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IFN-γ-response mediator GBP-1 represses human cell proliferation by inhibiting the Hippo signaling transcription factor TEAD. Biochem J 2018; 475:2955-2967. [PMID: 30120107 PMCID: PMC6156764 DOI: 10.1042/bcj20180123] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2018] [Revised: 08/03/2018] [Accepted: 08/17/2018] [Indexed: 12/28/2022]
Abstract
Interferon-gamma (IFN-γ) is a pleiotropic cytokine that exerts important functions in inflammation, infectious diseases, and cancer. The large GTPase human guanylate-binding protein 1 (GBP-1) is among the most strongly IFN-γ-induced cellular proteins. Previously, it has been shown that GBP-1 mediates manifold cellular responses to IFN-γ including the inhibition of proliferation, spreading, migration, and invasion and through this exerts anti-tumorigenic activity. However, the mechanisms of GBP-1 anti-tumorigenic activities remain poorly understood. Here, we elucidated the molecular mechanism of the human GBP-1-mediated suppression of proliferation by demonstrating for the first time a cross-talk between the anti-tumorigenic IFN-γ and Hippo pathways. The α9-helix of GBP-1 was found to be sufficient to inhibit proliferation. Protein-binding and molecular modeling studies revealed that the α9-helix binds to the DNA-binding domain of the Hippo signaling transcription factor TEA domain protein (TEAD) mediated by the 376VDHLFQK382 sequence at the N-terminus of the GBP-1-α9-helix. Mutation of this sequence resulted in abrogation of both TEAD interaction and suppression of proliferation. Further on, the interaction caused inhibition of TEAD transcriptional activity associated with the down-regulation of TEAD-target genes. In agreement with these results, IFN-γ treatment of the cells also impaired TEAD activity, and this effect was abrogated by siRNA-mediated inhibition of GBP-1 expression. Altogether, this demonstrated that the α9-helix is the proliferation inhibitory domain of GBP-1, which acts independent of the GTPase activity through the inhibition of the Hippo transcription factor TEAD in mediating the anti-proliferative cell response to IFN-γ.
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Detection and Classification of Overlapping Cell Nuclei in Cytology Effusion Images Using a Double-Strategy Random Forest. APPLIED SCIENCES-BASEL 2018. [DOI: 10.3390/app8091608] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Due to the close resemblance between overlapping and cancerous nuclei, the misinterpretation of overlapping nuclei can affect the final decision of cancer cell detection. Thus, it is essential to detect overlapping nuclei and distinguish them from single ones for subsequent quantitative analyses. This paper presents a method for the automated detection and classification of overlapping nuclei from single nuclei appearing in cytology pleural effusion (CPE) images. The proposed system is comprised of three steps: nuclei candidate extraction, dominant feature extraction, and classification of single and overlapping nuclei. A maximum entropy thresholding method complemented by image enhancement and post-processing was employed for nuclei candidate extraction. For feature extraction, a new combination of 16 geometrical and 10 textural features was extracted from each nucleus region. A double-strategy random forest was performed as an ensemble feature selector to select the most relevant features, and an ensemble classifier to differentiate between overlapping nuclei and single ones using selected features. The proposed method was evaluated on 4000 nuclei from CPE images using various performance metrics. The results were 96.6% sensitivity, 98.7% specificity, 92.7% precision, 94.6% F1 score, 98.4% accuracy, 97.6% G-mean, and 99% area under curve. The computation time required to run the entire algorithm was just 5.17 s. The experiment results demonstrate that the proposed algorithm yields a superior performance to previous studies and other classifiers. The proposed algorithm can serve as a new supportive tool in the automated diagnosis of cancer cells from cytology images.
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53
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Todorov H, Saeys Y. Computational approaches for high‐throughput single‐cell data analysis. FEBS J 2018; 286:1451-1467. [DOI: 10.1111/febs.14613] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2018] [Revised: 06/04/2018] [Accepted: 07/25/2018] [Indexed: 12/31/2022]
Affiliation(s)
- Helena Todorov
- Data Mining and Modelling for Biomedicine VIB Center for Inflammation Research Ghent Belgium
- Department of Applied Mathematics, Computer Science and Statistics Ghent University Belgium
- Centre International de Recherche en Infectiologie Inserm U1111, Université Claude Bernard Lyon 1 CNRS, UMR5308 École Normale Supérieure de Lyon Univ Lyon France
| | - Yvan Saeys
- Data Mining and Modelling for Biomedicine VIB Center for Inflammation Research Ghent Belgium
- Department of Applied Mathematics, Computer Science and Statistics Ghent University Belgium
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Knötel D, Seidel R, Prohaska S, Dean MN, Baum D. Automated segmentation of complex patterns in biological tissues: Lessons from stingray tessellated cartilage. PLoS One 2017; 12:e0188018. [PMID: 29236705 PMCID: PMC5728489 DOI: 10.1371/journal.pone.0188018] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2017] [Accepted: 10/29/2017] [Indexed: 11/18/2022] Open
Abstract
Introduction Many biological structures show recurring tiling patterns on one structural level or the other. Current image acquisition techniques are able to resolve those tiling patterns to allow quantitative analyses. The resulting image data, however, may contain an enormous number of elements. This renders manual image analysis infeasible, in particular when statistical analysis is to be conducted, requiring a larger number of image data to be analyzed. As a consequence, the analysis process needs to be automated to a large degree. In this paper, we describe a multi-step image segmentation pipeline for the automated segmentation of the calcified cartilage into individual tesserae from computed tomography images of skeletal elements of stingrays. Methods Besides applying state-of-the-art algorithms like anisotropic diffusion smoothing, local thresholding for foreground segmentation, distance map calculation, and hierarchical watershed, we exploit a graph-based representation for fast correction of the segmentation. In addition, we propose a new distance map that is computed only in the plane that locally best approximates the calcified cartilage. This distance map drastically improves the separation of individual tesserae. We apply our segmentation pipeline to hyomandibulae from three individuals of the round stingray (Urobatis halleri), varying both in age and size. Results Each of the hyomandibula datasets contains approximately 3000 tesserae. To evaluate the quality of the automated segmentation, four expert users manually generated ground truth segmentations of small parts of one hyomandibula. These ground truth segmentations allowed us to compare the segmentation quality w.r.t. individual tesserae. Additionally, to investigate the segmentation quality of whole skeletal elements, landmarks were manually placed on all tesserae and their positions were then compared to the segmented tesserae. With the proposed segmentation pipeline, we sped up the processing of a single skeletal element from days or weeks to a few hours.
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Affiliation(s)
- David Knötel
- Zuse Institute Berlin, Dept. of Visual Data Analysis, Berlin, Germany
- * E-mail:
| | - Ronald Seidel
- Max Planck Institute of Colloids and Interfaces, Dept. of Biomaterials, Potsdam-Golm, Germany
| | - Steffen Prohaska
- Zuse Institute Berlin, Dept. of Visual Data Analysis, Berlin, Germany
| | - Mason N. Dean
- Max Planck Institute of Colloids and Interfaces, Dept. of Biomaterials, Potsdam-Golm, Germany
| | - Daniel Baum
- Zuse Institute Berlin, Dept. of Visual Data Analysis, Berlin, Germany
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55
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Shuvaev SA, Lazutkin AA, Kedrov AV, Anokhin KV, Enikolopov GN, Koulakov AA. DALMATIAN: An Algorithm for Automatic Cell Detection and Counting in 3D. Front Neuroanat 2017; 11:117. [PMID: 29311849 PMCID: PMC5732941 DOI: 10.3389/fnana.2017.00117] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2017] [Accepted: 11/27/2017] [Indexed: 01/09/2023] Open
Abstract
Current 3D imaging methods, including optical projection tomography, light-sheet microscopy, block-face imaging, and serial two photon tomography enable visualization of large samples of biological tissue. Large volumes of data obtained at high resolution require development of automatic image processing techniques, such as algorithms for automatic cell detection or, more generally, point-like object detection. Current approaches to automated cell detection suffer from difficulties originating from detection of particular cell types, cell populations of different brightness, non-uniformly stained, and overlapping cells. In this study, we present a set of algorithms for robust automatic cell detection in 3D. Our algorithms are suitable for, but not limited to, whole brain regions and individual brain sections. We used watershed procedure to split regional maxima representing overlapping cells. We developed a bootstrap Gaussian fit procedure to evaluate the statistical significance of detected cells. We compared cell detection quality of our algorithm and other software using 42 samples, representing 6 staining and imaging techniques. The results provided by our algorithm matched manual expert quantification with signal-to-noise dependent confidence, including samples with cells of different brightness, non-uniformly stained, and overlapping cells for whole brain regions and individual tissue sections. Our algorithm provided the best cell detection quality among tested free and commercial software.
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Affiliation(s)
- Sergey A Shuvaev
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, United States.,Brain Stem Cell Laboratory, NBIC, Moscow Institute of Physics and Technology, Moscow, Russia
| | - Alexander A Lazutkin
- Brain Stem Cell Laboratory, NBIC, Moscow Institute of Physics and Technology, Moscow, Russia.,Center for Developmental Genetics and Department of Anesthesiology, Stony Brook University, Stony Brook, NY, United States.,P.K. Anokhin Institute of Normal Physiology, Moscow, Russia
| | - Alexander V Kedrov
- Brain Stem Cell Laboratory, NBIC, Moscow Institute of Physics and Technology, Moscow, Russia.,P.K. Anokhin Institute of Normal Physiology, Moscow, Russia
| | - Konstantin V Anokhin
- P.K. Anokhin Institute of Normal Physiology, Moscow, Russia.,National Research Center "Kurchatov Institute", Moscow, Russia
| | - Grigori N Enikolopov
- Brain Stem Cell Laboratory, NBIC, Moscow Institute of Physics and Technology, Moscow, Russia.,Center for Developmental Genetics and Department of Anesthesiology, Stony Brook University, Stony Brook, NY, United States
| | - Alexei A Koulakov
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, United States
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56
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Systems pathology by multiplexed immunohistochemistry and whole-slide digital image analysis. Sci Rep 2017; 7:15580. [PMID: 29138507 PMCID: PMC5686230 DOI: 10.1038/s41598-017-15798-4] [Citation(s) in RCA: 104] [Impact Index Per Article: 14.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2017] [Accepted: 11/02/2017] [Indexed: 12/11/2022] Open
Abstract
The paradigm of molecular histopathology is shifting from a single-marker immunohistochemistry towards multiplexed detection of markers to better understand the complex pathological processes. However, there are no systems allowing multiplexed IHC (mIHC) with high-resolution whole-slide tissue imaging and analysis, yet providing feasible throughput for routine use. We present an mIHC platform combining fluorescent and chromogenic staining with automated whole-slide imaging and integrated whole-slide image analysis, enabling simultaneous detection of six protein markers and nuclei, and automatic quantification and classification of hundreds of thousands of cells in situ in formalin-fixed paraffin-embedded tissues. In the first proof-of-concept, we detected immune cells at cell-level resolution (n = 128,894 cells) in human prostate cancer, and analysed T cell subpopulations in different tumour compartments (epithelium vs. stroma). In the second proof-of-concept, we demonstrated an automatic classification of epithelial cell populations (n = 83,558) and glands (benign vs. cancer) in prostate cancer with simultaneous analysis of androgen receptor (AR) and alpha-methylacyl-CoA (AMACR) expression at cell-level resolution. We conclude that the open-source combination of 8-plex mIHC detection, whole-slide image acquisition and analysis provides a robust tool allowing quantitative, spatially resolved whole-slide tissue cytometry directly in formalin-fixed human tumour tissues for improved characterization of histology and the tumour microenvironment.
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57
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Ulman V, Maška M, Magnusson KEG, Ronneberger O, Haubold C, Harder N, Matula P, Matula P, Svoboda D, Radojevic M, Smal I, Rohr K, Jaldén J, Blau HM, Dzyubachyk O, Lelieveldt B, Xiao P, Li Y, Cho SY, Dufour AC, Olivo-Marin JC, Reyes-Aldasoro CC, Solis-Lemus JA, Bensch R, Brox T, Stegmaier J, Mikut R, Wolf S, Hamprecht FA, Esteves T, Quelhas P, Demirel Ö, Malmström L, Jug F, Tomancak P, Meijering E, Muñoz-Barrutia A, Kozubek M, Ortiz-de-Solorzano C. An objective comparison of cell-tracking algorithms. Nat Methods 2017; 14:1141-1152. [PMID: 29083403 PMCID: PMC5777536 DOI: 10.1038/nmeth.4473] [Citation(s) in RCA: 221] [Impact Index Per Article: 31.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2017] [Accepted: 09/23/2017] [Indexed: 01/17/2023]
Abstract
We present a combined report on the results of three editions of the Cell Tracking Challenge, an ongoing initiative aimed at promoting the development and objective evaluation of cell segmentation and tracking algorithms. With 21 participating algorithms and a data repository consisting of 13 data sets from various microscopy modalities, the challenge displays today's state-of-the-art methodology in the field. We analyzed the challenge results using performance measures for segmentation and tracking that rank all participating methods. We also analyzed the performance of all of the algorithms in terms of biological measures and practical usability. Although some methods scored high in all technical aspects, none obtained fully correct solutions. We found that methods that either take prior information into account using learning strategies or analyze cells in a global spatiotemporal video context performed better than other methods under the segmentation and tracking scenarios included in the challenge.
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Affiliation(s)
- Vladimír Ulman
- Centre for Biomedical Image Analysis, Masaryk University, Brno, Czech Republic
| | - Martin Maška
- Centre for Biomedical Image Analysis, Masaryk University, Brno, Czech Republic
| | - Klas E G Magnusson
- ACCESS Linnaeus Centre, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Olaf Ronneberger
- Computer Science Department and BIOSS Centre for Biological Signaling Studies University of Freiburg, Frieburg, Germany
| | - Carsten Haubold
- Heidelberg Collaboratory for Image Processing, IWR, University of Heidelberg, Heidelberg, Germany
| | - Nathalie Harder
- Biomedical Computer Vision Group, Department of Bioinformatics and Functional Genomics, BIOQUANT, IPMB, University of Heidelberg and DKFZ, Heidelberg, Germany
| | - Pavel Matula
- Centre for Biomedical Image Analysis, Masaryk University, Brno, Czech Republic
| | - Petr Matula
- Centre for Biomedical Image Analysis, Masaryk University, Brno, Czech Republic
| | - David Svoboda
- Centre for Biomedical Image Analysis, Masaryk University, Brno, Czech Republic
| | - Miroslav Radojevic
- Biomedical Imaging Group Rotterdam, Departments of Medical Informatics and Radiology, Erasmus University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Ihor Smal
- Biomedical Imaging Group Rotterdam, Departments of Medical Informatics and Radiology, Erasmus University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Karl Rohr
- Biomedical Computer Vision Group, Department of Bioinformatics and Functional Genomics, BIOQUANT, IPMB, University of Heidelberg and DKFZ, Heidelberg, Germany
| | - Joakim Jaldén
- ACCESS Linnaeus Centre, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Helen M Blau
- Baxter Laboratory for Stem Cell Biology, Department of Microbiology and Immunology, and Institute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, Stanford, California, USA
| | - Oleh Dzyubachyk
- Division of Image Processing, Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands
| | - Boudewijn Lelieveldt
- Division of Image Processing, Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands.,Intelligent Systems Department, Delft University of Technology, Delft, the Netherlands
| | - Pengdong Xiao
- Institute of Molecular and Cell Biology, A*Star, Singapore
| | - Yuexiang Li
- Department of Engineering, University of Nottingham, Nottingham, UK
| | - Siu-Yeung Cho
- Faculty of Engineering, University of Nottingham, Ningbo, China
| | | | | | - Constantino C Reyes-Aldasoro
- Research Centre in Biomedical Engineering, School of Mathematics, Computer Science and Engineering, City University of London, London, UK
| | - Jose A Solis-Lemus
- Research Centre in Biomedical Engineering, School of Mathematics, Computer Science and Engineering, City University of London, London, UK
| | - Robert Bensch
- Computer Science Department and BIOSS Centre for Biological Signaling Studies University of Freiburg, Frieburg, Germany
| | - Thomas Brox
- Computer Science Department and BIOSS Centre for Biological Signaling Studies University of Freiburg, Frieburg, Germany
| | - Johannes Stegmaier
- Group for Automated Image and Data Analysis, Institute for Applied Computer Science, Karlsruhe Institute of Technology, Eggenstein-Leopoldshafen, Germany
| | - Ralf Mikut
- Group for Automated Image and Data Analysis, Institute for Applied Computer Science, Karlsruhe Institute of Technology, Eggenstein-Leopoldshafen, Germany
| | - Steffen Wolf
- Heidelberg Collaboratory for Image Processing, IWR, University of Heidelberg, Heidelberg, Germany
| | - Fred A Hamprecht
- Heidelberg Collaboratory for Image Processing, IWR, University of Heidelberg, Heidelberg, Germany
| | - Tiago Esteves
- i3S - Instituto de Investigação e Inovação em Saúde, Universidade do Porto, Porto, Portugal.,Facultade de Engenharia, Universidade do Porto, Porto, Portugal
| | - Pedro Quelhas
- i3S - Instituto de Investigação e Inovação em Saúde, Universidade do Porto, Porto, Portugal
| | | | | | - Florian Jug
- Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany
| | - Pavel Tomancak
- Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany
| | - Erik Meijering
- Biomedical Imaging Group Rotterdam, Departments of Medical Informatics and Radiology, Erasmus University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Arrate Muñoz-Barrutia
- Bioengineering and Aerospace Engineering Department, Universidad Carlos III de Madrid, Getafe, Spain.,Instituto de Investigación Sanitaria Gregorio Marañon, Madrid, Spain
| | - Michal Kozubek
- Centre for Biomedical Image Analysis, Masaryk University, Brno, Czech Republic
| | - Carlos Ortiz-de-Solorzano
- CIBERONC, IDISNA and Program of Solid Tumors and Biomarkers, Center for Applied Medical Research, University of Navarra, Pamplona, Spain.,Bioengineering Department, TECNUN School of Engineering, University of Navarra, San Sebastián, Spain
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58
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SAHA M, ARUN I, AGARWAL S, AHMED R, CHATTERJEE S, CHAKRABORTY C. Imprint cytology-based breast malignancy screening: an efficient nuclei segmentation technique. J Microsc 2017; 268:155-171. [DOI: 10.1111/jmi.12595] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2016] [Revised: 04/26/2017] [Accepted: 05/29/2017] [Indexed: 12/20/2022]
Affiliation(s)
- M. SAHA
- School of Medical Science & Technology; Indian Institute of Technology; Kharagpur India
| | - I. ARUN
- Tata Medical Center; New Town Rajarhat Kolkata India
| | - S. AGARWAL
- Tata Medical Center; New Town Rajarhat Kolkata India
| | - R. AHMED
- Tata Medical Center; New Town Rajarhat Kolkata India
| | - S. CHATTERJEE
- Tata Medical Center; New Town Rajarhat Kolkata India
| | - C. CHAKRABORTY
- School of Medical Science & Technology; Indian Institute of Technology; Kharagpur India
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59
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Paulik R, Micsik T, Kiszler G, Kaszál P, Székely J, Paulik N, Várhalmi E, Prémusz V, Krenács T, Molnár B. An optimized image analysis algorithm for detecting nuclear signals in digital whole slides for histopathology. Cytometry A 2017; 91:595-608. [DOI: 10.1002/cyto.a.23124] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2016] [Revised: 03/08/2017] [Accepted: 03/28/2017] [Indexed: 11/11/2022]
Affiliation(s)
| | - Tamás Micsik
- 1st Department of Pathology and Experimental Cancer Research; Semmelweis University; Budapest Hungary
| | | | | | | | | | | | | | - Tibor Krenács
- 1st Department of Pathology and Experimental Cancer Research; Semmelweis University; Budapest Hungary
| | - Béla Molnár
- Clinical Gastroenterology Research Unit; Hungarian Academy of Sciences; Budapest Hungary
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60
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Quantitative 3D analysis of complex single border cell behaviors in coordinated collective cell migration. Nat Commun 2017; 8:14905. [PMID: 28374738 PMCID: PMC5382290 DOI: 10.1038/ncomms14905] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2015] [Accepted: 02/10/2017] [Indexed: 11/08/2022] Open
Abstract
Understanding the mechanisms of collective cell migration is crucial for cancer metastasis, wound healing and many developmental processes. Imaging a migrating cluster in vivo is feasible, but the quantification of individual cell behaviours remains challenging. We have developed an image analysis toolkit, CCMToolKit, to quantify the Drosophila border cell system. In addition to chaotic motion, previous studies reported that the migrating cells are able to migrate in a highly coordinated pattern. We quantify the rotating and running migration modes in 3D while also observing a range of intermediate behaviours. Running mode is driven by cluster external protrusions. Rotating mode is associated with cluster internal cell extensions that could not be easily characterized. Although the cluster moves slower while rotating, individual cells retain their mobility and are in fact slightly more active than in running mode. We also show that individual cells may exchange positions during migration.
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61
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Using simulated fluorescence cell micrographs for the evaluation of cell image segmentation algorithms. BMC Bioinformatics 2017; 18:176. [PMID: 28315633 PMCID: PMC5357336 DOI: 10.1186/s12859-017-1591-2] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2016] [Accepted: 03/09/2017] [Indexed: 11/29/2022] Open
Abstract
Background 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. Results 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. Conclusion 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. Electronic supplementary material The online version of this article (doi:10.1186/s12859-017-1591-2) contains supplementary material, which is available to authorized users.
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62
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Kelly JG, Hawken MJ. Quantification of neuronal density across cortical depth using automated 3D analysis of confocal image stacks. Brain Struct Funct 2017; 222:3333-3353. [PMID: 28243763 DOI: 10.1007/s00429-017-1382-6] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2016] [Accepted: 01/31/2017] [Indexed: 10/20/2022]
Abstract
A new framework for measuring densities of immunolabeled neurons across cortical layers was implemented that combines a confocal microscopy sampling strategy with automated analysis of 3D image stacks. Its utility was demonstrated by quantifying neuronal density in macaque cortical areas V1 and V2. A series of overlapping confocal image stacks were acquired, each spanning from the pial surface to the white matter. DAPI channel images were automatically thresholded, and contiguous regions that included multiple clumped nuclear profiles were split using k-means clustering of image pixels for a set of candidate k values determined based on the clump's area; the most likely candidate segmentation was selected based on criteria that capture expected nuclear profile shape and size. The centroids of putative nuclear profiles estimated from 2D images were then grouped across z planes in an image stack to identify the positions of nuclei in x-y-z. 3D centroids falling outside user-specified exclusion boundaries were deleted, nuclei were classified by the presence or absence of signal in a channel corresponding to an immunolabeled antigen (e.g., the pan-neuronal marker NeuN) at the nuclear centroid location, and the set of classified cells was combined across image stacks to estimate density across cortical depth. The method was validated by comparison with conventional stereological methods. The average neuronal density across cortical layers was 230 × 103 neurons per mm3 in V1 and 130 × 103 neurons per mm3 in V2. The method is accurate, flexible, and general enough to measure densities of neurons of various molecularly identified types.
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Affiliation(s)
- Jenna G Kelly
- Center for Neural Science, New York University, 4 Washington Place, New York, NY, 10003, USA
| | - Michael J Hawken
- Center for Neural Science, New York University, 4 Washington Place, New York, NY, 10003, USA.
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63
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Attayek PJ, Hunsucker SA, Sims CE, Allbritton NL, Armistead PM. Identification and isolation of antigen-specific cytotoxic T lymphocytes with an automated microraft sorting system. Integr Biol (Camb) 2016; 8:1208-1220. [PMID: 27853786 PMCID: PMC5138107 DOI: 10.1039/c6ib00168h] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
The simultaneous measurement of T cell function with recovery of individual T cells would greatly facilitate characterizing antigen-specific responses both in vivo and in model systems. We have developed a microraft array methodology that automatically measures the ability of individual T cells to kill a population of target cells and viably sorts specific cells into a 96-well plate for expansion. A human T cell culture was generated against the influenza M1p antigen. Individual microrafts on a 70 × 70 array were loaded with on average 1 CD8+ cell from the culture and a population of M1p presenting target cells. Target cell killing, measured by fluorescence microscopy, was quantified in each microraft. The rates of target cell death among the individual CD8+ T cells varied greatly; however, individual T cells maintained their rates of cytotoxicity throughout the time course of the experiment enabling rapid identification of highly cytotoxic CD8+ T cells. Microrafts with highly active CD8+ T cells were individually transferred to wells of a 96-well plate, using a needle-release device coupled to the microscope. Three sorted T cells clonally expanded. All of these expressed high-avidity T cell receptors for M1p/HLA*02:01 tetramers, and 2 of the 3 receptors were sequenced. While this study investigated single T cell cytotoxicity rates against simple targets with subsequent cell sorting, future studies will involve measuring T cell mediated cytotoxicity in more complex cellular environments, enlarging the arrays to identify very rare antigen specific T cells, and measuring single cell CD4+ and CD8+ T cell proliferation.
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Affiliation(s)
- Peter J. Attayek
- Department of Biomedical Engineering, University of North Carolina, Chapel Hill NC and North Carolina State University, Raleigh NC
| | - Sally A. Hunsucker
- Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, NC
| | - Christopher E. Sims
- Department of Chemistry, University of North Carolina, Chapel Hill, NC
- Department of Medicine, University of North Carolina, Chapel Hill, NC
| | - Nancy L. Allbritton
- Department of Biomedical Engineering, University of North Carolina, Chapel Hill NC and North Carolina State University, Raleigh NC
- Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, NC
- Department of Chemistry, University of North Carolina, Chapel Hill, NC
| | - Paul M. Armistead
- Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, NC
- Department of Medicine, University of North Carolina, Chapel Hill, NC
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64
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Kaliman S, Jayachandran C, Rehfeldt F, Smith AS. Limits of Applicability of the Voronoi Tessellation Determined by Centers of Cell Nuclei to Epithelium Morphology. Front Physiol 2016; 7:551. [PMID: 27932987 PMCID: PMC5122581 DOI: 10.3389/fphys.2016.00551] [Citation(s) in RCA: 35] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2016] [Accepted: 11/03/2016] [Indexed: 01/13/2023] Open
Abstract
It is well accepted that cells in the tissue can be regarded as tiles tessellating space. A number of approaches were developed to find an appropriate mathematical description of such cell tiling. A particularly useful approach is the so called Voronoi tessellation, built from centers of mass of the cell nuclei (CMVT), which is commonly used for estimating the morphology of cells in epithelial tissues. However, a study providing a statistically sound analysis of this method's accuracy is not available in the literature. We addressed this issue here by comparing a number of morphological measures of the cells, including area, perimeter, and elongation obtained from such a tessellation with identical measures extracted from direct imaging acquired by staining the cell membranes. After analyzing the shapes of 15,000 MDCK II epithelial cells under several conditions, we find that CMVT reasonably well reproduces many of the morphological properties of the tissue with an error that is between 10 and 15%. Moreover, cross-correlations between different morphological measures are reproduced qualitatively correctly by this method. However, all of the properties including the cell perimeters, number of neighbors, and anisotropy measures often suffer from systematic or size dependent errors. These discrepancies originate from the polygonal nature of the tessellation which sets the limits of the applicability of CMVT.
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Affiliation(s)
- Sara Kaliman
- Physics Underlying Life Sciences Group, Institute for Theoretical Physics and Cluster of Excellence: Engineering of Advanced Materials, Friedrich Alexander University Erlangen-Nürnberg Erlangen, Germany
| | | | - Florian Rehfeldt
- Third Institute of Physics-Biophysics, Georg-August-University Göttingen, Germany
| | - Ana-Sunčana Smith
- Physics Underlying Life Sciences Group, Institute for Theoretical Physics and Cluster of Excellence: Engineering of Advanced Materials, Friedrich Alexander University Erlangen-NürnbergErlangen, Germany; Group for Computational Life Sciences, Division of Physical Chemistry, Institute Ruđer BoškovićZagreb, Croatia
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Van Valen DA, Kudo T, Lane KM, Macklin DN, Quach NT, DeFelice MM, Maayan I, Tanouchi Y, Ashley EA, Covert MW. Deep Learning Automates the Quantitative Analysis of Individual Cells in Live-Cell Imaging Experiments. PLoS Comput Biol 2016; 12:e1005177. [PMID: 27814364 PMCID: PMC5096676 DOI: 10.1371/journal.pcbi.1005177] [Citation(s) in RCA: 281] [Impact Index Per Article: 35.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2016] [Accepted: 10/03/2016] [Indexed: 02/01/2023] Open
Abstract
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.
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Affiliation(s)
- David A. Van Valen
- Department of Bioengineering, Stanford University, Stanford, California, United States of America
| | - Takamasa Kudo
- Department of Chemical and Systems Biology, Stanford University, Stanford, California, United States of America
| | - Keara M. Lane
- Department of Bioengineering, Stanford University, Stanford, California, United States of America
| | - Derek N. Macklin
- Department of Bioengineering, Stanford University, Stanford, California, United States of America
| | - Nicolas T. Quach
- Department of Bioengineering, Stanford University, Stanford, California, United States of America
| | - Mialy M. DeFelice
- Department of Bioengineering, Stanford University, Stanford, California, United States of America
| | - Inbal Maayan
- Department of Bioengineering, Stanford University, Stanford, California, United States of America
| | - Yu Tanouchi
- Department of Bioengineering, Stanford University, Stanford, California, United States of America
| | - Euan A. Ashley
- Department of Genetics, Stanford University, Stanford, California, United States of America
- Department of Cardiovascular Medicine, Stanford University, Stanford, California, United States of America
| | - Markus W. Covert
- Department of Bioengineering, Stanford University, Stanford, California, United States of America
- Department of Chemical and Systems Biology, Stanford University, Stanford, California, United States of America
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66
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Kerz M, Folarin A, Meleckyte R, Watt FM, Dobson RJ, Danovi D. A Novel Automated High-Content Analysis Workflow Capturing Cell Population Dynamics from Induced Pluripotent Stem Cell Live Imaging Data. JOURNAL OF BIOMOLECULAR SCREENING 2016; 21:887-96. [PMID: 27256155 PMCID: PMC5030730 DOI: 10.1177/1087057116652064] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/29/2016] [Revised: 04/29/2016] [Accepted: 05/06/2016] [Indexed: 11/17/2022]
Abstract
Most image analysis pipelines rely on multiple channels per image with subcellular reference points for cell segmentation. Single-channel phase-contrast images are often problematic, especially for cells with unfavorable morphology, such as induced pluripotent stem cells (iPSCs). Live imaging poses a further challenge, because of the introduction of the dimension of time. Evaluations cannot be easily integrated with other biological data sets including analysis of endpoint images. Here, we present a workflow that incorporates a novel CellProfiler-based image analysis pipeline enabling segmentation of single-channel images with a robust R-based software solution to reduce the dimension of time to a single data point. These two packages combined allow robust segmentation of iPSCs solely on phase-contrast single-channel images and enable live imaging data to be easily integrated to endpoint data sets while retaining the dynamics of cellular responses. The described workflow facilitates characterization of the response of live-imaged iPSCs to external stimuli and definition of cell line-specific, phenotypic signatures. We present an efficient tool set for automated high-content analysis suitable for cells with challenging morphology. This approach has potentially widespread applications for human pluripotent stem cells and other cell types.
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Affiliation(s)
- Maximilian Kerz
- Centre for Stem Cells and Regenerative Medicine, King’s College London, Tower Wing, Guy’s Hospital, London, UK
- Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
- National Institute for Health Research, Biomedical Research Centre for Mental Health, and Biomedical Research Unit for Dementia at South London and Maudsley NHS Foundation, London, UK
- Farr Institute of Health Informatics Research, UCL Institute of Health Informatics, University College London, London, UK
| | - Amos Folarin
- Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
- National Institute for Health Research, Biomedical Research Centre for Mental Health, and Biomedical Research Unit for Dementia at South London and Maudsley NHS Foundation, London, UK
- Farr Institute of Health Informatics Research, UCL Institute of Health Informatics, University College London, London, UK
| | - Ruta Meleckyte
- Centre for Stem Cells and Regenerative Medicine, King’s College London, Tower Wing, Guy’s Hospital, London, UK
| | - Fiona M. Watt
- Centre for Stem Cells and Regenerative Medicine, King’s College London, Tower Wing, Guy’s Hospital, London, UK
| | - Richard J. Dobson
- Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
- National Institute for Health Research, Biomedical Research Centre for Mental Health, and Biomedical Research Unit for Dementia at South London and Maudsley NHS Foundation, London, UK
- Farr Institute of Health Informatics Research, UCL Institute of Health Informatics, University College London, London, UK
| | - Davide Danovi
- Centre for Stem Cells and Regenerative Medicine, King’s College London, Tower Wing, Guy’s Hospital, London, UK
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Wirjadi O, Kim YJ, Stech F, Bonfert L, Wagner M. Bayesian model for detection and classification of meningioma nuclei in microscopic images. J Microsc 2016; 265:159-168. [PMID: 27649284 DOI: 10.1111/jmi.12471] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2016] [Accepted: 08/08/2016] [Indexed: 11/25/2022]
Abstract
Image segmentation aims to determine structures of interest inside a digital picture in biomedical sciences. State-of-the art automatic methods however still fail to provide the segmentation quality achievable by humans who employ expert knowledge and use software to mark target structures on an image. Manual segmentation is time-consuming, tedious and suffers from interoperator variability, thus not serving the requirements of daily use well. Therefore, the approach presented here abandons the goal of full-fledged segmentation and settles for the localization of circular objects in photographs (10 training images and 20 testing images with several hundreds of nuclei each). A fully trainable softcore interaction point process model was hence fit to the most likely locations of nuclei of meningioma cells. The Broad Bioimage Benchmark Collection/SIMCEP data set of virtual cells served as controls. A 'colour deconvolution' algorithm was integrated to determine (based on anti-Ki67 immunohistochemistry) which real cells might have the potential to proliferate. In addition, a density parameter of the underlying Bayesian model was estimated. Immunohistochemistry results were 'simulated'for the virtual cells. The system yielded true positive (TP) rates in the detection and classification of real nuclei and their virtual counterparts. These hits outnumbered those obtained from the public domain image processing software ImageJ by 10%. The method introduced here can be trained to function not only in medicine and morphology-based systems biology but in other application domains as well. The algorithm lends itself to an automated approach that constitutes a valuable tool which is easy to use and generates acceptable results quickly.
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Affiliation(s)
- O Wirjadi
- Fraunhofer ITWM, Kaiserslautern, Germany
| | - Y-J Kim
- Institute of Pathology, Saarland University Medical Center, Homburg/Saar, Germany
| | - F Stech
- Fraunhofer ITWM, Kaiserslautern, Germany
| | - L Bonfert
- Fraunhofer ITWM, Kaiserslautern, Germany
| | - M Wagner
- Institute of Pathology, Saarland University Medical Center, Homburg/Saar, Germany
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68
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Cheng S, Quan T, Liu X, Zeng S. Large-scale localization of touching somas from 3D images using density-peak clustering. BMC Bioinformatics 2016; 17:375. [PMID: 27628179 PMCID: PMC5024436 DOI: 10.1186/s12859-016-1252-x] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2015] [Accepted: 09/08/2016] [Indexed: 12/13/2022] Open
Abstract
Background Soma localization is an important step in computational neuroscience to map neuronal circuits. However, locating somas from large-scale and complicated datasets is challenging. The challenges primarily originate from the dense distribution of somas, the diversity of soma sizes and the inhomogeneity of image contrast. Results We proposed a novel localization method based on density-peak clustering. In this method, we introduced two quantities (the local density ρ of each voxel and its minimum distance δ from voxels of higher density) to describe the soma imaging signal, and developed an automatic algorithm to identify the soma positions from the feature space (ρ, δ). Compared with other methods focused on high local density, our method allowed the soma center to be characterized by high local density and large minimum distance. The simulation results indicated that our method had a strong ability to locate the densely positioned somas and strong robustness of the key parameter for the localization. From the analysis of the experimental datasets, we demonstrated that our method was effective at locating somas from large-scale and complicated datasets, and was superior to current state-of-the-art methods for the localization of densely positioned somas. Conclusions Our method effectively located somas from large-scale and complicated datasets. Furthermore, we demonstrated the strong robustness of the key parameter for the localization and its effectiveness at a low signal-to-noise ratio (SNR) level. Thus, the method provides an effective tool for the neuroscience community to quantify the spatial distribution of neurons and the morphologies of somas. Electronic supplementary material The online version of this article (doi:10.1186/s12859-016-1252-x) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Shenghua Cheng
- School of Mathematics and Statistics, Huazhong University of Science and Technology, 1037 Luoyu Rd, Building of Science - 715, Wuhan, 430074, China.,Britton Chance Center for Biomedical Photonics, Huazhong University of Science and Technology-Wuhan National Laboratory for Optoelectronics, Wuhan, 430074, China.,MoE Key Laboratory for Biomedical Photonics, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Tingwei Quan
- Britton Chance Center for Biomedical Photonics, Huazhong University of Science and Technology-Wuhan National Laboratory for Optoelectronics, Wuhan, 430074, China.,MoE Key Laboratory for Biomedical Photonics, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China.,School of Mathematics and Statistics, Hubei University of Education, Wuhan, 430205, China
| | - Xiaomao Liu
- School of Mathematics and Statistics, Huazhong University of Science and Technology, 1037 Luoyu Rd, Building of Science - 715, Wuhan, 430074, China.
| | - Shaoqun Zeng
- Britton Chance Center for Biomedical Photonics, Huazhong University of Science and Technology-Wuhan National Laboratory for Optoelectronics, Wuhan, 430074, China.,MoE Key Laboratory for Biomedical Photonics, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China
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69
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Wilczyński S, Koprowski R, Wiernek BK, Błońska-Fajfrowska B. Image-guided automatic triggering of a fractional CO2 laser in aesthetic procedures. Comput Biol Med 2016; 76:1-6. [PMID: 27348182 DOI: 10.1016/j.compbiomed.2016.06.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2016] [Revised: 06/08/2016] [Accepted: 06/09/2016] [Indexed: 10/21/2022]
Abstract
BACKGROUND Laser procedures in dermatology and aesthetic medicine are associated with the need for manual laser triggering. This leads to pulse overlapping and side effects. METHODS Automatic laser triggering based on image analysis can provide a secure fit to each successive doses of radiation. A fractional CO2 laser was used in the study. 500 images of the human skin of healthy subjects were acquired. Automatic triggering was initiated by an application together with a camera which tracks and analyses the skin in visible light. The tracking algorithm uses the methods of image analysis to overlap images. After locating the characteristic points in analysed adjacent areas, the correspondence of graphs is found. The point coordinates derived from the images are the vertices of graphs with respect to which isomorphism is sought. When the correspondence of graphs is found, it is possible to overlap the neighbouring parts of the image. RESULTS The proposed method of laser triggering owing to the automatic image fitting method allows for 100% repeatability. To meet this requirement, there must be at least 13 graph vertices obtained from the image. For this number of vertices, the time of analysis of a single image is less than 0.5s. CONCLUSIONS The proposed method, applied in practice, may help reduce the number of side effects during dermatological laser procedures resulting from laser pulse overlapping. In addition, it reduces treatment time and enables to propose new techniques of treatment through controlled, precise laser pulse overlapping.
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Affiliation(s)
- Sławomir Wilczyński
- Department of Basic Biomedical Science, School of Pharmacy with the Division of Laboratory Medicine, Medical University of Silesia in Katowice, Poland.
| | - Robert Koprowski
- Department of Biomedical Computer Systems, University of Silesia, Faculty of Computer Science and Materials Science, Institute of Computer Science, Będzińska Street 39, Sosnowiec 41-200, Poland
| | | | - Barbara Błońska-Fajfrowska
- Department of Basic Biomedical Science, School of Pharmacy with the Division of Laboratory Medicine, Medical University of Silesia in Katowice, Poland
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70
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Shi P, Zhong J, Hong J, Huang R, Wang K, Chen Y. Automated Ki-67 Quantification of Immunohistochemical Staining Image of Human Nasopharyngeal Carcinoma Xenografts. Sci Rep 2016; 6:32127. [PMID: 27562647 PMCID: PMC4999801 DOI: 10.1038/srep32127] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2016] [Accepted: 08/02/2016] [Indexed: 01/15/2023] Open
Abstract
Nasopharyngeal carcinoma is one of the malignant neoplasm with high incidence in China and south-east Asia. Ki-67 protein is strictly associated with cell proliferation and malignant degree. Cells with higher Ki-67 expression are always sensitive to chemotherapy and radiotherapy, the assessment of which is beneficial to NPC treatment. It is still challenging to automatically analyze immunohistochemical Ki-67 staining nasopharyngeal carcinoma images due to the uneven color distributions in different cell types. In order to solve the problem, an automated image processing pipeline based on clustering of local correlation features is proposed in this paper. Unlike traditional morphology-based methods, our algorithm segments cells by classifying image pixels on the basis of local pixel correlations from particularly selected color spaces, then characterizes cells with a set of grading criteria for the reference of pathological analysis. Experimental results showed high accuracy and robustness in nucleus segmentation despite image data variance. Quantitative indicators obtained in this essay provide a reliable evidence for the analysis of Ki-67 staining nasopharyngeal carcinoma microscopic images, which would be helpful in relevant histopathological researches.
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Affiliation(s)
- Peng Shi
- School of Mathematics and Computer Science, Fujian Normal University, Fuzhou, Fujian 350117, China
| | - Jing Zhong
- The Graduate School, Fujian Medical University, Fuzhou, Fujian 350004, China
| | - Jinsheng Hong
- Department of Radiation Oncology, Laboratory of Radiation Biology, First Affiliated Hospital, Fujian Medical University, Fuzhou, Fujian 350005, China
| | - Rongfang Huang
- Department of Pathology, Fujian Provincial Cancer Hospital, Fuzhou, Fujian 350014, China
| | - Kaijun Wang
- School of Mathematics and Computer Science, Fujian Normal University, Fuzhou, Fujian 350117, China
| | - Yunbin Chen
- The Graduate School, Fujian Medical University, Fuzhou, Fujian 350004, China.,Department of Radiology, Fujian Provincial Cancer Hospital, Fuzhou, Fujian 350014, China
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71
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Szczepankiewicz F, van Westen D, Englund E, Westin CF, Ståhlberg F, Lätt J, Sundgren PC, Nilsson M. The link between diffusion MRI and tumor heterogeneity: Mapping cell eccentricity and density by diffusional variance decomposition (DIVIDE). Neuroimage 2016; 142:522-532. [PMID: 27450666 DOI: 10.1016/j.neuroimage.2016.07.038] [Citation(s) in RCA: 120] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2016] [Revised: 06/24/2016] [Accepted: 07/16/2016] [Indexed: 01/18/2023] Open
Abstract
The structural heterogeneity of tumor tissue can be probed by diffusion MRI (dMRI) in terms of the variance of apparent diffusivities within a voxel. However, the link between the diffusional variance and the tissue heterogeneity is not well-established. To investigate this link we test the hypothesis that diffusional variance, caused by microscopic anisotropy and isotropic heterogeneity, is associated with variable cell eccentricity and cell density in brain tumors. We performed dMRI using a novel encoding scheme for diffusional variance decomposition (DIVIDE) in 7 meningiomas and 8 gliomas prior to surgery. The diffusional variance was quantified from dMRI in terms of the total mean kurtosis (MKT), and DIVIDE was used to decompose MKT into components caused by microscopic anisotropy (MKA) and isotropic heterogeneity (MKI). Diffusion anisotropy was evaluated in terms of the fractional anisotropy (FA) and microscopic fractional anisotropy (μFA). Quantitative microscopy was performed on the excised tumor tissue, where structural anisotropy and cell density were quantified by structure tensor analysis and cell nuclei segmentation, respectively. In order to validate the DIVIDE parameters they were correlated to the corresponding parameters derived from microscopy. We found an excellent agreement between the DIVIDE parameters and corresponding microscopy parameters; MKA correlated with cell eccentricity (r=0.95, p<10-7) and MKI with the cell density variance (r=0.83, p<10-3). The diffusion anisotropy correlated with structure tensor anisotropy on the voxel-scale (FA, r=0.80, p<10-3) and microscopic scale (μFA, r=0.93, p<10-6). A multiple regression analysis showed that the conventional MKT parameter reflects both variable cell eccentricity and cell density, and therefore lacks specificity in terms of microstructure characteristics. However, specificity was obtained by decomposing the two contributions; MKA was associated only to cell eccentricity, and MKI only to cell density variance. The variance in meningiomas was caused primarily by microscopic anisotropy (mean±s.d.) MKA=1.11±0.33 vs MKI=0.44±0.20 (p<10-3), whereas in the gliomas, it was mostly caused by isotropic heterogeneity MKI=0.57±0.30 vs MKA=0.26±0.11 (p<0.05). In conclusion, DIVIDE allows non-invasive mapping of parameters that reflect variable cell eccentricity and density. These results constitute convincing evidence that a link exists between specific aspects of tissue heterogeneity and parameters from dMRI. Decomposing effects of microscopic anisotropy and isotropic heterogeneity facilitates an improved interpretation of tumor heterogeneity as well as diffusion anisotropy on both the microscopic and macroscopic scale.
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Affiliation(s)
- Filip Szczepankiewicz
- Lund University, Department of Clinical Sciences Lund, Medical Radiation Physics, Lund, Sweden.
| | - Danielle van Westen
- Lund University, Skåne University Hospital, Department of Clinical Sciences Lund, Diagnostic Radiology, Lund, Sweden; Skåne University Hospital, Department of Imaging and Function, Lund, Sweden
| | - Elisabet Englund
- Lund University, Skåne University Hospital, Department of Clinical Sciences Lund, Division of Oncology and Pathology, Lund, Sweden
| | - Carl-Fredrik Westin
- Harvard Medical School, Brigham and Women's Hospital, Department of Radiology, Boston, MA, USA
| | - Freddy Ståhlberg
- Lund University, Department of Clinical Sciences Lund, Medical Radiation Physics, Lund, Sweden; Lund University, Skåne University Hospital, Department of Clinical Sciences Lund, Diagnostic Radiology, Lund, Sweden
| | - Jimmy Lätt
- Skåne University Hospital, Department of Imaging and Function, Lund, Sweden
| | - Pia C Sundgren
- Lund University, Skåne University Hospital, Department of Clinical Sciences Lund, Diagnostic Radiology, Lund, Sweden
| | - Markus Nilsson
- Lund University, Skåne University Hospital, Department of Clinical Sciences Lund, Diagnostic Radiology, Lund, Sweden; Lund University, Lund University Bioimaging Center, Lund, Sweden
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Ram S, Rodriguez JJ. Size-Invariant Detection of Cell Nuclei in Microscopy Images. IEEE TRANSACTIONS ON MEDICAL IMAGING 2016; 35:1753-1764. [PMID: 26886972 DOI: 10.1109/tmi.2016.2527740] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
Accurate detection of individual cell nuclei in microscopy images is an essential and fundamental task for many biological studies. In particular, multivariate fluorescence microscopy is used to observe different aspects of cells in cultures. Manual detection of individual cell nuclei by visual inspection is time consuming, and prone to induce subjective bias. This makes automatic detection of cell nuclei essential for large-scale, objective studies of cell cultures. Blur, clutter, bleed-through, imaging noise and touching and partially overlapping nuclei with varying sizes and shapes make automated detection of individual cell nuclei a challenging task using image analysis. In this paper we propose a new automated method for fast and robust detection of individual cell nuclei based on their radial symmetric nature in fluorescence in-situ hybridization (FISH) images obtained via confocal microscopy. The main contributions are two-fold. 1) This work presents a more accurate cell nucleus detection system using the fast radial symmetry transform (FRST). 2) The proposed cell nucleus detection system is robust against most occlusions and variations in size and moderate shape deformations. We evaluate the performance of the proposed algorithm using precision/recall rates, Fβ-score and root-mean-squared distance (RMSD) and show that our algorithm provides improved detection accuracy compared to existing algorithms.
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73
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Su H, Xing F, Yang L. Robust Cell Detection of Histopathological Brain Tumor Images Using Sparse Reconstruction and Adaptive Dictionary Selection. IEEE TRANSACTIONS ON MEDICAL IMAGING 2016; 35:1575-1586. [PMID: 26812706 PMCID: PMC4922900 DOI: 10.1109/tmi.2016.2520502] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/30/2023]
Abstract
Successful diagnostic and prognostic stratification, treatment outcome prediction, and therapy planning depend on reproducible and accurate pathology analysis. Computer aided diagnosis (CAD) is a useful tool to help doctors make better decisions in cancer diagnosis and treatment. Accurate cell detection is often an essential prerequisite for subsequent cellular analysis. The major challenge of robust brain tumor nuclei/cell detection is to handle significant variations in cell appearance and to split touching cells. In this paper, we present an automatic cell detection framework using sparse reconstruction and adaptive dictionary learning. The main contributions of our method are: 1) A sparse reconstruction based approach to split touching cells; 2) An adaptive dictionary learning method used to handle cell appearance variations. The proposed method has been extensively tested on a data set with more than 2000 cells extracted from 32 whole slide scanned images. The automatic cell detection results are compared with the manually annotated ground truth and other state-of-the-art cell detection algorithms. The proposed method achieves the best cell detection accuracy with a F1 score = 0.96.
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Affiliation(s)
- Hai Su
- J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, FL 32611, USA
| | - Fuyong Xing
- Department of Electrical and Computer Engineering, University of Florida, FL 32611, USA
| | - Lin Yang
- J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, FL 32611, USA
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74
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Hodneland E, Tai XC, Kalisch H. PDE Based Algorithms for Smooth Watersheds. IEEE TRANSACTIONS ON MEDICAL IMAGING 2016; 35:957-966. [PMID: 26625408 DOI: 10.1109/tmi.2015.2503328] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
Watershed segmentation is useful for a number of image segmentation problems with a wide range of practical applications. Traditionally, the tracking of the immersion front is done by applying a fast sorting algorithm. In this work, we explore a continuous approach based on a geometric description of the immersion front which gives rise to a partial differential equation. The main advantage of using a partial differential equation to track the immersion front is that the method becomes versatile and may easily be stabilized by introducing regularization terms. Coupling the geometric approach with a proper "merging strategy" creates a robust algorithm which minimizes over- and under-segmentation even without predefined markers. Since reliable markers defined prior to segmentation can be difficult to construct automatically for various reasons, being able to treat marker-free situations is a major advantage of the proposed method over earlier watershed formulations. The motivation for the methods developed in this paper is taken from high-throughput screening of cells. A fully automated segmentation of single cells enables the extraction of cell properties from large data sets, which can provide substantial insight into a biological model system. Applying smoothing to the boundaries can improve the accuracy in many image analysis tasks requiring a precise delineation of the plasma membrane of the cell. The proposed segmentation method is applied to real images containing fluorescently labeled cells, and the experimental results show that our implementation is robust and reliable for a variety of challenging segmentation tasks.
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75
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Reza SMS, Iftekharuddin KM. Glioma Grading Using Cell Nuclei Morphologic Features in Digital Pathology Images. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2016; 9785:97852U. [PMID: 27942094 PMCID: PMC5142817 DOI: 10.1117/12.2217559] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
This work proposes a computationally efficient cell nuclei morphologic feature analysis technique to characterize the brain gliomas in tissue slide images. In this work, our contributions are two-fold: 1) obtain an optimized cell nuclei segmentation method based on the pros and cons of the existing techniques in literature, 2) extract representative features by k-mean clustering of nuclei morphologic features to include area, perimeter, eccentricity, and major axis length. This clustering based representative feature extraction avoids shortcomings of extensive tile [1] [2] and nuclear score [3] based methods for brain glioma grading in pathology images. Multilayer perceptron (MLP) is used to classify extracted features into two tumor types: glioblastoma multiforme (GBM) and low grade glioma (LGG). Quantitative scores such as precision, recall, and accuracy are obtained using 66 clinical patients' images from The Cancer Genome Atlas (TCGA) [4] dataset. On an average ~94% accuracy from 10 fold cross-validation confirms the efficacy of the proposed method.
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76
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Zafari S, Eerola T, Sampo J, Kälviäinen H, Haario H. Segmentation of Overlapping Elliptical Objects in Silhouette Images. IEEE TRANSACTIONS ON IMAGE PROCESSING : A PUBLICATION OF THE IEEE SIGNAL PROCESSING SOCIETY 2015; 24:5942-5952. [PMID: 26513788 DOI: 10.1109/tip.2015.2492828] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
Segmentation of partially overlapping objects with a known shape is needed in an increasing amount of various machine vision applications. This paper presents a method for segmentation of clustered partially overlapping objects with a shape that can be approximated using an ellipse. The method utilizes silhouette images, which means that it requires only that the foreground (objects) and background can be distinguished from each other. The method starts with seedpoint extraction using bounded erosion and fast radial symmetry transform. Extracted seedpoints are then utilized to associate edge points to objects in order to create contour evidence. Finally, contours of the objects are estimated by fitting ellipses to the contour evidence. The experiments on one synthetic and two different real data sets showed that the proposed method outperforms two current state-of-art approaches in overlapping objects segmentation.
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77
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Kaviani R, Merat P, Moldovan F, Villemure I. An automated cell viability quantification method for low-resolution confocal images of closely packed cells based on a modified gradient flow tracking algorithm. J Microsc 2015; 261:217-26. [PMID: 26551967 DOI: 10.1111/jmi.12322] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2014] [Accepted: 08/31/2015] [Indexed: 11/28/2022]
Abstract
Fluorescent-based live/dead labelling combined with fluorescent microscopy is one of the widely used and reliable methods for assessment of cell viability. This method is, however, not quantitative. Many image-processing methods have been proposed for cell quantification in an image. Among all these methods, several of them are capable of quantifying the number of cells in high-resolution images with closely packed cells. However, no method has addressed the quantification of the number of cells in low-resolution images containing closely packed cells with variable sizes. This paper presents a novel method for automatic quantification of live/dead cells in 2D fluorescent low-resolution images containing closely packed cells with variable sizes using a mean shift-based gradient flow tracking. Accuracy and performance of the method was tested on growth plate confocal images. Experimental results show that our algorithm has a better performance in comparison to other methods used in similar detection conditions.
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Affiliation(s)
- R Kaviani
- Department of Mechanical Engineering, Ecole Polytechnique of Montreal, Montreal, Canada.,Research Center, Sainte-Justine University Hospital, Montreal, Canada
| | - P Merat
- Department of Electrical and Computer Engineering, McGill University, Montreal, Canada
| | - F Moldovan
- Research Center, Sainte-Justine University Hospital, Montreal, Canada.,Department of Dental Medicine, University of Montreal, Montreal, Canada
| | - I Villemure
- Department of Mechanical Engineering, Ecole Polytechnique of Montreal, Montreal, Canada.,Research Center, Sainte-Justine University Hospital, Montreal, Canada
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78
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Mari JF, Saito JH, Neves AF, Lotufo CMDC, Destro-Filho JB, Nicoletti MDC. Quantitative Analysis of Rat Dorsal Root Ganglion Neurons Cultured on Microelectrode Arrays Based on Fluorescence Microscopy Image Processing. Int J Neural Syst 2015; 25:1550033. [PMID: 26510475 DOI: 10.1142/s0129065715500331] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Microelectrode Arrays (MEA) are devices for long term electrophysiological recording of extracellular spontaneous or evocated activities on in vitro neuron culture. This work proposes and develops a framework for quantitative and morphological analysis of neuron cultures on MEAs, by processing their corresponding images, acquired by fluorescence microscopy. The neurons are segmented from the fluorescence channel images using a combination of segmentation by thresholding, watershed transform, and object classification. The positioning of microelectrodes is obtained from the transmitted light channel images using the circular Hough transform. The proposed method was applied to images of dissociated culture of rat dorsal root ganglion (DRG) neuronal cells. The morphological and topological quantitative analysis carried out produced information regarding the state of culture, such as population count, neuron-to-neuron and neuron-to-microelectrode distances, soma morphologies, neuron sizes, neuron and microelectrode spatial distributions. Most of the analysis of microscopy images taken from neuronal cultures on MEA only consider simple qualitative analysis. Also, the proposed framework aims to standardize the image processing and to compute quantitative useful measures for integrated image-signal studies and further computational simulations. As results show, the implemented microelectrode identification method is robust and so are the implemented neuron segmentation and classification one (with a correct segmentation rate up to 84%). The quantitative information retrieved by the method is highly relevant to assist the integrated signal-image study of recorded electrophysiological signals as well as the physical aspects of the neuron culture on MEA. Although the experiments deal with DRG cell images, cortical and hippocampal cell images could also be processed with small adjustments in the image processing parameter estimation.
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Affiliation(s)
- João Fernando Mari
- * Instituto de Ciências Exatas e Tecnológicas - Universidade Federal de Viçosa, 38810-000 Rio Paranaí, MG, Brazil.,† Department of Computer Science - UFSCar, 13565-905 S. Carlos, SP, Brazil
| | - José Hiroki Saito
- † Department of Computer Science - UFSCar, 13565-905 S. Carlos, SP, Brazil.,‡ FACCAMP - 13231-230 Campo Limpo Paulista, SP, Brazil
| | - Amanda Ferreira Neves
- § School of Electrical Engineering - UFU, 38400-902 Uberlândia, MG, Brazil.,∥ Department of Structural and Functional Biology - UNICAMP, 13083-970 Campinas, SP, Brazil
| | | | | | - Maria do Carmo Nicoletti
- † Department of Computer Science - UFSCar, 13565-905 S. Carlos, SP, Brazil.,‡ FACCAMP - 13231-230 Campo Limpo Paulista, SP, Brazil
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79
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Lim J, Lee HK, Yu W, Ahmed S. Light sheet fluorescence microscopy (LSFM): past, present and future. Analyst 2015; 139:4758-68. [PMID: 25118817 DOI: 10.1039/c4an00624k] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Light sheet fluorescence microscopy (LSFM) has emerged as an important imaging modality to follow biology in live 3D samples over time with reduced phototoxicity and photobleaching. In particular, LSFM has been instrumental in revealing the detail of early embryonic development of Zebrafish, Drosophila, and C. elegans. Open access projects, DIY-SPIM, OpenSPIM, and OpenSPIN, now allow LSFM to be set-up easily and at low cost. The aim of this paper is to facilitate the set-up and use of LSFM by reviewing and comparing open access projects, image processing tools and future challenges.
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Affiliation(s)
- John Lim
- Institute of Medical Biology, 8A Biomedical Grove, Immunos 5.37, Singapore 138648, Singapore.
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80
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Wiesmann V, Reimer D, Franz D, Hüttmayer H, Mielenz D, Wittenberg T. Automated high-throughput analysis of B cell spreading on immobilized antibodies with whole slide imaging. CURRENT DIRECTIONS IN BIOMEDICAL ENGINEERING 2015. [DOI: 10.1515/cdbme-2015-0056] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
AbstractAutomated image processing methods enable objective, reproducible and high quality analysis of fluorescent cell images in a reasonable amount of time. Therefore, we propose the application of image processing pipelines based on established segmentation algorithms which can handle massive amounts of whole slide imaging data of multiple fluorescent labeled cells. After automated parameter adaption the segmentation pipelines provide high quality cell delineations revealing significant differences in the spreading of B cells: LPS-activated B cells spread significantly less on anti CD19 mAb than on anti BCR mAb and both processes could be inhibited by the F-actin destabilizing drug Cytochalasin D. Moreover, anti CD19 mAb induce a more symmetrical spreading than anti BCR mAb as reflected by the higher cell circularity.
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Affiliation(s)
- Veit Wiesmann
- Image Processing and Medical Engineering Department, Fraunhofer Institute for Integrated Circuits IIS, Erlangen, Germany
| | - Dorothea Reimer
- Division of Molecular Immunology, Department of Internal Medicine III, Nikolaus Fiebiger Center, University Hospital Erlangen and Friedrich-Alexander University Erlangen-Nuremberg, Germany
| | - Daniela Franz
- Image Processing and Medical Engineering Department, Fraunhofer Institute for Integrated Circuits IIS, Erlangen, Germany
| | - Hanna Hüttmayer
- Image Processing and Medical Engineering Department, Fraunhofer Institute for Integrated Circuits IIS, Erlangen, Germany
| | - Dirk Mielenz
- Division of Molecular Immunology, Department of Internal Medicine III, Nikolaus Fiebiger Center, University Hospital Erlangen and Friedrich-Alexander University Erlangen-Nuremberg, Germany
| | - Thomas Wittenberg
- Image Processing and Medical Engineering Department, Fraunhofer Institute for Integrated Circuits IIS, Erlangen, Germany
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81
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Arif M, Rajpoot NM, Nattkemper TW, Technow U, Chakraborty T, Fisch N, Jensen NA, Niehaus K. Quantification of cell infection caused by Listeria monocytogenes invasion. J Biotechnol 2015; 154:76-83. [PMID: 21527293 DOI: 10.1016/j.jbiotec.2011.03.008] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2010] [Revised: 03/08/2011] [Accepted: 03/10/2011] [Indexed: 11/27/2022]
Abstract
Listeria monocytogenes causes a life-threatening food-borne disease known as Listeriosis. Elderly,immunocompromised, and pregnant women are primarily the victims of this facultative intracellular Gram-positive pathogen. Since the bacteria survive intracellularly within the human host cells they are protected against the immune system and poorly accessed by many antibiotics. In order to screen pharmaceutical substances for their ability to interfere with the infection, persistence and release of L. monocytogenes a high content as say is required. We established a high content screen (HCS) using the RAW 264.7 mouse macrophage cell line seeded into 96-well glass bottom microplates. Cells were infected with GFP-expressing L. monocytogenes and stained thereafter with Hoechst 33342.Automated image acquisition was carried out by the Scan(R) screening station. We have developed an algorithm that automatically grades cells in microscopy images of fluorescent-tagged Listeria for the severity of infection. The grading accuracy of this newly developed algorithm is 97.1% as compared to a 74.3%grading accuracy we obtained using the commercial Olympus Scan(R) software.
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Affiliation(s)
- Muhammad Arif
- Department of Electrical Engineering, Pakistan Institute of Engineering & Applied Sciences (PIEAS), P.O. Nilore, Islamabad 45650, Pakistan.
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82
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Wang Y, Zhang Z, Wang H, Bi S. Segmentation of the Clustered Cells with Optimized Boundary Detection in Negative Phase Contrast Images. PLoS One 2015; 10:e0130178. [PMID: 26066315 PMCID: PMC4467081 DOI: 10.1371/journal.pone.0130178] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2014] [Accepted: 05/18/2015] [Indexed: 11/19/2022] Open
Abstract
Cell image segmentation plays a central role in numerous biology studies and clinical applications. As a result, the development of cell image segmentation algorithms with high robustness and accuracy is attracting more and more attention. In this study, an automated cell image segmentation algorithm is developed to get improved cell image segmentation with respect to cell boundary detection and segmentation of the clustered cells for all cells in the field of view in negative phase contrast images. A new method which combines the thresholding method and edge based active contour method was proposed to optimize cell boundary detection. In order to segment clustered cells, the geographic peaks of cell light intensity were utilized to detect numbers and locations of the clustered cells. In this paper, the working principles of the algorithms are described. The influence of parameters in cell boundary detection and the selection of the threshold value on the final segmentation results are investigated. At last, the proposed algorithm is applied to the negative phase contrast images from different experiments. The performance of the proposed method is evaluated. Results show that the proposed method can achieve optimized cell boundary detection and highly accurate segmentation for clustered cells.
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Affiliation(s)
- Yuliang Wang
- Robotics Institute, School of Mechanical Engineering and Automation, Beihang University, Beijing 100191, P.R. China
- * E-mail:
| | - Zaicheng Zhang
- Robotics Institute, School of Mechanical Engineering and Automation, Beihang University, Beijing 100191, P.R. China
| | - Huimin Wang
- Department of Materials Science and Engineering, The Ohio State University, 2041 College Rd., Columbus, Ohio 43210, United States of America
| | - Shusheng Bi
- Robotics Institute, School of Mechanical Engineering and Automation, Beihang University, Beijing 100191, P.R. China
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83
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Mathew B, Schmitz A, Muñoz-Descalzo S, Ansari N, Pampaloni F, Stelzer EHK, Fischer SC. Robust and automated three-dimensional segmentation of densely packed cell nuclei in different biological specimens with Lines-of-Sight decomposition. BMC Bioinformatics 2015; 16:187. [PMID: 26049713 PMCID: PMC4458345 DOI: 10.1186/s12859-015-0617-x] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2014] [Accepted: 05/18/2015] [Indexed: 12/02/2022] Open
Abstract
Background Due to the large amount of data produced by advanced microscopy, automated image analysis is crucial in modern biology. Most applications require reliable cell nuclei segmentation. However, in many biological specimens cell nuclei are densely packed and appear to touch one another in the images. Therefore, a major difficulty of three-dimensional cell nuclei segmentation is the decomposition of cell nuclei that apparently touch each other. Current methods are highly adapted to a certain biological specimen or a specific microscope. They do not ensure similarly accurate segmentation performance, i.e. their robustness for different datasets is not guaranteed. Hence, these methods require elaborate adjustments to each dataset. Results We present an advanced three-dimensional cell nuclei segmentation algorithm that is accurate and robust. Our approach combines local adaptive pre-processing with decomposition based on Lines-of-Sight (LoS) to separate apparently touching cell nuclei into approximately convex parts. We demonstrate the superior performance of our algorithm using data from different specimens recorded with different microscopes. The three-dimensional images were recorded with confocal and light sheet-based fluorescence microscopes. The specimens are an early mouse embryo and two different cellular spheroids. We compared the segmentation accuracy of our algorithm with ground truth data for the test images and results from state-of-the-art methods. The analysis shows that our method is accurate throughout all test datasets (mean F-measure: 91 %) whereas the other methods each failed for at least one dataset (F-measure ≤ 69 %). Furthermore, nuclei volume measurements are improved for LoS decomposition. The state-of-the-art methods required laborious adjustments of parameter values to achieve these results. Our LoS algorithm did not require parameter value adjustments. The accurate performance was achieved with one fixed set of parameter values. Conclusion We developed a novel and fully automated three-dimensional cell nuclei segmentation method incorporating LoS decomposition. LoS are easily accessible features that ensure correct splitting of apparently touching cell nuclei independent of their shape, size or intensity. Our method showed superior performance compared to state-of-the-art methods, performing accurately for a variety of test images. Hence, our LoS approach can be readily applied to quantitative evaluation in drug testing, developmental and cell biology. Electronic supplementary material The online version of this article (doi:10.1186/s12859-015-0617-x) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- B Mathew
- Buchmann Institute for Molecular Life Sciences (BMLS), Fachbereich Biowissenschaften (FB15, IZN), Goethe Universität Frankfurt am Main, Max-von-Laue-Straße 15, 60438, Frankfurt am Main, Germany.
| | - A Schmitz
- Buchmann Institute for Molecular Life Sciences (BMLS), Fachbereich Biowissenschaften (FB15, IZN), Goethe Universität Frankfurt am Main, Max-von-Laue-Straße 15, 60438, Frankfurt am Main, Germany.
| | - S Muñoz-Descalzo
- Department of Biology and Biochemistry, University of Bath, Bath, BA2 7AY, UK.
| | - N Ansari
- Buchmann Institute for Molecular Life Sciences (BMLS), Fachbereich Biowissenschaften (FB15, IZN), Goethe Universität Frankfurt am Main, Max-von-Laue-Straße 15, 60438, Frankfurt am Main, Germany.
| | - F Pampaloni
- Buchmann Institute for Molecular Life Sciences (BMLS), Fachbereich Biowissenschaften (FB15, IZN), Goethe Universität Frankfurt am Main, Max-von-Laue-Straße 15, 60438, Frankfurt am Main, Germany.
| | - E H K Stelzer
- Buchmann Institute for Molecular Life Sciences (BMLS), Fachbereich Biowissenschaften (FB15, IZN), Goethe Universität Frankfurt am Main, Max-von-Laue-Straße 15, 60438, Frankfurt am Main, Germany.
| | - S C Fischer
- Buchmann Institute for Molecular Life Sciences (BMLS), Fachbereich Biowissenschaften (FB15, IZN), Goethe Universität Frankfurt am Main, Max-von-Laue-Straße 15, 60438, Frankfurt am Main, Germany.
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84
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Discrimination of cell cycle phases in PCNA-immunolabeled cells. BMC Bioinformatics 2015; 16:180. [PMID: 26022740 PMCID: PMC4448323 DOI: 10.1186/s12859-015-0618-9] [Citation(s) in RCA: 54] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2015] [Accepted: 05/18/2015] [Indexed: 11/13/2022] Open
Abstract
Background Protein function in eukaryotic cells is often controlled in a cell cycle-dependent manner. Therefore, the correct assignment of cellular phenotypes to cell cycle phases is a crucial task in cell biology research. Nuclear proteins whose localization varies during the cell cycle are valuable and frequently used markers of cell cycle progression. Proliferating cell nuclear antigen (PCNA) is a protein which is involved in DNA replication and has cell cycle dependent properties. In this work, we present a tool to identify cell cycle phases and in particular, sub-stages of the DNA replication phase (S-phase) based on the characteristic patterns of PCNA distribution. Single time point images of PCNA-immunolabeled cells are acquired using confocal and widefield fluorescence microscopy. In order to discriminate different cell cycle phases, an optimized processing pipeline is proposed. For this purpose, we provide an in-depth analysis and selection of appropriate features for classification, an in-depth evaluation of different classification algorithms, as well as a comparative analysis of classification performance achieved with confocal versus widefield microscopy images. Results We show that the proposed processing chain is capable of automatically classifying cell cycle phases in PCNA-immunolabeled cells from single time point images, independently of the technique of image acquisition. Comparison of confocal and widefield images showed that for the proposed approach, the overall classification accuracy is slightly higher for confocal microscopy images. Conclusion Overall, automated identification of cell cycle phases and in particular, sub-stages of the DNA replication phase (S-phase) based on the characteristic patterns of PCNA distribution, is feasible for both confocal and widefield images.
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85
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CAST: An automated segmentation and tracking tool for the analysis of transcriptional kinetics from single-cell time-lapse recordings. Methods 2015; 85:3-11. [PMID: 25934263 DOI: 10.1016/j.ymeth.2015.04.023] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2015] [Revised: 04/14/2015] [Accepted: 04/21/2015] [Indexed: 12/20/2022] Open
Abstract
Fluorescence and bioluminescence time-lapse imaging allows to investigate a vast range of cellular processes at single-cell or even subcellular resolution. In particular, time-lapse imaging can provide uniquely detailed information on the fine kinetics of transcription, as well as on biological oscillations such as the circadian and cell cycles. However, we face a paucity of automated methods to quantify time-lapse imaging data with single-cell precision, notably throughout multiple cell cycles. We developed CAST (Cell Automated Segmentation and Tracking platform) to automatically and robustly detect the position and size of cells or nuclei, quantify the corresponding light signals, while taking into account both cell divisions (lineage tracking) and migration events. We present here how CAST analyzes bioluminescence data from a short-lived transcriptional luciferase reporter. However, our flexible and modular implementation makes it easily adaptable to a wide variety of time-lapse recordings. We exemplify how CAST efficiently quantifies single-cell gene expression over multiple cell cycles using mouse NIH3T3 culture cells with a luminescence expression driven by the Bmal1 promoter, a central gene of the circadian oscillator. We further illustrate how such data can be used to quantify transcriptional bursting in conditions of lengthened circadian period, revealing thereby remarkably similar bursting signature compared to the endogenous circadian condition despite marked period lengthening. In summary, we establish CAST as novel tool for the efficient segmentation, signal quantification, and tracking of time-lapse images from mammalian cell culture.
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86
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Zhang C, Sun C, Su R, Pham TD. Clustered nuclei splitting via curvature information and gray-scale distance transform. J Microsc 2015; 259:36-52. [PMID: 25864866 DOI: 10.1111/jmi.12246] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2014] [Accepted: 02/17/2015] [Indexed: 12/01/2022]
Abstract
Clusters or clumps of cells or nuclei are frequently observed in two dimensional images of thick tissue sections. Correct and accurate segmentation of overlapping cells and nuclei is important for many biological and biomedical applications. Many existing algorithms split clumps through the binarization of the input images; therefore, the intensity information of the original image is lost during this process. In this paper, we present a curvature information, gray scale distance transform, and shortest path splitting line-based algorithm which can make full use of the concavity and image intensity information to find out markers, each of which represents an individual object, and detect accurate splitting lines between objects using shortest path and junction adjustment. The proposed algorithm is tested on both synthetic and real nuclei images. Experiment results show that the performance of the proposed method is better than that of marker-controlled watershed method and ellipse fitting method.
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Affiliation(s)
- Chao Zhang
- School of Engineering and Information Technology, The University of New South Wales, Canberra, ACT 2600, Australia.,CSIRO Computational Informatics, Locked Bag 17, North Ryde, NSW 2113, Australia
| | - Changming Sun
- CSIRO Computational Informatics, Locked Bag 17, North Ryde, NSW 2113, Australia
| | - Ran Su
- Bioinformatics Institute, 30 Biopolis Street, #07-01, Matrix, 138671, Singapore
| | - Tuan D Pham
- Aizu Research Cluster for Medical Engineering and Informatics, Research Center for Advanced Information Science and Technology, The University of Aizu, Fukushima, Japan
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87
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Lombardot B, Oh CT, Kwak J, Genovesio A, Kang M, Hansen MAE, Han SJ. High-throughput in vivo genotoxicity testing: an automated readout system for the somatic mutation and recombination test (SMART). PLoS One 2015; 10:e0121287. [PMID: 25830368 PMCID: PMC4382174 DOI: 10.1371/journal.pone.0121287] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2014] [Accepted: 01/29/2015] [Indexed: 11/27/2022] Open
Abstract
Genotoxicity testing is an important component of toxicity assessment. As illustrated by the European registration, evaluation, authorization, and restriction of chemicals (REACH) directive, it concerns all the chemicals used in industry. The commonly used in vivo mammalian tests appear to be ill adapted to tackle the large compound sets involved, due to throughput, cost, and ethical issues. The somatic mutation and recombination test (SMART) represents a more scalable alternative, since it uses Drosophila, which develops faster and requires less infrastructure. Despite these advantages, the manual scoring of the hairs on Drosophila wings required for the SMART limits its usage. To overcome this limitation, we have developed an automated SMART readout. It consists of automated imaging, followed by an image analysis pipeline that measures individual wing genotoxicity scores. Finally, we have developed a wing score-based dose-dependency approach that can provide genotoxicity profiles. We have validated our method using 6 compounds, obtaining profiles almost identical to those obtained from manual measures, even for low-genotoxicity compounds such as urethane. The automated SMART, with its faster and more reliable readout, fulfills the need for a high-throughput in vivo test. The flexible imaging strategy we describe and the analysis tools we provide should facilitate the optimization and dissemination of our methods.
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Affiliation(s)
- Benoit Lombardot
- Image Mining Group, Institut Pasteur Korea, Sampyeong-dong 696, Bundang-gu, Seongnam-si, Gyeonggi-do, Korea
| | - Chun-Taek Oh
- Drug Biology Group, Institut Pasteur Korea, Sampyeong-dong 696, Bundang-gu, Seongnam-si, Gyeonggi-do, Korea
| | - Jihoon Kwak
- Image Mining Group, Institut Pasteur Korea, Sampyeong-dong 696, Bundang-gu, Seongnam-si, Gyeonggi-do, Korea
| | - Auguste Genovesio
- Image Mining Group, Institut Pasteur Korea, Sampyeong-dong 696, Bundang-gu, Seongnam-si, Gyeonggi-do, Korea
| | - Myungjoo Kang
- Department of Mathematical Sciences, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 151–747, Korea
| | - Michael Adsett Edberg Hansen
- Image Mining Group, Institut Pasteur Korea, Sampyeong-dong 696, Bundang-gu, Seongnam-si, Gyeonggi-do, Korea
- * E-mail: (SJH); (MH)
| | - Sung-Jun Han
- Drug Biology Group, Institut Pasteur Korea, Sampyeong-dong 696, Bundang-gu, Seongnam-si, Gyeonggi-do, Korea
- * E-mail: (SJH); (MH)
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88
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An automated approach to the segmentation of HEp-2 cells for the indirect immunofluorescence ANA test. Comput Med Imaging Graph 2015; 40:62-9. [DOI: 10.1016/j.compmedimag.2014.12.005] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2014] [Revised: 11/10/2014] [Accepted: 12/24/2014] [Indexed: 12/27/2022]
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89
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Veta M, Pluim JPW, van Diest PJ, Viergever MA. Breast cancer histopathology image analysis: a review. IEEE Trans Biomed Eng 2015; 61:1400-11. [PMID: 24759275 DOI: 10.1109/tbme.2014.2303852] [Citation(s) in RCA: 265] [Impact Index Per Article: 29.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
This paper presents an overview of methods that have been proposed for the analysis of breast cancer histopathology images. This research area has become particularly relevant with the advent of whole slide imaging (WSI) scanners, which can perform cost-effective and high-throughput histopathology slide digitization, and which aim at replacing the optical microscope as the primary tool used by pathologist. Breast cancer is the most prevalent form of cancers among women, and image analysis methods that target this disease have a huge potential to reduce the workload in a typical pathology lab and to improve the quality of the interpretation. This paper is meant as an introduction for nonexperts. It starts with an overview of the tissue preparation, staining and slide digitization processes followed by a discussion of the different image processing techniques and applications, ranging from analysis of tissue staining to computer-aided diagnosis, and prognosis of breast cancer patients.
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90
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Automatic cell identification in the unique system of invariant embryogenesis in Caenorhabditis elegans. Biomed Eng Lett 2015. [DOI: 10.1007/s13534-014-0162-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022] Open
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91
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Guven M, Cengizler C. Data cluster analysis-based classification of overlapping nuclei in Pap smear samples. Biomed Eng Online 2014; 13:159. [PMID: 25487072 PMCID: PMC4269967 DOI: 10.1186/1475-925x-13-159] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2014] [Accepted: 12/01/2014] [Indexed: 11/17/2022] Open
Abstract
Background The extraction of overlapping cell nuclei is a critical issue in automated
diagnosis systems. Due to the similarities between overlapping and malignant
nuclei, misclassification of the overlapped regions can affect the automated
systems’ final decision. In this paper, we present a method for detecting
overlapping cell nuclei in Pap smear samples. Method Judgement about the presence of overlapping nuclei is performed in three steps
using an unsupervised clustering approach: candidate nuclei regions are located
and refined with morphological operations; key features are extracted; and
candidate nuclei regions are clustered into two groups, overlapping or
non-overlapping, A new combination of features containing two local minima-based
and three shape-dependent features are extracted for determination of the presence
or absence of overlapping. F1 score, precision, and recall values are used to
evaluate the method’s classification performance. Results In order to make evaluation, we compared the segmentation results of the
proposed system with empirical contours. Experimental results indicate that
applied morphological operations can locate most of the nuclei and produces
accurate boundaries. Independent features significance test indicates that our
feature combination is significant for overlapping nuclei. Comparisons of the
classification results of a fuzzy clustering algorithm and a non-fuzzy clustering
algorithm show that the fuzzy approach would be a more convenient mechanism for
classification of overlapping. Conclusion The main contribution of this study is the development of a decision mechanism
for identifying overlapping nuclei to further improve the extraction process with
respect to the segmentation of interregional borders, nuclei area, and radius.
Experimental results showed that our unsupervised approach with proposed feature
combination yields acceptable performance for detection of overlapping
nuclei.
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Affiliation(s)
- Mustafa Guven
- Faculty of Engineering and Architecture Department of Biomedical Engineering, Cukurova University, Balcalı, 01330 Adana, Turkey.
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92
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Sui D, Wang K, Park H, Chae J. Bright field microscopic cells counting method for BEVS using nonlinear convergence index sliding band filter. Biomed Eng Online 2014; 13:147. [PMID: 25342097 PMCID: PMC4221726 DOI: 10.1186/1475-925x-13-147] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2014] [Accepted: 09/02/2014] [Indexed: 11/29/2022] Open
Abstract
Background The Baculovirus Expression Vector System (BEVS) is a very popular expression vector system in gene engineering. An effective host cell line cultivation protocol can facilitate the baculovirus preparation and following experiments. However, the counting of the number of host cells in the protocol is usually performed by manual observation with microscopy, which is time consuming and labor intensive work, and prone to errors for one person or between different individuals. This study aims at giving a bright field insect cells counting protocol to help improve the efficient of BEVS. Method To develop a reliable and accurate counting method for the host cells in the bright field, such as Sf9 insect cells, a novel method based on a nonlinear Transformed Sliding Band Filter (TSBF) was proposed. And 3 collaborators counted cells at the same time to produce the ground truth for evaluation. The performance of TSBF method was evaluated with the image datasets of Sf9 insect cells according to the different periods of cell cultivation on the cell density, error rate and growth curve. Results The average error rate of our TSBF method is 2.21% on average, ranging from 0.89% to 3.97%, which exhibited an excellent performance with its high accuracy in lower error rate compared with traditional methods and manual counting. And the growth curve was much the manual method well. Conclusion Results suggest the proposed TSBF method can detect insect cells with low error rate, and it is suitable for the counting task in BEVS to take the place of manual counting by humans. Growth curve results can reflect the cells’ growth manner, which was generated by our proposed TSBF method in this paper can reflected the similar manner with it’s from the manual method. All of these proven that the proposed insect cell counting method can clearly improve the efficiency of BEVS.
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Affiliation(s)
| | - Kuanquan Wang
- Biocomputing Research Center, School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China.
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93
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Tokunaga T, Hirose O, Kawaguchi S, Toyoshima Y, Teramoto T, Ikebata H, Kuge S, Ishihara T, Iino Y, Yoshida R. Automated detection and tracking of many cells by using 4D live-cell imaging data. ACTA ACUST UNITED AC 2014; 30:i43-51. [PMID: 24932004 PMCID: PMC4058942 DOI: 10.1093/bioinformatics/btu271] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Motivation: Automated fluorescence microscopes produce massive amounts of images observing cells, often in four dimensions of space and time. This study addresses two tasks of time-lapse imaging analyses; detection and tracking of the many imaged cells, and it is especially intended for 4D live-cell imaging of neuronal nuclei of Caenorhabditis elegans. The cells of interest appear as slightly deformed ellipsoidal forms. They are densely distributed, and move rapidly in a series of 3D images. Thus, existing tracking methods often fail because more than one tracker will follow the same target or a tracker transits from one to other of different targets during rapid moves. Results: The present method begins by performing the kernel density estimation in order to convert each 3D image into a smooth, continuous function. The cell bodies in the image are assumed to lie in the regions near the multiple local maxima of the density function. The tasks of detecting and tracking the cells are then addressed with two hill-climbing algorithms. The positions of the trackers are initialized by applying the cell-detection method to an image in the first frame. The tracking method keeps attacking them to near the local maxima in each subsequent image. To prevent the tracker from following multiple cells, we use a Markov random field (MRF) to model the spatial and temporal covariation of the cells and to maximize the image forces and the MRF-induced constraint on the trackers. The tracking procedure is demonstrated with dynamic 3D images that each contain >100 neurons of C.elegans. Availability:http://daweb.ism.ac.jp/yoshidalab/crest/ismb2014 Supplementary information:Supplementary data are available at http://daweb.ism.ac.jp/yoshidalab/crest/ismb2014 Contact:yoshidar@ism.ac.jp
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Affiliation(s)
- Terumasa Tokunaga
- The Institute of Statistical Mathematics, Research Organization of Information and Systems, 10-3 Midori-cho, Tachikawa, Tokyo 190-8562, CREST, JST, Kanazawa University, Kakuma, Kanazawa 920-1192, The University of Tokyo, Building 3, 2-11-16 Yayoi, Bunkyo-ku, Tokyo 113-0032 Kyushu University, 6-10-1 Hakozaki, Higashi-ku, Fukuoka 812-8581, The Graduate University for Advanced Studies, 10-3 Midori-cho, Tachikawa, Tokyo 190-8562 and JST, ERATO, 2-2-2 Hikaridai Seika-cho, Soraku-gun, Kyoto-fu, JAPANThe Institute of Statistical Mathematics, Research Organization of Information and Systems, 10-3 Midori-cho, Tachikawa, Tokyo 190-8562, CREST, JST, Kanazawa University, Kakuma, Kanazawa 920-1192, The University of Tokyo, Building 3, 2-11-16 Yayoi, Bunkyo-ku, Tokyo 113-0032 Kyushu University, 6-10-1 Hakozaki, Higashi-ku, Fukuoka 812-8581, The Graduate University for Advanced Studies, 10-3 Midori-cho, Tachikawa, Tokyo 190-8562 and JST, ERATO, 2-2-2 Hikaridai Seika-cho, Soraku-gun, Kyoto-fu, JAPAN
| | - Osamu Hirose
- The Institute of Statistical Mathematics, Research Organization of Information and Systems, 10-3 Midori-cho, Tachikawa, Tokyo 190-8562, CREST, JST, Kanazawa University, Kakuma, Kanazawa 920-1192, The University of Tokyo, Building 3, 2-11-16 Yayoi, Bunkyo-ku, Tokyo 113-0032 Kyushu University, 6-10-1 Hakozaki, Higashi-ku, Fukuoka 812-8581, The Graduate University for Advanced Studies, 10-3 Midori-cho, Tachikawa, Tokyo 190-8562 and JST, ERATO, 2-2-2 Hikaridai Seika-cho, Soraku-gun, Kyoto-fu, JAPANThe Institute of Statistical Mathematics, Research Organization of Information and Systems, 10-3 Midori-cho, Tachikawa, Tokyo 190-8562, CREST, JST, Kanazawa University, Kakuma, Kanazawa 920-1192, The University of Tokyo, Building 3, 2-11-16 Yayoi, Bunkyo-ku, Tokyo 113-0032 Kyushu University, 6-10-1 Hakozaki, Higashi-ku, Fukuoka 812-8581, The Graduate University for Advanced Studies, 10-3 Midori-cho, Tachikawa, Tokyo 190-8562 and JST, ERATO, 2-2-2 Hikaridai Seika-cho, Soraku-gun, Kyoto-fu, JAPAN
| | - Shotaro Kawaguchi
- The Institute of Statistical Mathematics, Research Organization of Information and Systems, 10-3 Midori-cho, Tachikawa, Tokyo 190-8562, CREST, JST, Kanazawa University, Kakuma, Kanazawa 920-1192, The University of Tokyo, Building 3, 2-11-16 Yayoi, Bunkyo-ku, Tokyo 113-0032 Kyushu University, 6-10-1 Hakozaki, Higashi-ku, Fukuoka 812-8581, The Graduate University for Advanced Studies, 10-3 Midori-cho, Tachikawa, Tokyo 190-8562 and JST, ERATO, 2-2-2 Hikaridai Seika-cho, Soraku-gun, Kyoto-fu, JAPAN
| | - Yu Toyoshima
- The Institute of Statistical Mathematics, Research Organization of Information and Systems, 10-3 Midori-cho, Tachikawa, Tokyo 190-8562, CREST, JST, Kanazawa University, Kakuma, Kanazawa 920-1192, The University of Tokyo, Building 3, 2-11-16 Yayoi, Bunkyo-ku, Tokyo 113-0032 Kyushu University, 6-10-1 Hakozaki, Higashi-ku, Fukuoka 812-8581, The Graduate University for Advanced Studies, 10-3 Midori-cho, Tachikawa, Tokyo 190-8562 and JST, ERATO, 2-2-2 Hikaridai Seika-cho, Soraku-gun, Kyoto-fu, JAPANThe Institute of Statistical Mathematics, Research Organization of Information and Systems, 10-3 Midori-cho, Tachikawa, Tokyo 190-8562, CREST, JST, Kanazawa University, Kakuma, Kanazawa 920-1192, The University of Tokyo, Building 3, 2-11-16 Yayoi, Bunkyo-ku, Tokyo 113-0032 Kyushu University, 6-10-1 Hakozaki, Higashi-ku, Fukuoka 812-8581, The Graduate University for Advanced Studies, 10-3 Midori-cho, Tachikawa, Tokyo 190-8562 and JST, ERATO, 2-2-2 Hikaridai Seika-cho, Soraku-gun, Kyoto-fu, JAPAN
| | - Takayuki Teramoto
- The Institute of Statistical Mathematics, Research Organization of Information and Systems, 10-3 Midori-cho, Tachikawa, Tokyo 190-8562, CREST, JST, Kanazawa University, Kakuma, Kanazawa 920-1192, The University of Tokyo, Building 3, 2-11-16 Yayoi, Bunkyo-ku, Tokyo 113-0032 Kyushu University, 6-10-1 Hakozaki, Higashi-ku, Fukuoka 812-8581, The Graduate University for Advanced Studies, 10-3 Midori-cho, Tachikawa, Tokyo 190-8562 and JST, ERATO, 2-2-2 Hikaridai Seika-cho, Soraku-gun, Kyoto-fu, JAPANThe Institute of Statistical Mathematics, Research Organization of Information and Systems, 10-3 Midori-cho, Tachikawa, Tokyo 190-8562, CREST, JST, Kanazawa University, Kakuma, Kanazawa 920-1192, The University of Tokyo, Building 3, 2-11-16 Yayoi, Bunkyo-ku, Tokyo 113-0032 Kyushu University, 6-10-1 Hakozaki, Higashi-ku, Fukuoka 812-8581, The Graduate University for Advanced Studies, 10-3 Midori-cho, Tachikawa, Tokyo 190-8562 and JST, ERATO, 2-2-2 Hikaridai Seika-cho, Soraku-gun, Kyoto-fu, JAPAN
| | - Hisaki Ikebata
- The Institute of Statistical Mathematics, Research Organization of Information and Systems, 10-3 Midori-cho, Tachikawa, Tokyo 190-8562, CREST, JST, Kanazawa University, Kakuma, Kanazawa 920-1192, The University of Tokyo, Building 3, 2-11-16 Yayoi, Bunkyo-ku, Tokyo 113-0032 Kyushu University, 6-10-1 Hakozaki, Higashi-ku, Fukuoka 812-8581, The Graduate University for Advanced Studies, 10-3 Midori-cho, Tachikawa, Tokyo 190-8562 and JST, ERATO, 2-2-2 Hikaridai Seika-cho, Soraku-gun, Kyoto-fu, JAPAN
| | - Sayuri Kuge
- The Institute of Statistical Mathematics, Research Organization of Information and Systems, 10-3 Midori-cho, Tachikawa, Tokyo 190-8562, CREST, JST, Kanazawa University, Kakuma, Kanazawa 920-1192, The University of Tokyo, Building 3, 2-11-16 Yayoi, Bunkyo-ku, Tokyo 113-0032 Kyushu University, 6-10-1 Hakozaki, Higashi-ku, Fukuoka 812-8581, The Graduate University for Advanced Studies, 10-3 Midori-cho, Tachikawa, Tokyo 190-8562 and JST, ERATO, 2-2-2 Hikaridai Seika-cho, Soraku-gun, Kyoto-fu, JAPANThe Institute of Statistical Mathematics, Research Organization of Information and Systems, 10-3 Midori-cho, Tachikawa, Tokyo 190-8562, CREST, JST, Kanazawa University, Kakuma, Kanazawa 920-1192, The University of Tokyo, Building 3, 2-11-16 Yayoi, Bunkyo-ku, Tokyo 113-0032 Kyushu University, 6-10-1 Hakozaki, Higashi-ku, Fukuoka 812-8581, The Graduate University for Advanced Studies, 10-3 Midori-cho, Tachikawa, Tokyo 190-8562 and JST, ERATO, 2-2-2 Hikaridai Seika-cho, Soraku-gun, Kyoto-fu, JAPAN
| | - Takeshi Ishihara
- The Institute of Statistical Mathematics, Research Organization of Information and Systems, 10-3 Midori-cho, Tachikawa, Tokyo 190-8562, CREST, JST, Kanazawa University, Kakuma, Kanazawa 920-1192, The University of Tokyo, Building 3, 2-11-16 Yayoi, Bunkyo-ku, Tokyo 113-0032 Kyushu University, 6-10-1 Hakozaki, Higashi-ku, Fukuoka 812-8581, The Graduate University for Advanced Studies, 10-3 Midori-cho, Tachikawa, Tokyo 190-8562 and JST, ERATO, 2-2-2 Hikaridai Seika-cho, Soraku-gun, Kyoto-fu, JAPANThe Institute of Statistical Mathematics, Research Organization of Information and Systems, 10-3 Midori-cho, Tachikawa, Tokyo 190-8562, CREST, JST, Kanazawa University, Kakuma, Kanazawa 920-1192, The University of Tokyo, Building 3, 2-11-16 Yayoi, Bunkyo-ku, Tokyo 113-0032 Kyushu University, 6-10-1 Hakozaki, Higashi-ku, Fukuoka 812-8581, The Graduate University for Advanced Studies, 10-3 Midori-cho, Tachikawa, Tokyo 190-8562 and JST, ERATO, 2-2-2 Hikaridai Seika-cho, Soraku-gun, Kyoto-fu, JAPAN
| | - Yuichi Iino
- The Institute of Statistical Mathematics, Research Organization of Information and Systems, 10-3 Midori-cho, Tachikawa, Tokyo 190-8562, CREST, JST, Kanazawa University, Kakuma, Kanazawa 920-1192, The University of Tokyo, Building 3, 2-11-16 Yayoi, Bunkyo-ku, Tokyo 113-0032 Kyushu University, 6-10-1 Hakozaki, Higashi-ku, Fukuoka 812-8581, The Graduate University for Advanced Studies, 10-3 Midori-cho, Tachikawa, Tokyo 190-8562 and JST, ERATO, 2-2-2 Hikaridai Seika-cho, Soraku-gun, Kyoto-fu, JAPANThe Institute of Statistical Mathematics, Research Organization of Information and Systems, 10-3 Midori-cho, Tachikawa, Tokyo 190-8562, CREST, JST, Kanazawa University, Kakuma, Kanazawa 920-1192, The University of Tokyo, Building 3, 2-11-16 Yayoi, Bunkyo-ku, Tokyo 113-0032 Kyushu University, 6-10-1 Hakozaki, Higashi-ku, Fukuoka 812-8581, The Graduate University for Advanced Studies, 10-3 Midori-cho, Tachikawa, Tokyo 190-8562 and JST, ERATO, 2-2-2 Hikaridai Seika-cho, Soraku-gun, Kyoto-fu, JAPAN
| | - Ryo Yoshida
- The Institute of Statistical Mathematics, Research Organization of Information and Systems, 10-3 Midori-cho, Tachikawa, Tokyo 190-8562, CREST, JST, Kanazawa University, Kakuma, Kanazawa 920-1192, The University of Tokyo, Building 3, 2-11-16 Yayoi, Bunkyo-ku, Tokyo 113-0032 Kyushu University, 6-10-1 Hakozaki, Higashi-ku, Fukuoka 812-8581, The Graduate University for Advanced Studies, 10-3 Midori-cho, Tachikawa, Tokyo 190-8562 and JST, ERATO, 2-2-2 Hikaridai Seika-cho, Soraku-gun, Kyoto-fu, JAPANThe Institute of Statistical Mathematics, Research Organization of Information and Systems, 10-3 Midori-cho, Tachikawa, Tokyo 190-8562, CREST, JST, Kanazawa University, Kakuma, Kanazawa 920-1192, The University of Tokyo, Building 3, 2-11-16 Yayoi, Bunkyo-ku, Tokyo 113-0032 Kyushu University, 6-10-1 Hakozaki, Higashi-ku, Fukuoka 812-8581, The Graduate University for Advanced Studies, 10-3 Midori-cho, Tachikawa, Tokyo 190-8562 and JST, ERATO, 2-2-2 Hikaridai Seika-cho, Soraku-gun, Kyoto-fu, JAPANThe Institute of Statistical Mathematics, Research Organization of Information and Systems, 10-3 Midori-cho, Tachikawa, Tokyo 190-8562, CREST, JST, Kanazawa University, Kakuma, Kanazawa 920-1192, The University of Tokyo, Building 3, 2-11-16 Yayoi, Bunkyo-ku, Tokyo 113-0032 Kyushu University, 6-10-1 Hakozaki, Higashi-ku, Fukuoka 812-8581, The Graduate University for Advanced Studies, 10-3 Midori-cho, Tachikawa, Tokyo 190-8562 and JST, ERATO, 2-2-2 Hikaridai Seika-cho, Soraku-gun, Kyoto-fu, JAPANThe Institute of Statistical Mathematics, Research Organization of Information and Systems, 10-3 Midori-cho, Tachikawa, Tokyo 190-8562, CREST, JST, Kanazawa University, Kakuma, Kanazawa 920-1192, The University of Tokyo, Building 3, 2-11-16 Yayoi, Bunkyo-ku, Tokyo 113-0032 Kyushu University, 6-10-1 Hakozaki, Higashi-ku, Fukuoka 812-8581, The Graduate University for Advanced Studies, 10-3 Midori-cho, Tachikawa, Tokyo 190-8562 and JST, ERATO, 2-2-2 Hikaridai Seika-cho, Soraku-gun, Kyoto-fu, JAPAN
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94
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Gao J, Guildenbecher DR, Engvall L, Reu PL, Chen J. Refinement of particle detection by the hybrid method in digital in-line holography. APPLIED OPTICS 2014; 53:G130-G138. [PMID: 25322121 DOI: 10.1364/ao.53.00g130] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/08/2014] [Accepted: 07/02/2014] [Indexed: 06/04/2023]
Abstract
Digital in-line holography provides simultaneous particle size and three-dimensional position measurements. In general, the measurement accuracy varies locally, and tends to decrease where particles are closely spaced, due to noise resulting from diffraction by adjacent particles. Aggravating the situation is the identification of transversely adjoining particles as a single particle, which introduces significant errors in both size and position measurements. Here, we develop a refinement procedure that distinguishes such erroneous particles from accurately detected ones and further separates individual particles. Effectiveness of the refinement is characterized using simulations, experimental holograms of calibration fields, and a few practical applications to liquid breakup. Significant improvements in the accuracy of the measured particle sizes, positions, and displacements confirm the usefulness of the proposed method.
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95
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Tafavogh S, Catchpoole DR, Kennedy PJ. Cellular quantitative analysis of neuroblastoma tumor and splitting overlapping cells. BMC Bioinformatics 2014; 15:272. [PMID: 25109603 PMCID: PMC4139617 DOI: 10.1186/1471-2105-15-272] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2014] [Accepted: 07/25/2014] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Neuroblastoma Tumor (NT) is one of the most aggressive types of infant cancer. Essential to accurate diagnosis and prognosis is cellular quantitative analysis of the tumor. Counting enormous numbers of cells under an optical microscope is error-prone. There is therefore an urgent demand from pathologists for robust and automated cell counting systems. However, the main challenge in developing these systems is the inability of them to distinguish between overlapping cells and single cells, and to split the overlapping cells. We address this challenge in two stages by: 1) distinguishing overlapping cells from single cells using the morphological differences between them such as area, uniformity of diameters and cell concavity; and 2) splitting overlapping cells into single cells. We propose a novel approach by using the dominant concave regions of cells as markers to identify the overlap region. We then find the initial splitting points at the critical points of the concave regions by decomposing the concave regions into their components such as arcs, chords and edges, and the distance between the components is analyzed using the developed seed growing technique. Lastly, a shortest path determination approach is developed to determine the optimum splitting route between two candidate initial splitting points. RESULTS We compare the cell counting results of our system with those of a pathologist as the ground-truth. We also compare the system with three state-of-the-art methods, and the results of statistical tests show a significant improvement in the performance of our system compared to state-of-the-art methods. The F-measure obtained by our system is 88.70%. To evaluate the generalizability of our algorithm, we apply it to images of follicular lymphoma, which has similar histological regions to NT. Of the algorithms tested, our algorithm obtains the highest F-measure of 92.79%. CONCLUSION We develop a novel overlapping cell splitting algorithm to enhance the cellular quantitative analysis of infant neuroblastoma. The performance of the proposed algorithm promises a reliable automated cell counting system for pathology laboratories. Moreover, the high performance obtained by our algorithm for images of follicular lymphoma demonstrates the generalization of the proposed algorithm for cancers with similar histological regions and histological structures.
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Affiliation(s)
- Siamak Tafavogh
- Centre for Quantum Computation and Intelligent Systems (QCIS), Faculty of Engineering and IT, University of Technology, Sydney, PO Box 123, Broadway, NSW 2007 Sydney, Australia.
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96
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Su H, Xing F, Lee JD, Peterson CA, Yang L. Automatic Myonuclear Detection in Isolated Single Muscle Fibers Using Robust Ellipse Fitting and Sparse Representation. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2014; 11:714-726. [PMID: 26356342 PMCID: PMC4669954 DOI: 10.1109/tcbb.2013.151] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/01/2023]
Abstract
Accurate and robust detection of myonuclei in isolated single muscle fibers is required to calculate myonuclear domain size. However, this task is challenging because: 1) shape and size variations of the nuclei, 2) overlapping nuclear clumps, and 3) multiple z-stack images with out-of-focus regions. In this paper, we have proposed a novel automatic detection algorithm to robustly quantify myonuclei in isolated single skeletal muscle fibers. The original z-stack images are first converted into one all-in-focus image using multi-focus image fusion. A sufficient number of ellipse fitting hypotheses are then generated from the myonuclei contour segments using heteroscedastic errors-in-variables (HEIV) regression. A set of representative training samples and a set of discriminative features are selected by a two-stage sparse model. The selected samples with representative features are utilized to train a classifier to select the best candidates. A modified inner geodesic distance based mean-shift clustering algorithm is used to produce the final nuclei detection results. The proposed method was extensively tested using 42 sets of z-stack images containing over 1,500 myonuclei. The method demonstrates excellent results that are better than current state-of-the-art approaches.
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97
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Berginski ME, Creed SJ, Cochran S, Roadcap DW, Bear JE, Gomez SM. Automated analysis of invadopodia dynamics in live cells. PeerJ 2014; 2:e462. [PMID: 25071988 PMCID: PMC4103095 DOI: 10.7717/peerj.462] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2014] [Accepted: 06/09/2014] [Indexed: 01/07/2023] Open
Abstract
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.
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Affiliation(s)
- Matthew E Berginski
- UNC/NCSU Joint Department of Biomedical Engineering, University of North Carolina at Chapel Hill , Chapel Hill, NC , USA
| | - Sarah J Creed
- Department of Cell Biology and Physiology, University of North Carolina at Chapel Hill , Chapel Hill, NC , USA
| | - Shelly Cochran
- UNC/NCSU Joint Department of Biomedical Engineering, University of North Carolina at Chapel Hill , Chapel Hill, NC , USA
| | - David W Roadcap
- Department of Cell Biology and Physiology, University of North Carolina at Chapel Hill , Chapel Hill, NC , USA
| | - James E Bear
- Department of Cell Biology and Physiology, University of North Carolina at Chapel Hill , Chapel Hill, NC , USA ; Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill , Chapel Hill, NC , USA ; Howard Hughes Medical Institute , Chevy Chase, MD , USA
| | - Shawn M Gomez
- UNC/NCSU Joint Department of Biomedical Engineering, University of North Carolina at Chapel Hill , Chapel Hill, NC , USA ; Department of Computer Science, University of North Carolina at Chapel Hill , Chapel Hill, NC , USA ; Department of Pharmacology, University of North Carolina at Chapel Hill , Chapel Hill, NC , USA
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98
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Wang Y, Jeong Y, Jhiang SM, Yu L, Menq CH. Quantitative characterization of cell behaviors through cell cycle progression via automated cell tracking. PLoS One 2014; 9:e98762. [PMID: 24911281 PMCID: PMC4049640 DOI: 10.1371/journal.pone.0098762] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2013] [Accepted: 05/07/2014] [Indexed: 01/23/2023] Open
Abstract
Cell behaviors are reflections of intracellular tension dynamics and play important roles in many cellular processes. In this study, temporal variations in cell geometry and cell motion through cell cycle progression were quantitatively characterized via automated cell tracking for MCF-10A non-transformed breast cells, MCF-7 non-invasive breast cancer cells, and MDA-MB-231 highly metastatic breast cancer cells. A new cell segmentation method, which combines the threshold method and our modified edge based active contour method, was applied to optimize cell boundary detection for all cells in the field-of-view. An automated cell-tracking program was implemented to conduct live cell tracking over 40 hours for the three cell lines. The cell boundary and location information was measured and aligned with cell cycle progression with constructed cell lineage trees. Cell behaviors were studied in terms of cell geometry and cell motion. For cell geometry, cell area and cell axis ratio were investigated. For cell motion, instantaneous migration speed, cell motion type, as well as cell motion range were analyzed. We applied a cell-based approach that allows us to examine and compare temporal variations of cell behavior along with cell cycle progression at a single cell level. Cell body geometry along with distribution of peripheral protrusion structures appears to be associated with cell motion features. Migration speed together with motion type and motion ranges are required to distinguish the three cell-lines examined. We found that cells dividing or overlapping vertically are unique features of cell malignancy for both MCF-7 and MDA-MB-231 cells, whereas abrupt changes in cell body geometry and cell motion during mitosis are unique to highly metastatic MDA-MB-231 cells. Taken together, our live cell tracking system serves as an invaluable tool to identify cell behaviors that are unique to malignant and/or highly metastatic breast cancer cells.
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Affiliation(s)
- Yuliang Wang
- Precision Measurement and Control Laboratory, Department of Mechanical and Aerospace Engineering, The Ohio State University, Columbus, Ohio, United States of America
- The Ohio State University Comprehensive Cancer Center, The Ohio State University, Columbus, Ohio, United States of America
| | - Younkoo Jeong
- Precision Measurement and Control Laboratory, Department of Mechanical and Aerospace Engineering, The Ohio State University, Columbus, Ohio, United States of America
| | - Sissy M. Jhiang
- The Ohio State University Comprehensive Cancer Center, The Ohio State University, Columbus, Ohio, United States of America
- Department of Physiology and Cell Biology, The Ohio State University, Columbus, Ohio, United States of America
| | - Lianbo Yu
- Center for Biostatistics, The Ohio State University, Columbus, Ohio, United States of America
| | - Chia-Hsiang Menq
- Precision Measurement and Control Laboratory, Department of Mechanical and Aerospace Engineering, The Ohio State University, Columbus, Ohio, United States of America
- * E-mail:
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Quan T, Li J, Zhou H, Li S, Zheng T, Yang Z, Luo Q, Gong H, Zeng S. Digital reconstruction of the cell body in dense neural circuits using a spherical-coordinated variational model. Sci Rep 2014; 4:4970. [PMID: 24829141 PMCID: PMC4021323 DOI: 10.1038/srep04970] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2013] [Accepted: 04/09/2014] [Indexed: 02/03/2023] Open
Abstract
Mapping the neuronal circuits is essential to understand brain function. Recent technological advancements have made it possible to acquire the brain atlas at single cell resolution. Digital reconstruction of the neural circuits down to this level across the whole brain would significantly facilitate brain studies. However, automatic reconstruction of the dense neural connections from microscopic image still remains a challenge. Here we developed a spherical-coordinate based variational model to reconstruct the shape of the cell body i.e. soma, as one of the procedures for this purpose. When intuitively processing the volumetric images in the spherical coordinate system, the reconstruction of somas with variational model is no longer sensitive to the interference of the complicated neuronal morphology, and could automatically and robustly achieve accurate soma shape regardless of the dense spatial distribution, and diversity in cell size, and morphology. We believe this method would speed drawing the neural circuits and boost brain studies.
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Affiliation(s)
- Tingwei Quan
- Britton Chance Center for Biomedical Photonics, Huazhong University of Science and Technology- Wuhan National Laboratory for Optoelectronics, Wuhan 430074, China
- MoE Key Laboratory for Biomedical Photonics, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
- School of Mathematics and Statistics, Hubei University of Education, Wuhan 430205, China
- These authors contributed equally to this work
| | - Jing Li
- Britton Chance Center for Biomedical Photonics, Huazhong University of Science and Technology- Wuhan National Laboratory for Optoelectronics, Wuhan 430074, China
- MoE Key Laboratory for Biomedical Photonics, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
- These authors contributed equally to this work
| | - Hang Zhou
- Britton Chance Center for Biomedical Photonics, Huazhong University of Science and Technology- Wuhan National Laboratory for Optoelectronics, Wuhan 430074, China
- MoE Key Laboratory for Biomedical Photonics, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Shiwei Li
- Britton Chance Center for Biomedical Photonics, Huazhong University of Science and Technology- Wuhan National Laboratory for Optoelectronics, Wuhan 430074, China
- MoE Key Laboratory for Biomedical Photonics, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Ting Zheng
- Britton Chance Center for Biomedical Photonics, Huazhong University of Science and Technology- Wuhan National Laboratory for Optoelectronics, Wuhan 430074, China
- MoE Key Laboratory for Biomedical Photonics, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Zhongqing Yang
- Britton Chance Center for Biomedical Photonics, Huazhong University of Science and Technology- Wuhan National Laboratory for Optoelectronics, Wuhan 430074, China
- MoE Key Laboratory for Biomedical Photonics, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Qingming Luo
- Britton Chance Center for Biomedical Photonics, Huazhong University of Science and Technology- Wuhan National Laboratory for Optoelectronics, Wuhan 430074, China
- MoE Key Laboratory for Biomedical Photonics, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Hui Gong
- Britton Chance Center for Biomedical Photonics, Huazhong University of Science and Technology- Wuhan National Laboratory for Optoelectronics, Wuhan 430074, China
- MoE Key Laboratory for Biomedical Photonics, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Shaoqun Zeng
- Britton Chance Center for Biomedical Photonics, Huazhong University of Science and Technology- Wuhan National Laboratory for Optoelectronics, Wuhan 430074, China
- MoE Key Laboratory for Biomedical Photonics, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
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Saraswat M, Arya KV. Automated microscopic image analysis for leukocytes identification: a survey. Micron 2014; 65:20-33. [PMID: 25041828 DOI: 10.1016/j.micron.2014.04.001] [Citation(s) in RCA: 56] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2013] [Revised: 01/30/2014] [Accepted: 04/01/2014] [Indexed: 02/07/2023]
Abstract
Automatic quantification and classification of leukocytes in microscopic images are of paramount importance in the perspective of disease identification, its progress and drugs development. Extracting numerical values of leukocytes from microscopic images of blood or tissue sections represents a tricky challenge. Research efforts in quantification of these cells include normalization of images, segmentation of its nuclei and cytoplasm followed by their classification. However, there are several related problems viz., coarse background, overlapped nuclei, conversion of 3-D nuclei into 2-D nuclei etc. In this review, we have categorized, evaluated, and discussed recently developed methods for leukocyte identification. After reviewing these methods and finding their constraints, a future research perspective has been presented. Further, the challenges faced by the pathologists with respect to these problems are also discussed.
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Affiliation(s)
- Mukesh Saraswat
- ABV-Indian Institute of Information Technology and Management, Gwalior, India.
| | - K V Arya
- ABV-Indian Institute of Information Technology and Management, Gwalior, India.
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