_26] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register]
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185
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Prasad B, Badawy W. High-Throughput Identification and Classification Algorithm for Leukemia Population Statistics. J Imaging Sci Technol 2008. [DOI: 10.2352/j.imagingsci.technol.(2008)52:3(030509)] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/01/2022]
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186
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Lin G, Chawla MK, Olson K, Barnes CA, Guzowski JF, Bjornsson C, Shain W, Roysam B. A multi-model approach to simultaneous segmentation and classification of heterogeneous populations of cell nuclei in 3D confocal microscope images. Cytometry A 2007; 71:724-36. [PMID: 17654650 DOI: 10.1002/cyto.a.20430] [Citation(s) in RCA: 88] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Automated segmentation and morphometry of fluorescently labeled cell nuclei in batches of 3D confocal stacks is essential for quantitative studies. Model-based segmentation algorithms are attractive due to their robustness. Previous methods incorporated a single nuclear model. This is a limitation for tissues containing multiple cell types with different nuclear features. Improved segmentation for such tissues requires algorithms that permit multiple models to be used simultaneously. This requires a tight integration of classification and segmentation algorithms. Two or more nuclear models are constructed semiautomatically from user-provided training examples. Starting with an initial over-segmentation produced by a gradient-weighted watershed algorithm, a hierarchical fragment merging tree rooted at each object is built. Linear discriminant analysis is used to classify each candidate using multiple object models. On the basis of the selected class, a Bayesian score is computed. Fragment merging decisions are made by comparing the score with that of other candidates, and the scores of constituent fragments of each candidate. The overall segmentation accuracy was 93.7% and classification accuracy was 93.5%, respectively, on a diverse collection of images drawn from five different regions of the rat brain. The multi-model method was found to achieve high accuracy on nuclear segmentation and classification by correctly resolving ambiguities in clustered regions containing heterogeneous cell populations.
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Affiliation(s)
- Gang Lin
- ECSE Department and Center for Subsurface Sensing and Imaging Systems, Rensselaer Polytechnic Institute, Troy, New York 12180, USA
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187
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Lehmussola A, Ruusuvuori P, Selinummi J, Huttunen H, Yli-Harja O. Computational framework for simulating fluorescence microscope images with cell populations. IEEE TRANSACTIONS ON MEDICAL IMAGING 2007; 26:1010-6. [PMID: 17649914 DOI: 10.1109/tmi.2007.896925] [Citation(s) in RCA: 82] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2023]
Abstract
Fluorescence microscopy combined with digital imaging constructs a basic platform for numerous biomedical studies in the field of cellular imaging. As the studies relying on analysis of digital images have become popular, the validation of image processing methods used in automated image cytometry has become an important topic. Especially, the need for efficient validation has arisen from emerging high-throughput microscopy systems where manual validation is impractical. We present a simulation platform for generating synthetic images of fluorescence-stained cell populations with realistic properties. Moreover, we show that the synthetic images enable the validation of analysis methods for automated image cytometry and comparison of their performance. Finally, we suggest additional usage scenarios for the simulator. The presented simulation framework, with several user-controllable parameters, forms a versatile tool for many kinds of validation tasks, and is freely available at http://www.cs.tut.fi/sgn/csb/simcep.
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Affiliation(s)
- Antti Lehmussola
- Institute of Signal Processing, Tampere University of Technology, FI-33101 Tampere, Finland
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188
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Lamprecht MR, Sabatini DM, Carpenter AE. CellProfiler: free, versatile software for automated biological image analysis. Biotechniques 2007; 42:71-5. [PMID: 17269487 DOI: 10.2144/000112257] [Citation(s) in RCA: 620] [Impact Index Per Article: 36.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
Abstract
Careful visual examination of biological samples is quite powerful, but many visual analysis tasks done in the laboratory are repetitive, tedious, and subjective. Here we describe the use of the open-source software, CellProfiler, to automatically identify and measure a variety of biological objects in images. The applications demonstrated here include yeast colony counting and classifying, cell microarray annotation, yeast patch assays, mouse tumor quantification, wound healing assays, and tissue topology measurement. The software automatically identifies objects in digital images, counts them, and records a full spectrum of measurements for each object, including location within the image, size, shape, color intensity, degree of correlation between colors, texture (smoothness), and number of neighbors. Small numbers of images can be processed automatically on a personal computer and hundreds of thousands can be analyzed using a computing cluster. This free, easy-to-use software enables biologists to comprehensively and quantitatively address many questions that previously would have required custom programming, thereby facilitating discovery in a variety of biological fields of study.
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189
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AHMED WAMIQMANZOOR, AYYAZ MUHAMMADNAEEM, RAJWA BARTEK, KHAN FARRUKH, GHAFOOR ARIF, ROBINSON JPAUL. SEMANTIC ANALYSIS OF BIOLOGICAL IMAGING DATA: CHALLENGES AND OPPORTUNITIES. INTERNATIONAL JOURNAL OF SEMANTIC COMPUTING 2007. [DOI: 10.1142/s1793351x07000032] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Microscopic imaging is one of the most common techniques for investigating biological systems. In recent years there has been a tremendous growth in the volume of biological imaging data owing to rapid advances in optical instrumentation, high-speed cameras and fluorescent probes. Powerful semantic analysis tools are required to exploit the full potential of the information content of these data. Semantic analysis of multi-modality imaging data, however, poses unique challenges. In this paper we outline the state-of-the-art in this area along with the challenges facing this domain. Information extraction from biological imaging data requires modeling at multiple levels of detail. While some applications require only quantitative analysis at the level of cells and subcellular objects, others require modeling of spatial and temporal changes associated with dynamic biological processes. Modeling of biological data at different levels of detail allows not only quantitative analysis but also the extraction of high-level semantics. Development of powerful image interpretation and semantic analysis tools has the potential to significantly help in understanding biological processes, which in turn will result in improvements in drug development and healthcare.
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Affiliation(s)
- WAMIQ MANZOOR AHMED
- School of Electrical and Computer Engineering, Purdue University, 465 Northwestern Avenue, West Lafayette, IN 47907, USA
| | - MUHAMMAD NAEEM AYYAZ
- School of Electrical and Computer Engineering, Purdue University, 465 Northwestern Avenue, West Lafayette, IN 47907, USA
| | - BARTEK RAJWA
- Bindley Bioscience Center, Purdue University, 1203 West State Street, West Lafayette, IN 47907, USA
| | - FARRUKH KHAN
- Department of Computer Science, Texas Southern University, Houston, TX 77004, USA
| | - ARIF GHAFOOR
- School of Electrical and Computer Engineering, Purdue University, 465 Northwestern Avenue, West Lafayette, IN 47907, USA
| | - J. PAUL ROBINSON
- Bindley Bioscience Center, Purdue University, 1203 West State Street, West Lafayette, IN 47907, USA
- Weldon School of Biomedical Engineering, Purdue University, 206 South Intramural Drive, West Lafayette, IN 47907, USA
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190
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Zhou H, Mao KZ. Adaptive successive erosion-based cell image segmentation for p53 immunohistochemistry in bladder inverted papilloma. CONFERENCE PROCEEDINGS : ... ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL CONFERENCE 2007; 2005:6484-7. [PMID: 17281754 DOI: 10.1109/iembs.2005.1615984] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/13/2023]
Abstract
Cell nuclei segmentation is a critical issue in automatic cell analysis for cancer diagnosis and prognosis. Marker-controlled watershed segmentation algorithm is used the most commonly. In this paper, adaptive successive erosion-based (ASE) marker extraction method for watershed algorithm is presented, with the goal of extracting markers labelling each individual nucleus, including overlapping cell nuclei. Based on the new marker detection method, an integrated cell image segmentation algorithm is developed for p53 immunohistochemistry in bladder inverted papilloma. Experiments were performed on a number of images, and results demonstrate that the algorithm produces more accurate segmentation than other methods.
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Affiliation(s)
- Hao Zhou
- Sch. of Electr. & Electron. Eng., Nanyang Technol. Univ
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191
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Fernandez-Gonzalez R, de Solorzano CO. A tool for the quantitative spatial analysis of mammary gland epithelium. CONFERENCE PROCEEDINGS : ... ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL CONFERENCE 2007; 2004:1549-52. [PMID: 17271993 DOI: 10.1109/iembs.2004.1403473] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
In this paper we present a method for the spatial analysis of complex cellular systems based on a multiscale study of neighborhood relationships. A function to measure those relationships, M, is introduced. The refined relative neighborhood graph is then presented as a method to establish vicinity relationships within layered cellular structures, and particularized to epithelial cell nuclei in the mammary gland. Finally, the method is illustrated with two examples that show interactions within one population of epithelial cells and between two different populations.
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192
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Crosta GF, Fumarola L, Malerba I, Gribaldo L. Scoring CFU-GM colonies in vitro by data fusion: A first account. Exp Hematol 2007; 35:1-12. [PMID: 17198868 DOI: 10.1016/j.exphem.2006.08.015] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2006] [Revised: 08/11/2006] [Accepted: 08/21/2006] [Indexed: 11/19/2022]
Abstract
OBJECTIVE In vitro models of hematopoiesis used in investigative hematopathology and in safety studies on candidate drugs, involve clonogenic assays on colony-forming unit granulocyte macrophage (CFU-GM). These assays require live and unstained colonies to be counted. Most laboratories still rely on visual scoring, which is time-consuming and error-prone. As a consequence, automated scoring is highly desired. An algorithm that recognizes and scores CFU-GM colonies by data fusion has been developed. Some preliminary results are presented in this article. METHODS CFU-GM assays were carried out on hematopoietic progenitors (human umbilical cord blood cells) grown in methylcellulose. Colony images were acquired by a digital camera and stored. RESULTS The classifier was designed to process images of layers sampled from a three-dimensional (3D) domain and forming a stack. Structure and texture information was extracted from each image. Classifier training was based on a 3D colony model applied to the image stack. The number of scored colonies (assigned class) was required to match the count supplied by the human expert (class of belonging). The trained classifier was validated on one more stack and then applied to a stack with overlapping colonies. Scoring in distortion- and caustic-affected border areas was also successfully demonstrated. Because of hardware limitations, compact colonies in some cases were missed. CONCLUSIONS The industry's scoring methods all rely on structure alone and process 2D data. Instead, the classifier here fuses data from a whole stack and is capable, in principle, of high-throughput screening.
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Affiliation(s)
- Giovanni F Crosta
- Inverse Problems and Mathematical Morphology Unit, Department of Environmental Sciences, Università degli Studi Milano-Bicocca, Milano, Italy.
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193
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194
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Yan J, Zhao B, Wang L, Zelenetz A, Schwartz LH. Marker-controlled watershed for lymphoma segmentation in sequential CT images. Med Phys 2006; 33:2452-60. [PMID: 16898448 DOI: 10.1118/1.2207133] [Citation(s) in RCA: 58] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Abstract
Segmentation of lymphoma containing lymph nodes is a difficult task because of multiple variables associated with the tumor's location, intensity distribution, and contrast to its surrounding tissues. In this paper, we present a reliable and practical marker-controlled watershed algorithm for semi-automated segmentation of lymphoma in sequential CT images. Robust determination of internal and external markers is the key to successful use of the marker-controlled watershed transform in the segmentation of lymphoma and is the focus of this work. The external marker in our algorithm is the circle enclosing the lymphoma in a single slice. The internal marker, however, is determined automatically by combining techniques including Canny edge detection, thresholding, morphological operation, and distance map estimation. To obtain tumor volume, the segmented lymphoma in the current slice needs to be propagated to the adjacent slice to help determine the external and internal markers for delineation of the lymphoma in that slice. The algorithm was applied to 29 lymphomas (size range, 9-53 mm in diameter; mean, 23 mm) in nine patients. A blinded radiologist manually delineated all lymphomas on all slices. The manual result served as the "gold standard" for comparison. Several quantitative methods were applied to objectively evaluate the performance of the segmentation algorithm. The algorithm received a mean overlap, overestimation, and underestimation ratios of 83.2%, 13.5%, and 5.5%, respectively. The mean average boundary distance and Hausdorff boundary distance were 0.7 and 3.7 mm. Preliminary results have shown the potential of this computer algorithm to allow reliable segmentation and quantification of lymphomas on sequential CT images.
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Affiliation(s)
- Jiayong Yan
- Medical Physics Department, Memorial Sloan-Kettering Cancer Center, 1275 York Avenue, New York, New York 10021, USA.
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195
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Xiong G, Zhou X, Ji L. Automated Segmentation of Drosophila RNAi Fluorescence Cellular Images Using Deformable Models. ACTA ACUST UNITED AC 2006. [DOI: 10.1109/tcsi.2006.884461] [Citation(s) in RCA: 39] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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196
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Carpenter AE, Jones TR, Lamprecht MR, Clarke C, Kang IH, Friman O, Guertin DA, Chang JH, Lindquist RA, Moffat J, Golland P, Sabatini DM. CellProfiler: image analysis software for identifying and quantifying cell phenotypes. Genome Biol 2006; 7:R100. [PMID: 17076895 PMCID: PMC1794559 DOI: 10.1186/gb-2006-7-10-r100] [Citation(s) in RCA: 3588] [Impact Index Per Article: 199.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2006] [Accepted: 10/31/2006] [Indexed: 11/26/2022] Open
Abstract
CellProfiler, the first free, open-source system for flexible and high-throughput cell image analysis is described. Biologists can now prepare and image thousands of samples per day using automation, enabling chemical screens and functional genomics (for example, using RNA interference). Here we describe the first free, open-source system designed for flexible, high-throughput cell image analysis, CellProfiler. CellProfiler can address a variety of biological questions quantitatively, including standard assays (for example, cell count, size, per-cell protein levels) and complex morphological assays (for example, cell/organelle shape or subcellular patterns of DNA or protein staining).
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Affiliation(s)
- Anne E Carpenter
- Whitehead Institute for Biomedical Research, Cambridge, MA 02142, USA
| | - Thouis R Jones
- Whitehead Institute for Biomedical Research, Cambridge, MA 02142, USA
- Computer Sciences and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02142, USA
| | | | - Colin Clarke
- Whitehead Institute for Biomedical Research, Cambridge, MA 02142, USA
- Computer Sciences and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02142, USA
| | - In Han Kang
- Computer Sciences and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02142, USA
| | - Ola Friman
- Department of Radiology, Brigham and Women's Hospital, Boston, MA 02115, USA
| | - David A Guertin
- Whitehead Institute for Biomedical Research, Cambridge, MA 02142, USA
| | - Joo Han Chang
- Whitehead Institute for Biomedical Research, Cambridge, MA 02142, USA
| | | | - Jason Moffat
- Whitehead Institute for Biomedical Research, Cambridge, MA 02142, USA
| | - Polina Golland
- Computer Sciences and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02142, USA
| | - David M Sabatini
- Whitehead Institute for Biomedical Research, Cambridge, MA 02142, USA
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02142, USA
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197
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Selinummi J, Sarkanen JR, Niemistö A, Linne ML, Ylikomi T, Yli-Harja O, Jalonen TO. Quantification of vesicles in differentiating human SH-SY5Y neuroblastoma cells by automated image analysis. Neurosci Lett 2006; 396:102-7. [PMID: 16356645 DOI: 10.1016/j.neulet.2005.11.021] [Citation(s) in RCA: 13] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2005] [Revised: 09/26/2005] [Accepted: 11/08/2005] [Indexed: 11/23/2022]
Abstract
A new automated image analysis method for quantification of fluorescent dots is presented. This method facilitates counting the number of fluorescent puncta in specific locations of individual cells and also enables estimation of the number of cells by detecting the labeled nuclei. The method is here used for counting the AM1-43 labeled fluorescent puncta in human SH-SY5Y neuroblastoma cells induced to differentiate with all-trans retinoic acid (RA), and further stimulated with high potassium (K+) containing solution. The automated quantification results correlate well with the results obtained manually through visual inspection. The manual method has the disadvantage of being slow, labor-intensive, and subjective, and the results may not be reproducible even in the intra-observer case. The automated method, however, has the advantage of allowing fast quantification with explicitly defined methods, with no user intervention. This ensures objectivity of the quantification. In addition to the number of fluorescent dots, further development of the method allows its use for quantification of several other parameters, such as intensity, size, and shape of the puncta, that are difficult to quantify manually.
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Affiliation(s)
- Jyrki Selinummi
- Institute of Signal Processing, Tampere University of Technology, FIN-33101 Tampere, Finland.
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198
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Lin G, Bjornsson CS, Smith KL, Abdul-Karim MA, Turner JN, Shain W, Roysam B. Automated image analysis methods for 3-D quantification of the neurovascular unit from multichannel confocal microscope images. Cytometry A 2006; 66:9-23. [PMID: 15934061 DOI: 10.1002/cyto.a.20149] [Citation(s) in RCA: 25] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
BACKGROUND There is a need for integrative and quantitative methods to investigate the structural and functional relations among elements of complex systems, such as the neurovascular unit (NVU), that involve multiple cell types, microvasculatures, and various genomic/proteomic/ionic functional entities. METHODS Vascular casting and selective labeling enabled simultaneous three-dimensional imaging of the microvasculature, cell nuclei, and cytoplasmic stains. Multidimensional segmentation was achieved by (i) bleed-through removal and attenuation correction; (ii) independent segmentation and morphometry for each corrected channel; and (iii) spatially associative feature computation across channels. The combined measurements enabled cell classification based on nuclear morphometry, cytoplasmic signals, and distance from vascular elements. Specific spatial relations among the NVU elements could be quantified. RESULTS A software system combining nuclear and vessel segmentation codes and associative features was constructed and validated. Biological variability contributed to misidentified nuclei (9.3%), undersegmentation of nuclei (3.7%), hypersegmentation of nuclei (14%), and missed nuclei (4.7%). Microvessel segmentation errors occurred rarely, mainly due to nonuniform lumen staining. CONCLUSIONS Associative features across fluorescence channels, in combination with standard features, enable integrative structural and functional analysis of the NVU. By labeling additional structural and functional entities, this method can be scaled up to larger-scale systems biology studies that integrate spatial and molecular information.
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Affiliation(s)
- Gang Lin
- Department of Electrical, Computer, and Systems Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180-3590, New York, USA
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199
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Towards Safer, Faster Prenatal Genetic Tests: Novel Unsupervised, Automatic and Robust Methods of Segmentation of Nuclei and Probes. ACTA ACUST UNITED AC 2006. [DOI: 10.1007/11744085_34] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register]
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200
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Arámbula Cosío F, Márquez Flores JA, Padilla Castañeda MA, Solano S, Tato P. Automatic analysis of immunocytochemically stained tissue samples. Med Biol Eng Comput 2005; 43:672-7. [PMID: 16411641 DOI: 10.1007/bf02351042] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Abstract
An automatic colour image segmentation and cell counting software system has been developed for immunocytochemical analysis of stained tissue samples. The system was designed to count the total number of positive and negative cells in tissue samples treated with cytokine DNA probes from pigs naturally parasitised with Taenia solium metacestodes, using in situ hybridisation. A reaction index was calculated as the ratio of the number of cells with a positive reaction to the total number of cells (positives plus negatives) for each of five different probes. The objectives of automatic counting were to improve the reproducibility of the analysis and reduce the processing time of large image batches. A fast KNN classifier was used for colour segmentation. Watershed segmentation combined with edge detection was used to isolate individual cells that were then automatically labelled, using the results of the corresponding colour segmented image. Validation was performed on 122 non-training digital images with a total of 1069 positive cells and 1459 negative cells, with the following results: a mean true positive rate of 90.2% for positive cells and a mean true positive rate of 85.4% for negative cells. The corresponding mean false positive rates were 9.6% and 6.6%. The mean reaction index error of the automatic analysis was 5.35%. The processing of each digital image took 10 s on a Pentium IV PC.
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