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Alterations in the properties of the cell membrane due to glycosphingolipid accumulation in a model of Gaucher disease. Sci Rep 2018; 8:157. [PMID: 29317695 PMCID: PMC5760709 DOI: 10.1038/s41598-017-18405-8] [Citation(s) in RCA: 47] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2017] [Accepted: 12/11/2017] [Indexed: 01/07/2023] Open
Abstract
Gaucher disease is a lysosomal storage disease characterized by the malfunction of glucocerebrosidase resulting in the accumulation of glucosylceramide and other sphingolipids in certain cells. Although the disease symptoms are usually attributed to the storage of undigested substrate in lysosomes, here we show that glycosphingolipids accumulating in the plasma membrane cause profound changes in the properties of the membrane. The fluidity of the sphingolipid-enriched membrane decreased accompanied by the enlargement of raft-like ordered membrane domains. The mobility of non-raft proteins and lipids was severely restricted, while raft-resident components were only mildly affected. The rate of endocytosis of transferrin receptor, a non-raft protein, was significantly retarded in Gaucher cells, while the endocytosis of the raft-associated GM1 ganglioside was unaffected. Interferon-γ-induced STAT1 phosphorylation was also significantly inhibited in Gaucher cells. Atomic force microscopy revealed that sphingolipid accumulation was associated with a more compliant membrane capable of producing an increased number of nanotubes. The results imply that glycosphingolipid accumulation in the plasma membrane has significant effects on membrane properties, which may be important in the pathogenesis of Gaucher disease.
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52
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Wang Y, Wang C, Zhang Z. Segmentation of clustered cells in negative phase contrast images with integrated light intensity and cell shape information. J Microsc 2017; 270:188-199. [PMID: 29280132 DOI: 10.1111/jmi.12673] [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: 08/04/2017] [Revised: 10/01/2017] [Accepted: 11/27/2017] [Indexed: 11/28/2022]
Abstract
Automated cell segmentation plays a key role in characterisations of cell behaviours for both biology research and clinical practices. Currently, the segmentation of clustered cells still remains as a challenge and is the main reason for false segmentation. In this study, the emphasis was put on the segmentation of clustered cells in negative phase contrast images. A new method was proposed to combine both light intensity and cell shape information through the construction of grey-weighted distance transform (GWDT) within preliminarily segmented areas. With the constructed GWDT, the clustered cells can be detected and then separated with a modified region skeleton-based method. Moreover, a contour expansion operation was applied to get optimised detection of cell boundaries. In this paper, the working principle and detailed procedure of the proposed method are described, followed by the evaluation of the method on clustered cell segmentation. Results show that the proposed method achieves an improved performance in clustered cell segmentation compared with other methods, with 85.8% and 97.16% accuracy rate for clustered cells and all cells, respectively.
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Affiliation(s)
- Y Wang
- School of Mechanical Engineering and Automation, Robotics Institute, Beihang University, Beijing, China
| | - C Wang
- School of Mechanical Engineering and Automation, Robotics Institute, Beihang University, Beijing, China
| | - Z Zhang
- Université de Bordeaux & CNRS, LOMA, Talence, France
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53
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Tsujikawa T, Kumar S, Borkar RN, Azimi V, Thibault G, Chang YH, Balter A, Kawashima R, Choe G, Sauer D, El Rassi E, Clayburgh DR, Kulesz-Martin MF, Lutz ER, Zheng L, Jaffee EM, Leyshock P, Margolin AA, Mori M, Gray JW, Flint PW, Coussens LM. Quantitative Multiplex Immunohistochemistry Reveals Myeloid-Inflamed Tumor-Immune Complexity Associated with Poor Prognosis. Cell Rep 2017; 19:203-217. [PMID: 28380359 DOI: 10.1016/j.celrep.2017.03.037] [Citation(s) in RCA: 399] [Impact Index Per Article: 57.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2016] [Revised: 02/04/2017] [Accepted: 03/10/2017] [Indexed: 12/11/2022] Open
Abstract
Here, we describe a multiplexed immunohistochemical platform with computational image processing workflows, including image cytometry, enabling simultaneous evaluation of 12 biomarkers in one formalin-fixed paraffin-embedded tissue section. To validate this platform, we used tissue microarrays containing 38 archival head and neck squamous cell carcinomas and revealed differential immune profiles based on lymphoid and myeloid cell densities, correlating with human papilloma virus status and prognosis. Based on these results, we investigated 24 pancreatic ductal adenocarcinomas from patients who received neoadjuvant GVAX vaccination and revealed that response to therapy correlated with degree of mono-myelocytic cell density and percentages of CD8+ T cells expressing T cell exhaustion markers. These data highlight the utility of in situ immune monitoring for patient stratification and provide digital image processing pipelines to the community for examining immune complexity in precious tissue sections, where phenotype and tissue architecture are preserved to improve biomarker discovery and assessment.
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Affiliation(s)
- Takahiro Tsujikawa
- Department of Cell, Developmental & Cancer Biology, Oregon Health and Science University, Portland, OR 97239, USA; Department of Otolaryngology-Head and Neck Surgery, Oregon Health and Science University, Portland, OR 97239, USA
| | - Sushil Kumar
- Department of Cell, Developmental & Cancer Biology, Oregon Health and Science University, Portland, OR 97239, USA
| | - Rohan N Borkar
- Intel Health and Life Sciences, Intel Corporation, Hillsboro, OR 97124, USA
| | - Vahid Azimi
- Intel Health and Life Sciences, Intel Corporation, Hillsboro, OR 97124, USA
| | - Guillaume Thibault
- Department of Biomedical Engineering, Oregon Health and Science University, Portland, OR 97239, USA
| | - Young Hwan Chang
- Department of Biomedical Engineering, Oregon Health and Science University, Portland, OR 97239, USA; Department of Computational Biology, Oregon Health and Science University, Portland, OR 97239, USA
| | - Ariel Balter
- Department of Computational Biology, Oregon Health and Science University, Portland, OR 97239, USA
| | - Rie Kawashima
- Department of Cell, Developmental & Cancer Biology, Oregon Health and Science University, Portland, OR 97239, USA
| | - Gina Choe
- Department of Cell, Developmental & Cancer Biology, Oregon Health and Science University, Portland, OR 97239, USA
| | - David Sauer
- Department of Pathology, Oregon Health and Science University, Portland, OR 97239, USA
| | - Edward El Rassi
- Department of Otolaryngology-Head and Neck Surgery, Oregon Health and Science University, Portland, OR 97239, USA
| | - Daniel R Clayburgh
- Department of Otolaryngology-Head and Neck Surgery, Oregon Health and Science University, Portland, OR 97239, USA; Knight Cancer Institute, Oregon Health and Science University, Portland, OR 97239, USA
| | - Molly F Kulesz-Martin
- Department of Cell, Developmental & Cancer Biology, Oregon Health and Science University, Portland, OR 97239, USA; Department of Dermatology, Oregon Health and Science University, Portland, OR 97239, USA; Knight Cancer Institute, Oregon Health and Science University, Portland, OR 97239, USA
| | - Eric R Lutz
- Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA; The Sidney Kimmel Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA; The Skip Viragh Center for Pancreatic Cancer Research and Clinical Care, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Lei Zheng
- Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA; Department of Surgery, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA; The Sidney Kimmel Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA; The Skip Viragh Center for Pancreatic Cancer Research and Clinical Care, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Elizabeth M Jaffee
- Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA; Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA; The Sidney Kimmel Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA; The Skip Viragh Center for Pancreatic Cancer Research and Clinical Care, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Patrick Leyshock
- Department of Computational Biology, Oregon Health and Science University, Portland, OR 97239, USA
| | - Adam A Margolin
- Department of Computational Biology, Oregon Health and Science University, Portland, OR 97239, USA; OHSU Center for Spatial Systems Biomedicine, Oregon Health and Science University, Portland, OR 97239, USA; Knight Cancer Institute, Oregon Health and Science University, Portland, OR 97239, USA
| | - Motomi Mori
- School of Public Health, Oregon Health and Science University, Portland, OR 97239, USA; Knight Cancer Institute, Oregon Health and Science University, Portland, OR 97239, USA
| | - Joe W Gray
- Department of Biomedical Engineering, Oregon Health and Science University, Portland, OR 97239, USA; OHSU Center for Spatial Systems Biomedicine, Oregon Health and Science University, Portland, OR 97239, USA; Knight Cancer Institute, Oregon Health and Science University, Portland, OR 97239, USA
| | - Paul W Flint
- Department of Otolaryngology-Head and Neck Surgery, Oregon Health and Science University, Portland, OR 97239, USA; Knight Cancer Institute, Oregon Health and Science University, Portland, OR 97239, USA
| | - Lisa M Coussens
- Department of Cell, Developmental & Cancer Biology, Oregon Health and Science University, Portland, OR 97239, USA; Knight Cancer Institute, Oregon Health and Science University, Portland, OR 97239, USA.
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54
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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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55
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56
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Tajada S, Moreno CM, O'Dwyer S, Woods S, Sato D, Navedo MF, Santana LF. Distance constraints on activation of TRPV4 channels by AKAP150-bound PKCα in arterial myocytes. J Gen Physiol 2017; 149:639-659. [PMID: 28507079 PMCID: PMC5460949 DOI: 10.1085/jgp.201611709] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2016] [Revised: 03/03/2017] [Accepted: 04/27/2017] [Indexed: 11/20/2022] Open
Abstract
Vascular smooth muscle tone can be regulated by angiotensin II, which enhances TRPV4 channel activity via AKAP150-bound protein kinase C. Tajada et al. show that the effect of AKAP150 on TRPV4 channels is inversely proportional to the distance between them, which varies with sex and arterial bed. TRPV4 (transient receptor potential vanilloid 4) channels are Ca2+-permeable channels that play a key role in regulating vascular tone. In arterial myocytes, opening of TRPV4 channels creates local increases in Ca2+ influx, detectable optically as “TRPV4 sparklets.” TRPV4 sparklet activity can be enhanced by the action of the vasoconstrictor angiotensin II (AngII). This modulation depends on the activation of subcellular signaling domains that comprise protein kinase C α (PKCα) bound to the anchoring protein AKAP150. Here, we used super-resolution nanoscopy, patch-clamp electrophysiology, Ca2+ imaging, and mathematical modeling approaches to test the hypothesis that AKAP150-dependent modulation of TRPV4 channels is critically dependent on the distance between these two proteins in the sarcolemma of arterial myocytes. Our data show that the distance between AKAP150 and TRPV4 channel clusters varies with sex and arterial bed. Consistent with our hypothesis, we further find that basal and AngII-induced TRPV4 channel activity decays exponentially as the distance between TRPV4 and AKAP150 increases. Our data suggest a maximum radius of action of ∼200 nm for local modulation of TRPV4 channels by AKAP150-associated PKCα.
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Affiliation(s)
- Sendoa Tajada
- Department of Physiology and Membrane Biology, University of California Davis School of Medicine, Davis, CA
| | - Claudia M Moreno
- Department of Physiology and Membrane Biology, University of California Davis School of Medicine, Davis, CA
| | - Samantha O'Dwyer
- Department of Physiology and Membrane Biology, University of California Davis School of Medicine, Davis, CA
| | - Sean Woods
- Department of Physiology and Membrane Biology, University of California Davis School of Medicine, Davis, CA
| | - Daisuke Sato
- Department of Pharmacology, University of California Davis School of Medicine, Davis, CA
| | - Manuel F Navedo
- Department of Pharmacology, University of California Davis School of Medicine, Davis, CA
| | - L Fernando Santana
- Department of Physiology and Membrane Biology, University of California Davis School of Medicine, Davis, CA
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57
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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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58
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Gandhamal A, Talbar S, Gajre S, Hani AFM, Kumar D. Local gray level S-curve transformation – A generalized contrast enhancement technique for medical images. Comput Biol Med 2017; 83:120-133. [DOI: 10.1016/j.compbiomed.2017.03.001] [Citation(s) in RCA: 30] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2016] [Revised: 02/09/2017] [Accepted: 03/01/2017] [Indexed: 10/20/2022]
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59
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Yi F, Huang J, Yang L, Xie Y, Xiao G. Automatic extraction of cell nuclei from H&E-stained histopathological images. J Med Imaging (Bellingham) 2017; 4:027502. [PMID: 28653017 PMCID: PMC5478972 DOI: 10.1117/1.jmi.4.2.027502] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2017] [Accepted: 05/31/2017] [Indexed: 12/15/2022] Open
Abstract
Extraction of cell nuclei from hematoxylin and eosin (H&E)-stained histopathological images is an essential preprocessing step in computerized image analysis for disease detection, diagnosis, and prognosis. We present an automated cell nuclei segmentation approach that works with H&E-stained images. A color deconvolution algorithm was first applied to the image to get the hematoxylin channel. Using a morphological operation and thresholding technique on the hematoxylin channel image, candidate target nuclei and background regions were detected, which were then used as markers for a marker-controlled watershed transform segmentation algorithm. Moreover, postprocessing was conducted to split the touching nuclei. For each segmented region from the previous steps, the regional maximum value positions were identified as potential nuclei centers. These maximum values were further grouped into [Formula: see text]-clusters, and the locations within each cluster were connected with the minimum spanning tree technique. Then, these connected positions were utilized as new markers for a watershed segmentation approach. The final number of nuclei at each region was determined by minimizing an objective function that iterated all of the possible [Formula: see text]-values. The proposed method was applied to the pathological images of the tumor tissues from The Cancer Genome Atlas study. Experimental results show that the proposed method can lead to promising results in terms of segmentation accuracy and separation of touching nuclei.
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Affiliation(s)
- Faliu Yi
- University of Texas Southwestern Medical Center, Quantitative Biomedical Research Center, Department of Clinical Science, Dallas, Texas, United States
| | - Junzhou Huang
- University of Texas at Arlington, Department of Computer Science and Engineering, Arlington, Texas, United States
| | - Lin Yang
- University of Texas Southwestern Medical Center, Quantitative Biomedical Research Center, Department of Clinical Science, Dallas, Texas, United States
- Chinese Academy of Medical Science and Peking Union Medical College, National Cancer Center/Cancer Hospital, Department of Pathology, Chaoyang District, Beijing, China
| | - Yang Xie
- University of Texas Southwestern Medical Center, Quantitative Biomedical Research Center, Department of Clinical Science, Dallas, Texas, United States
- University of Texas Southwestern Medical Center, Department of Bioinformatics, Dallas, Texas, United States
- University of Texas Southwestern Medical Center, Harold C. Simmons Comprehensive Cancer Center, Dallas, Texas, United States
| | - Guanghua Xiao
- University of Texas Southwestern Medical Center, Quantitative Biomedical Research Center, Department of Clinical Science, Dallas, Texas, United States
- University of Texas Southwestern Medical Center, Department of Bioinformatics, Dallas, Texas, United States
- University of Texas Southwestern Medical Center, Harold C. Simmons Comprehensive Cancer Center, Dallas, Texas, United States
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60
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A novel toolbox to investigate tissue spatial organization applied to the study of the islets of Langerhans. Sci Rep 2017; 7:44261. [PMID: 28303903 PMCID: PMC5355872 DOI: 10.1038/srep44261] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2016] [Accepted: 02/07/2017] [Indexed: 12/20/2022] Open
Abstract
Thanks to the development of new 3D Imaging techniques, volumetric data of thick samples, especially tissues, are commonly available. Several algorithms were proposed to analyze cells or nuclei in tissues, however these tools are limited to two dimensions. Within any given tissue, cells are not likely to be organized randomly and as such have specific patterns of cell-cell interaction forming complex communication networks. In this paper, we propose a new set of tools as an approach to segment and analyze tissues in 3D with single cell resolution. This new tool box can identify and compute the geographical location of single cells and analyze the potential physical interactions between different cell types and in 3D. As a proof-of-principle, we applied our methodology to investigation of the cyto-architecture of the islets of Langerhans in mice and monkeys. The results obtained here are a significant improvement in current methodologies and provides new insight into the organization of alpha cells and their cellular interactions within the islet’s cellular framework.
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61
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Robust detection and segmentation of cell nuclei in biomedical images based on a computational topology framework. Med Image Anal 2017; 38:90-103. [PMID: 28314191 DOI: 10.1016/j.media.2017.02.009] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2016] [Revised: 02/20/2017] [Accepted: 02/28/2017] [Indexed: 12/14/2022]
Abstract
The segmentation of cell nuclei is an important step towards the automated analysis of histological images. The presence of a large number of nuclei in whole-slide images necessitates methods that are computationally tractable in addition to being effective. In this work, a method is developed for the robust segmentation of cell nuclei in histological images based on the principles of persistent homology. More specifically, an abstract simplicial homology approach for image segmentation is established. Essentially, the approach deals with the persistence of disconnected sets in the image, thus identifying salient regions that express patterns of persistence. By introducing an image representation based on topological features, the task of segmentation is less dependent on variations of color or texture. This results in a novel approach that generalizes well and provides stable performance. The method conceptualizes regions of interest (cell nuclei) pertinent to their topological features in a successful manner. The time cost of the proposed approach is lower-bounded by an almost linear behavior and upper-bounded by O(n2) in a worst-case scenario. Time complexity matches a quasilinear behavior which is O(n1+ɛ) for ε < 1. Images acquired from histological sections of liver tissue are used as a case study to demonstrate the effectiveness of the approach. The histological landscape consists of hepatocytes and non-parenchymal cells. The accuracy of the proposed methodology is verified against an automated workflow created by the output of a conventional filter bank (validated by experts) and the supervised training of a random forest classifier. The results are obtained on a per-object basis. The proposed workflow successfully detected both hepatocyte and non-parenchymal cell nuclei with an accuracy of 84.6%, and hepatocyte cell nuclei only with an accuracy of 86.2%. A public histological dataset with supplied ground-truth data is also used for evaluating the performance of the proposed approach (accuracy: 94.5%). Further validations are carried out with a publicly available dataset and ground-truth data from the Gland Segmentation in Colon Histology Images Challenge (GlaS) contest. The proposed method is useful for obtaining unsupervised robust initial segmentations that can be further integrated in image/data processing and management pipelines. The development of a fully automated system supporting a human expert provides tangible benefits in the context of clinical decision-making.
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62
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Zhou N, Yu X, Zhao T, Wen S, Wang F, Zhu W, Kurc T, Tannenbaum A, Saltz J, Gao Y. Evaluation of nucleus segmentation in digital pathology images through large scale image synthesis. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2017; 10140. [PMID: 30344361 DOI: 10.1117/12.2254220] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
Digital histopathology images with more than 1 Gigapixel are drawing more and more attention in clinical, biomedical research, and computer vision fields. Among the multiple observable features spanning multiple scales in the pathology images, the nuclear morphology is one of the central criteria for diagnosis and grading. As a result it is also the mostly studied target in image computing. Large amount of research papers have devoted to the problem of extracting nuclei from digital pathology images, which is the foundation of any further correlation study. However, the validation and evaluation of nucleus extraction have yet been formulated rigorously and systematically. Some researches report a human verified segmentation with thousands of nuclei, whereas a single whole slide image may contain up to million. The main obstacle lies in the difficulty of obtaining such a large number of validated nuclei, which is essentially an impossible task for pathologist. We propose a systematic validation and evaluation approach based on large scale image synthesis. This could facilitate a more quantitatively validated study for current and future histopathology image analysis field.
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Affiliation(s)
- Naiyun Zhou
- Department of Biomedical Engineering, Stony Brook University
| | - Xiaxia Yu
- Department of Biomedical Informatics, Stony Brook University
| | - Tianhao Zhao
- Department of Biomedical Informatics, Stony Brook University
| | - Si Wen
- Department of Applied Mathematics and Statistics, Stony Brook University
| | - Fusheng Wang
- Department of Biomedical Informatics, Stony Brook University.,Department of Computer Science, Stony Brook University
| | - Wei Zhu
- Department of Applied Mathematics and Statistics, Stony Brook University
| | - Tahsin Kurc
- Department of Biomedical Informatics, Stony Brook University.,Department of Computer Science, Stony Brook University
| | - Allen Tannenbaum
- Department of Computer Science, Stony Brook University.,Department of Applied Mathematics and Statistics, Stony Brook University
| | - Joel Saltz
- Department of Biomedical Informatics, Stony Brook University.,Department of Computer Science, Stony Brook University
| | - Yi Gao
- Department of Biomedical Informatics, Stony Brook University.,Department of Applied Mathematics and Statistics, Stony Brook University
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Wang MFZ, Hunter MV, Wang G, McFaul C, Yip CM, Fernandez-Gonzalez R. Automated cell tracking identifies mechanically oriented cell divisions during Drosophila axis elongation. Development 2017; 144:1350-1361. [PMID: 28213553 DOI: 10.1242/dev.141473] [Citation(s) in RCA: 30] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2016] [Accepted: 02/09/2017] [Indexed: 01/12/2023]
Abstract
Embryos extend their anterior-posterior (AP) axis in a conserved process known as axis elongation. Drosophila axis elongation occurs in an epithelial monolayer, the germband, and is driven by cell intercalation, cell shape changes, and oriented cell divisions at the posterior germband. Anterior germband cells also divide during axis elongation. We developed image analysis and pattern-recognition methods to track dividing cells from confocal microscopy movies in a generally applicable approach. Mesectoderm cells, forming the ventral midline, divided parallel to the AP axis, while lateral cells displayed a uniform distribution of division orientations. Mesectoderm cells did not intercalate and sustained increased AP strain before cell division. After division, mesectoderm cell density increased along the AP axis, thus relieving strain. We used laser ablation to isolate mesectoderm cells from the influence of other tissues. Uncoupling the mesectoderm from intercalating cells did not affect cell division orientation. Conversely, separating the mesectoderm from the anterior and posterior poles of the embryo resulted in uniformly oriented divisions. Our data suggest that mesectoderm cells align their division angle to reduce strain caused by mechanical forces along the AP axis of the embryo.
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Affiliation(s)
- Michael F Z Wang
- Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, Ontario, Canada M5S 3G9.,Ted Rogers Centre for Heart Research, University of Toronto, Toronto, Ontario, Canada M5G 1M1
| | - Miranda V Hunter
- Ted Rogers Centre for Heart Research, University of Toronto, Toronto, Ontario, Canada M5G 1M1.,Department of Cell and Systems Biology, University of Toronto, Toronto, Ontario, Canada M5S 3G5
| | - Gang Wang
- Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, Ontario, Canada M5S 3G9
| | - Christopher McFaul
- Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, Ontario, Canada M5S 3G9.,Ted Rogers Centre for Heart Research, University of Toronto, Toronto, Ontario, Canada M5G 1M1
| | - Christopher M Yip
- Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, Ontario, Canada M5S 3G9
| | - Rodrigo Fernandez-Gonzalez
- Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, Ontario, Canada M5S 3G9 .,Ted Rogers Centre for Heart Research, University of Toronto, Toronto, Ontario, Canada M5G 1M1.,Department of Cell and Systems Biology, University of Toronto, Toronto, Ontario, Canada M5S 3G5.,Developmental and Stem Cell Biology Program, The Hospital for Sick Children, Toronto, Ontario, Canada M5G 1X8
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64
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Nketia TA, Sailem H, Rohde G, Machiraju R, Rittscher J. Analysis of live cell images: Methods, tools and opportunities. Methods 2017; 115:65-79. [DOI: 10.1016/j.ymeth.2017.02.007] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2016] [Revised: 02/20/2017] [Accepted: 02/21/2017] [Indexed: 01/19/2023] Open
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65
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Grys BT, Lo DS, Sahin N, Kraus OZ, Morris Q, Boone C, Andrews BJ. Machine learning and computer vision approaches for phenotypic profiling. J Cell Biol 2016; 216:65-71. [PMID: 27940887 PMCID: PMC5223612 DOI: 10.1083/jcb.201610026] [Citation(s) in RCA: 88] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2016] [Revised: 11/18/2016] [Accepted: 11/21/2016] [Indexed: 11/27/2022] Open
Abstract
Grys et al. review computer vision and machine-learning methods that have been applied to phenotypic profiling of image-based data. Descriptions are provided for segmentation, feature extraction, selection, and dimensionality reduction, as well as clustering, outlier detection, and classification of data. With recent advances in high-throughput, automated microscopy, there has been an increased demand for effective computational strategies to analyze large-scale, image-based data. To this end, computer vision approaches have been applied to cell segmentation and feature extraction, whereas machine-learning approaches have been developed to aid in phenotypic classification and clustering of data acquired from biological images. Here, we provide an overview of the commonly used computer vision and machine-learning methods for generating and categorizing phenotypic profiles, highlighting the general biological utility of each approach.
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Affiliation(s)
- Ben T Grys
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5S 3E1, Canada.,Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada
| | - Dara S Lo
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5S 3E1, Canada.,Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada
| | - Nil Sahin
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5S 3E1, Canada.,Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada
| | - Oren Z Kraus
- Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada.,Department of Electrical and Computer Engineering, University of Toronto, Toronto, Ontario M5S 2E4, Canada
| | - Quaid Morris
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5S 3E1, Canada.,Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada.,Department of Electrical and Computer Engineering, University of Toronto, Toronto, Ontario M5S 2E4, Canada
| | - Charles Boone
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5S 3E1, Canada .,Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada
| | - Brenda J Andrews
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5S 3E1, Canada .,Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada
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66
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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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67
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Chitalia R, Mueller J, Fu HL, Whitley MJ, Kirsch DG, Brown JQ, Willett R, Ramanujam N. Algorithms for differentiating between images of heterogeneous tissue across fluorescence microscopes. BIOMEDICAL OPTICS EXPRESS 2016; 7:3412-3424. [PMID: 27699108 PMCID: PMC5030020 DOI: 10.1364/boe.7.003412] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/23/2016] [Revised: 07/25/2016] [Accepted: 08/04/2016] [Indexed: 06/06/2023]
Abstract
Fluorescence microscopy can be used to acquire real-time images of tissue morphology and with appropriate algorithms can rapidly quantify features associated with disease. The objective of this study was to assess the ability of various segmentation algorithms to isolate fluorescent positive features (FPFs) in heterogeneous images and identify an approach that can be used across multiple fluorescence microscopes with minimal tuning between systems. Specifically, we show a variety of image segmentation algorithms applied to images of stained tumor and muscle tissue acquired with 3 different fluorescence microscopes. Results indicate that a technique called maximally stable extremal regions followed by thresholding (MSER + Binary) yielded the greatest contrast in FPF density between tumor and muscle images across multiple microscopy systems.
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Affiliation(s)
- Rhea Chitalia
- Department of Biomedical Engineering, Duke University, Durham, North Carolina, USA;
| | - Jenna Mueller
- Department of Biomedical Engineering, Duke University, Durham, North Carolina, USA;
| | - Henry L Fu
- Department of Biomedical Engineering, Duke University, Durham, North Carolina, USA
| | - Melodi Javid Whitley
- Department of Pharmacology & Cancer Biology, Duke University School of Medicine, Durham, North Carolina, USA
| | - David G Kirsch
- Department of Pharmacology & Cancer Biology, Duke University School of Medicine, Durham, North Carolina, USA; Department of Radiation Oncology, Duke University School of Medicine, Durham, North Carolina, USA
| | - J Quincy Brown
- Department of Biomedical Engineering, Tulane University, New Orleans, Louisiana, USA
| | - Rebecca Willett
- Department of Electrical and Computer Engineering, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Nimmi Ramanujam
- Department of Biomedical Engineering, Duke University, Durham, North Carolina, USA
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68
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Kroll T, Schmidt D, Schwanitz G, Ahmad M, Hamann J, Schlosser C, Lin YC, Böhm KJ, Tuckermann J, Ploubidou A. High-Content Microscopy Analysis of Subcellular Structures: Assay Development and Application to Focal Adhesion Quantification. ACTA ACUST UNITED AC 2016; 77:12.43.1-12.43.44. [PMID: 27367288 DOI: 10.1002/cpcy.7] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
High-content analysis (HCA) converts raw light microscopy images to quantitative data through the automated extraction, multiparametric analysis, and classification of the relevant information content. Combined with automated high-throughput image acquisition, HCA applied to the screening of chemicals or RNAi-reagents is termed high-content screening (HCS). Its power in quantifying cell phenotypes makes HCA applicable also to routine microscopy. However, developing effective HCA and bioinformatic analysis pipelines for acquisition of biologically meaningful data in HCS is challenging. Here, the step-by-step development of an HCA assay protocol and an HCS bioinformatics analysis pipeline are described. The protocol's power is demonstrated by application to focal adhesion (FA) detection, quantitative analysis of multiple FA features, and functional annotation of signaling pathways regulating FA size, using primary data of a published RNAi screen. The assay and the underlying strategy are aimed at researchers performing microscopy-based quantitative analysis of subcellular features, on a small scale or in large HCS experiments. © 2016 by John Wiley & Sons, Inc.
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Affiliation(s)
- Torsten Kroll
- Leibniz Institute on Aging-Fritz Lipmann Institute, Jena, Germany.,These authors contributed equally to this work
| | - David Schmidt
- Leibniz Institute on Aging-Fritz Lipmann Institute, Jena, Germany.,Current address: Max Planck Institute for Molecular Biomedicine, Münster, Germany.,These authors contributed equally to this work
| | - Georg Schwanitz
- Leibniz Institute on Aging-Fritz Lipmann Institute, Jena, Germany
| | - Mubashir Ahmad
- Leibniz Institute on Aging-Fritz Lipmann Institute, Jena, Germany.,Institute for Comparative Molecular Endocrinology, University of Ulm, Ulm, Germany
| | - Jana Hamann
- Leibniz Institute on Aging-Fritz Lipmann Institute, Jena, Germany
| | | | - Yu-Chieh Lin
- Leibniz Institute on Aging-Fritz Lipmann Institute, Jena, Germany
| | - Konrad J Böhm
- Leibniz Institute on Aging-Fritz Lipmann Institute, Jena, Germany
| | - Jan Tuckermann
- Institute for Comparative Molecular Endocrinology, University of Ulm, Ulm, Germany
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69
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Talapatra SN, Mitra P, Swarnakar S. Morphology and Phenotype of Peripheral Erythrocytes of Fish: A Rapid Screening of Images by Using Software. INTERNATIONAL LETTERS OF NATURAL SCIENCES 2016. [DOI: 10.18052/www.scipress.com/ilns.54.27] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Many information of biological study as stained cells analysis under microscope cannot be obtained rich information like detail morphology, shape, size, proper intensity etc. but image analysis software can easily be detected all these parameters within short duration. The cells types can be yeast cells to mammalian cells. An attempt has been made to detect cellular abnormalities from an image of metronidazole (MTZ) treated compared to control images of peripheral erythrocytes of fish by using non-commercial, open-source, CellProfiler (CP) image analysis software (Ver. 2.1.0). The comparative results were obtained after analysis the software. In conclusion, this image based screening of Giemsa stained fish erythrocytes can be a suitable tool in biological research for primary toxicity prediction at DNA level alongwith cellular phenotypes. Moreover, still suggestions are needed in relation to accuracy of present analysis for Giemsa stained fish erythrocytes because previous works have been carried out images of cells with fluorescence dye.
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70
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Al-Mamun M, Ravenhill L, Srisukkham W, Hossain A, Fall C, Ellis V, Bass R. Effects of Noninhibitory Serpin Maspin on the Actin Cytoskeleton: A Quantitative Image Modeling Approach. MICROSCOPY AND MICROANALYSIS : THE OFFICIAL JOURNAL OF MICROSCOPY SOCIETY OF AMERICA, MICROBEAM ANALYSIS SOCIETY, MICROSCOPICAL SOCIETY OF CANADA 2016; 22:394-409. [PMID: 26906065 DOI: 10.1017/s1431927616000520] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
Recent developments in quantitative image analysis allow us to interrogate confocal microscopy images to answer biological questions. Clumped and layered cell nuclei and cytoplasm in confocal images challenges the ability to identify subcellular compartments. To date, there is no perfect image analysis method to identify cytoskeletal changes in confocal images. Here, we present a multidisciplinary study where an image analysis model was developed to allow quantitative measurements of changes in the cytoskeleton of cells with different maspin exposure. Maspin, a noninhibitory serpin influences cell migration, adhesion, invasion, proliferation, and apoptosis in ways that are consistent with its identification as a tumor metastasis suppressor. Using different cell types, we tested the hypothesis that reduction in cell migration by maspin would be reflected in the architecture of the actin cytoskeleton. A hybrid marker-controlled watershed segmentation technique was used to segment the nuclei, cytoplasm, and ruffling regions before measuring cytoskeletal changes. This was informed by immunohistochemical staining of cells transfected stably or transiently with maspin proteins, or with added bioactive peptides or protein. Image analysis results showed that the effects of maspin were mirrored by effects on cell architecture, in a way that could be described quantitatively.
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Affiliation(s)
- Mohammed Al-Mamun
- 1Computational Intelligence Group, Faculty of Engineering and Environment,Northumbria University,Newcastle upon Tyne NE1 8ST,UK
| | - Lorna Ravenhill
- 3School of Biological Sciences,University of East Anglia,Norwich,Norfolk, NR4 7TJ,UK
| | - Worawut Srisukkham
- 1Computational Intelligence Group, Faculty of Engineering and Environment,Northumbria University,Newcastle upon Tyne NE1 8ST,UK
| | - Alamgir Hossain
- 1Computational Intelligence Group, Faculty of Engineering and Environment,Northumbria University,Newcastle upon Tyne NE1 8ST,UK
| | - Charles Fall
- 1Computational Intelligence Group, Faculty of Engineering and Environment,Northumbria University,Newcastle upon Tyne NE1 8ST,UK
| | - Vincent Ellis
- 3School of Biological Sciences,University of East Anglia,Norwich,Norfolk, NR4 7TJ,UK
| | - Rosemary Bass
- 5Department of Applied Sciences, Faculty of Health and Life Sciences,Northumbria University,Newcastle upon Tyne NE1 8ST,UK
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Nuquantus: Machine learning software for the characterization and quantification of cell nuclei in complex immunofluorescent tissue images. Sci Rep 2016; 6:23431. [PMID: 27005843 PMCID: PMC4804284 DOI: 10.1038/srep23431] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2015] [Accepted: 03/04/2016] [Indexed: 01/27/2023] Open
Abstract
Determination of fundamental mechanisms of disease often hinges on histopathology visualization and quantitative image analysis. Currently, the analysis of multi-channel fluorescence tissue images is primarily achieved by manual measurements of tissue cellular content and sub-cellular compartments. Since the current manual methodology for image analysis is a tedious and subjective approach, there is clearly a need for an automated analytical technique to process large-scale image datasets. Here, we introduce Nuquantus (Nuclei quantification utility software) - a novel machine learning-based analytical method, which identifies, quantifies and classifies nuclei based on cells of interest in composite fluorescent tissue images, in which cell borders are not visible. Nuquantus is an adaptive framework that learns the morphological attributes of intact tissue in the presence of anatomical variability and pathological processes. Nuquantus allowed us to robustly perform quantitative image analysis on remodeling cardiac tissue after myocardial infarction. Nuquantus reliably classifies cardiomyocyte versus non-cardiomyocyte nuclei and detects cell proliferation, as well as cell death in different cell classes. Broadly, Nuquantus provides innovative computerized methodology to analyze complex tissue images that significantly facilitates image analysis and minimizes human bias.
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72
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Koyuncu CF, Akhan E, Ersahin T, Cetin-Atalay R, Gunduz-Demir C. Iterative h-minima-based marker-controlled watershed for cell nucleus segmentation. Cytometry A 2016; 89:338-49. [PMID: 26945784 DOI: 10.1002/cyto.a.22824] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2015] [Revised: 10/26/2015] [Accepted: 01/11/2016] [Indexed: 02/05/2023]
Abstract
Automated microscopy imaging systems facilitate high-throughput screening in molecular cellular biology research. The first step of these systems is cell nucleus segmentation, which has a great impact on the success of the overall system. The marker-controlled watershed is a technique commonly used by the previous studies for nucleus segmentation. These studies define their markers finding regional minima on the intensity/gradient and/or distance transform maps. They typically use the h-minima transform beforehand to suppress noise on these maps. The selection of the h value is critical; unnecessarily small values do not sufficiently suppress the noise, resulting in false and oversegmented markers, and unnecessarily large ones suppress too many pixels, causing missing and undersegmented markers. Because cell nuclei show different characteristics within an image, the same h value may not work to define correct markers for all the nuclei. To address this issue, in this work, we propose a new watershed algorithm that iteratively identifies its markers, considering a set of different h values. In each iteration, the proposed algorithm defines a set of candidates using a particular h value and selects the markers from those candidates provided that they fulfill the size requirement. Working with widefield fluorescence microscopy images, our experiments reveal that the use of multiple h values in our iterative algorithm leads to better segmentation results, compared to its counterparts. © 2016 International Society for Advancement of Cytometry.
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Affiliation(s)
| | - Ece Akhan
- Molecular Biology and Genetics Department, Bilkent University, Ankara, TR-06800, Turkey
| | - Tulin Ersahin
- Medical Informatics Department, Graduate School of Informatics, Middle East Technical University, Ankara, TR-06800, Turkey
| | - Rengul Cetin-Atalay
- Medical Informatics Department, Graduate School of Informatics, Middle East Technical University, Ankara, TR-06800, Turkey
| | - Cigdem Gunduz-Demir
- Computer Engineering Department, Bilkent University, Ankara, TR-06800, Turkey.,Neuroscience Graduate Program, Bilkent University, Ankara, TR-06800, Turkey
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73
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Taking Aim at Moving Targets in Computational Cell Migration. Trends Cell Biol 2016; 26:88-110. [DOI: 10.1016/j.tcb.2015.09.003] [Citation(s) in RCA: 81] [Impact Index Per Article: 10.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2015] [Revised: 08/31/2015] [Accepted: 09/03/2015] [Indexed: 01/07/2023]
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75
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Xing F, Yang L. Robust Nucleus/Cell Detection and Segmentation in Digital Pathology and Microscopy Images: A Comprehensive Review. IEEE Rev Biomed Eng 2016; 9:234-63. [PMID: 26742143 PMCID: PMC5233461 DOI: 10.1109/rbme.2016.2515127] [Citation(s) in RCA: 213] [Impact Index Per Article: 26.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
Digital pathology and microscopy image analysis is widely used for comprehensive studies of cell morphology or tissue structure. Manual assessment is labor intensive and prone to interobserver variations. Computer-aided methods, which can significantly improve the objectivity and reproducibility, have attracted a great deal of interest in recent literature. Among the pipeline of building a computer-aided diagnosis system, nucleus or cell detection and segmentation play a very important role to describe the molecular morphological information. In the past few decades, many efforts have been devoted to automated nucleus/cell detection and segmentation. In this review, we provide a comprehensive summary of the recent state-of-the-art nucleus/cell segmentation approaches on different types of microscopy images including bright-field, phase-contrast, differential interference contrast, fluorescence, and electron microscopies. In addition, we discuss the challenges for the current methods and the potential future work of nucleus/cell detection and segmentation.
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76
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Scherzinger A, Kleene F, Dierkes C, Kiefer F, Hinrichs KH, Jiang X. Automated Segmentation of Immunostained Cell Nuclei in 3D Ultramicroscopy Images. LECTURE NOTES IN COMPUTER SCIENCE 2016. [DOI: 10.1007/978-3-319-45886-1_9] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
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77
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Automatic segmentation of cell nuclei using Krill Herd optimization based multi-thresholding and Localized Active Contour Model. Biocybern Biomed Eng 2016. [DOI: 10.1016/j.bbe.2016.06.005] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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78
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John J, Nair MS, Anil Kumar P, Wilscy M. A novel approach for detection and delineation of cell nuclei using feature similarity index measure. Biocybern Biomed Eng 2016. [DOI: 10.1016/j.bbe.2015.11.002] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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79
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Bajcsy P, Cardone A, Chalfoun J, Halter M, Juba D, Kociolek M, Majurski M, Peskin A, Simon C, Simon M, Vandecreme A, Brady M. Survey statistics of automated segmentations applied to optical imaging of mammalian cells. BMC Bioinformatics 2015; 16:330. [PMID: 26472075 PMCID: PMC4608288 DOI: 10.1186/s12859-015-0762-2] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2015] [Accepted: 10/07/2015] [Indexed: 12/30/2022] Open
Abstract
BACKGROUND The goal of this survey paper is to overview cellular measurements using optical microscopy imaging followed by automated image segmentation. The cellular measurements of primary interest are taken from mammalian cells and their components. They are denoted as two- or three-dimensional (2D or 3D) image objects of biological interest. In our applications, such cellular measurements are important for understanding cell phenomena, such as cell counts, cell-scaffold interactions, cell colony growth rates, or cell pluripotency stability, as well as for establishing quality metrics for stem cell therapies. In this context, this survey paper is focused on automated segmentation as a software-based measurement leading to quantitative cellular measurements. METHODS We define the scope of this survey and a classification schema first. Next, all found and manually filteredpublications are classified according to the main categories: (1) objects of interests (or objects to be segmented), (2) imaging modalities, (3) digital data axes, (4) segmentation algorithms, (5) segmentation evaluations, (6) computational hardware platforms used for segmentation acceleration, and (7) object (cellular) measurements. Finally, all classified papers are converted programmatically into a set of hyperlinked web pages with occurrence and co-occurrence statistics of assigned categories. RESULTS The survey paper presents to a reader: (a) the state-of-the-art overview of published papers about automated segmentation applied to optical microscopy imaging of mammalian cells, (b) a classification of segmentation aspects in the context of cell optical imaging, (c) histogram and co-occurrence summary statistics about cellular measurements, segmentations, segmented objects, segmentation evaluations, and the use of computational platforms for accelerating segmentation execution, and (d) open research problems to pursue. CONCLUSIONS The novel contributions of this survey paper are: (1) a new type of classification of cellular measurements and automated segmentation, (2) statistics about the published literature, and (3) a web hyperlinked interface to classification statistics of the surveyed papers at https://isg.nist.gov/deepzoomweb/resources/survey/index.html.
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Affiliation(s)
- Peter Bajcsy
- Information Technology Laboratory, National Institute of Standards and Technology, Gaithersburg, USA.
| | - Antonio Cardone
- Information Technology Laboratory, National Institute of Standards and Technology, Gaithersburg, USA.
| | - Joe Chalfoun
- Information Technology Laboratory, National Institute of Standards and Technology, Gaithersburg, USA.
| | - Michael Halter
- Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, USA.
| | - Derek Juba
- Information Technology Laboratory, National Institute of Standards and Technology, Gaithersburg, USA.
| | | | - Michael Majurski
- Information Technology Laboratory, National Institute of Standards and Technology, Gaithersburg, USA.
| | - Adele Peskin
- Information Technology Laboratory, National Institute of Standards and Technology, Gaithersburg, USA.
| | - Carl Simon
- Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, USA.
| | - Mylene Simon
- Information Technology Laboratory, National Institute of Standards and Technology, Gaithersburg, USA.
| | - Antoine Vandecreme
- Information Technology Laboratory, National Institute of Standards and Technology, Gaithersburg, USA.
| | - Mary Brady
- Information Technology Laboratory, National Institute of Standards and Technology, Gaithersburg, USA.
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Saha PK, Strand R, Borgefors G. Digital Topology and Geometry in Medical Imaging: A Survey. IEEE TRANSACTIONS ON MEDICAL IMAGING 2015; 34:1940-1964. [PMID: 25879908 DOI: 10.1109/tmi.2015.2417112] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
Digital topology and geometry refers to the use of topologic and geometric properties and features for images defined in digital grids. Such methods have been widely used in many medical imaging applications, including image segmentation, visualization, manipulation, interpolation, registration, surface-tracking, object representation, correction, quantitative morphometry etc. Digital topology and geometry play important roles in medical imaging research by enriching the scope of target outcomes and by adding strong theoretical foundations with enhanced stability, fidelity, and efficiency. This paper presents a comprehensive yet compact survey on results, principles, and insights of methods related to digital topology and geometry with strong emphasis on understanding their roles in various medical imaging applications. Specifically, this paper reviews methods related to distance analysis and path propagation, connectivity, surface-tracking, image segmentation, boundary and centerline detection, topology preservation and local topological properties, skeletonization, and object representation, correction, and quantitative morphometry. A common thread among the topics reviewed in this paper is that their theory and algorithms use the principle of digital path connectivity, path propagation, and neighborhood analysis.
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81
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Soliman K. CellProfiler: Novel Automated Image Segmentation Procedure for Super-Resolution Microscopy. Biol Proced Online 2015; 17:11. [PMID: 26251640 PMCID: PMC4527132 DOI: 10.1186/s12575-015-0023-9] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2015] [Accepted: 07/28/2015] [Indexed: 02/01/2023] Open
Abstract
BACKGROUND Super resolution (SR) microscopy enabled cell biologists to visualize subcellular details up to 20 nm in resolution. This breakthrough in spatial resolution made image analysis a challenging procedure. Direct and automated segmentation of SR images remains largely unsolved, especially when it comes to providing meaningful biological interpretations. RESULTS Here, we introduce a novel automated imaging analysis routine, based on Gaussian, followed by a segmentation procedure using CellProfiler software (www.cellprofiler.org). We tested this method and succeeded to segment individual nuclear pore complexes stained with gp210 and pan-FG proteins and captured by two-color STED microscopy. Test results confirmed accuracy and robustness of the method even in noisy STED images of gp210. CONCLUSIONS Our pipeline and novel segmentation procedure may benefit end-users of SR microscopy to analyze their images and extract biologically significant quantitative data about them in user-friendly and fully-automated settings.
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Affiliation(s)
- Kareem Soliman
- Department of Pediatrics and Adolescent Medicine, University Medical Center, University of Göttingen, Robert-Koch Str.40 1.D3.644 Histology Lab, Göttingen, 37075 Germany
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82
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Farabella I, Vasishtan D, Joseph AP, Pandurangan AP, Sahota H, Topf M. TEMPy: a Python library for assessment of three-dimensional electron microscopy density fits. J Appl Crystallogr 2015; 48:1314-1323. [PMID: 26306092 PMCID: PMC4520291 DOI: 10.1107/s1600576715010092] [Citation(s) in RCA: 61] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2014] [Accepted: 05/24/2015] [Indexed: 12/21/2022] Open
Abstract
TEMPy is an object-oriented Python library that provides the means to validate density fits in electron microscopy reconstructions. This article highlights several features of particular interest for this purpose and includes some customized examples. Three-dimensional electron microscopy is currently one of the most promising techniques used to study macromolecular assemblies. Rigid and flexible fitting of atomic models into density maps is often essential to gain further insights into the assemblies they represent. Currently, tools that facilitate the assessment of fitted atomic models and maps are needed. TEMPy (template and electron microscopy comparison using Python) is a toolkit designed for this purpose. The library includes a set of methods to assess density fits in intermediate-to-low resolution maps, both globally and locally. It also provides procedures for single-fit assessment, ensemble generation of fits, clustering, and multiple and consensus scoring, as well as plots and output files for visualization purposes to help the user in analysing rigid and flexible fits. The modular nature of TEMPy helps the integration of scoring and assessment of fits into large pipelines, making it a tool suitable for both novice and expert structural biologists.
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Affiliation(s)
- Irene Farabella
- Institute of Structural and Molecular Biology, Department of Biological Sciences, Birkbeck, University of London , Malet street, London WC1E 7HX, UK
| | - Daven Vasishtan
- Oxford Particle Imaging Centre, Division of Structural Biology, Wellcome Trust Centre for Human Genetics, University of Oxford , Oxford OX3 7BN, UK
| | - Agnel Praveen Joseph
- Scientific Computing Department, Science and Technology Facilities Council, Research Complex at Harwell , Didcot, Oxon OX11 0QX, UK
| | - Arun Prasad Pandurangan
- Institute of Structural and Molecular Biology, Department of Biological Sciences, Birkbeck, University of London , Malet street, London WC1E 7HX, UK
| | - Harpal Sahota
- Institute of Structural and Molecular Biology, Department of Biological Sciences, Birkbeck, University of London , Malet street, London WC1E 7HX, UK
| | - Maya Topf
- Institute of Structural and Molecular Biology, Department of Biological Sciences, Birkbeck, University of London , Malet street, London WC1E 7HX, UK
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83
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Chittajallu DR, Florian S, Kohler RH, Iwamoto Y, Orth JD, Weissleder R, Danuser G, Mitchison TJ. In vivo cell-cycle profiling in xenograft tumors by quantitative intravital microscopy. Nat Methods 2015; 12:577-85. [PMID: 25867850 PMCID: PMC4579269 DOI: 10.1038/nmeth.3363] [Citation(s) in RCA: 58] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2014] [Accepted: 02/18/2015] [Indexed: 10/31/2022]
Abstract
Quantification of cell-cycle state at a single-cell level is essential to understand fundamental three-dimensional (3D) biological processes such as tissue development and cancer. Analysis of 3D in vivo images, however, is very challenging. Today's best practice, manual annotation of select image events, generates arbitrarily sampled data distributions, which are unsuitable for reliable mechanistic inferences. Here, we present an integrated workflow for quantitative in vivo cell-cycle profiling. It combines image analysis and machine learning methods for automated 3D segmentation and cell-cycle state identification of individual cell-nuclei with widely varying morphologies embedded in complex tumor environments. We applied our workflow to quantify cell-cycle effects of three antimitotic cancer drugs over 8 d in HT-1080 fibrosarcoma xenografts in living mice using a data set of 38,000 cells and compared the induced phenotypes. In contrast to results with 2D culture, observed mitotic arrest was relatively low, suggesting involvement of additional mechanisms in their antitumor effect in vivo.
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Affiliation(s)
| | - Stefan Florian
- Department of Systems Biology, Harvard Medical School, Boston, Massachusetts, USA
| | - Rainer H Kohler
- Center for Systems Biology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Yoshiko Iwamoto
- Center for Systems Biology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - James D Orth
- Molecular, Cellular and Developmental Biology, University of Colorado Boulder, Boulder, Colorado, USA
| | - Ralph Weissleder
- 1] Department of Systems Biology, Harvard Medical School, Boston, Massachusetts, USA. [2] Center for Systems Biology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Gaudenz Danuser
- Department of Cell Biology, Harvard Medical School, Boston, Massachusetts, USA
| | - Timothy J Mitchison
- Department of Systems Biology, Harvard Medical School, Boston, Massachusetts, USA
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84
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Sanders J, Singh A, Sterne G, Ye B, Zhou J. Learning-guided automatic three dimensional synapse quantification for drosophila neurons. BMC Bioinformatics 2015; 16:177. [PMID: 26017624 PMCID: PMC4445279 DOI: 10.1186/s12859-015-0616-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2015] [Accepted: 05/16/2015] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND The subcellular distribution of synapses is fundamentally important for the assembly, function, and plasticity of the nervous system. Automated and effective quantification tools are a prerequisite to large-scale studies of the molecular mechanisms of subcellular synapse distribution. Common practices for synapse quantification in neuroscience labs remain largely manual or semi-manual. This is mainly due to computational challenges in automatic quantification of synapses, including large volume, high dimensions and staining artifacts. In the case of confocal imaging, optical limit and xy-z resolution disparity also require special considerations to achieve the necessary robustness. RESULTS A novel algorithm is presented in the paper for learning-guided automatic recognition and quantification of synaptic markers in 3D confocal images. The method developed a discriminative model based on 3D feature descriptors that detected the centers of synaptic markers. It made use of adaptive thresholding and multi-channel co-localization to improve the robustness. The detected markers then guided the splitting of synapse clumps, which further improved the precision and recall of the detected synapses. Algorithms were tested on lobula plate tangential cells (LPTCs) in the brain of Drosophila melanogaster, for GABAergic synaptic markers on axon terminals as well as dendrites. CONCLUSIONS The presented method was able to overcome the staining artifacts and the fuzzy boundaries of synapse clumps in 3D confocal image, and automatically quantify synaptic markers in a complex neuron such as LPTC. Comparison with some existing tools used in automatic 3D synapse quantification also proved the effectiveness of the proposed method.
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Affiliation(s)
- Jonathan Sanders
- Department of Computer Science, Northern Illinois University, DeKalb, IL, 60115, USA
| | - Anil Singh
- Department of Computer Science, Northern Illinois University, DeKalb, IL, 60115, USA
| | - Gabriella Sterne
- Life Sciences Institute and Department of Cell and Developmental Biology University of Michigan, Ann Arbor, MI, 48109, USA
| | - Bing Ye
- Life Sciences Institute and Department of Cell and Developmental Biology University of Michigan, Ann Arbor, MI, 48109, USA
| | - Jie Zhou
- Department of Computer Science, Northern Illinois University, DeKalb, IL, 60115, USA.
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85
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Gertych A, Ma Z, Tajbakhsh J, Velásquez-Vacca A, Knudsen BS. Rapid 3-D delineation of cell nuclei for high-content screening platforms. Comput Biol Med 2015; 69:328-38. [PMID: 25982066 DOI: 10.1016/j.compbiomed.2015.04.025] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2014] [Revised: 04/08/2015] [Accepted: 04/16/2015] [Indexed: 12/17/2022]
Abstract
High-resolution three-dimensional (3-D) microscopy combined with multiplexing of fluorescent labels allows high-content analysis of large numbers of cell nuclei. The full automation of 3-D screening platforms necessitates image processing algorithms that can accurately and robustly delineate nuclei in images with little to no human intervention. Imaging-based high-content screening was originally developed as a powerful tool for drug discovery. However, cell confluency, complexity of nuclear staining as well as poor contrast between nuclei and background result in slow and unreliable 3-D image processing and therefore negatively affect the performance of studying a drug response. Here, we propose a new method, 3D-RSD, to delineate nuclei by means of 3-D radial symmetries and test it on high-resolution image data of human cancer cells treated by drugs. The nuclei detection performance was evaluated by means of manually generated ground truth from 2351 nuclei (27 confocal stacks). When compared to three other nuclei segmentation methods, 3D-RSD possessed a better true positive rate of 83.3% and F-score of 0.895±0.045 (p-value=0.047). Altogether, 3D-RSD is a method with a very good overall segmentation performance. Furthermore, implementation of radial symmetries offers good processing speed, and makes 3D-RSD less sensitive to staining patterns. In particular, the 3D-RSD method performs well in cell lines, which are often used in imaging-based HCS platforms and are afflicted by nuclear crowding and overlaps that hinder feature extraction.
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Affiliation(s)
- Arkadiusz Gertych
- Department of Surgery, Cedars-Sinai Medical Center, Los Angeles, CA, USA; Department of Pathology and Laboratory Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
| | - Zhaoxuan Ma
- Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Jian Tajbakhsh
- Department of Surgery, Cedars-Sinai Medical Center, Los Angeles, CA, USA; Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | | | - Beatrice S Knudsen
- Department of Pathology and Laboratory Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA; Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA; Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
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86
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Guo Y, Xu X, Wang Y, Yang Z, Wang Y, Xia S. A computational approach to detect and segment cytoplasm in muscle fiber images. Microsc Res Tech 2015; 78:508-18. [PMID: 25900156 DOI: 10.1002/jemt.22502] [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: 01/28/2015] [Revised: 03/11/2015] [Accepted: 03/17/2015] [Indexed: 11/09/2022]
Abstract
We developed a computational approach to detect and segment cytoplasm in microscopic images of skeletal muscle fibers. The computational approach provides computer-aided analysis of cytoplasm objects in muscle fiber images to facilitate biomedical research. Cytoplasm in muscle fibers plays an important role in maintaining the functioning and health of muscular tissues. Therefore, cytoplasm is often used as a marker in broad applications of musculoskeletal research, including our search on treatment of muscular disorders such as Duchenne muscular dystrophy, a disease that has no available treatment. However, it is often challenging to analyze cytoplasm and quantify it given the large number of images typically generated in experiments and the large number of muscle fibers contained in each image. Manual analysis is not only time consuming but also prone to human errors. In this work we developed a computational approach to detect and segment the longitudinal sections of cytoplasm based on a modified graph cuts technique and iterative splitting method to extract cytoplasm objects from the background. First, cytoplasm objects are extracted from the background using the modified graph cuts technique which is designed to optimize an energy function. Second, an iterative splitting method is designed to separate the touching or adjacent cytoplasm objects from the results of graph cuts. We tested the computational approach on real data from in vitro experiments and found that it can achieve satisfactory performance in terms of precision and recall rates.
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Affiliation(s)
- Yanen Guo
- Key Laboratory of Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou, China.,Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, Zhejiang University, Hangzhou, China
| | - Xiaoyin Xu
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Yuanyuan Wang
- Key Laboratory of Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou, China.,Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, Zhejiang University, Hangzhou, China
| | - Zhong Yang
- Department of Clinical Hematology, Southwestern Hospital, Third Military Medical University, Chongqing, China
| | - Yaming Wang
- Department of Anesthesia, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Shunren Xia
- Key Laboratory of Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou, China.,Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, Zhejiang University, Hangzhou, China
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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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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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89
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Kawase T, Sugano SS, Shimada T, Hara-Nishimura I. A direction-selective local-thresholding method, DSLT, in combination with a dye-based method for automated three-dimensional segmentation of cells and airspaces in developing leaves. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2015; 81:357-66. [PMID: 25440085 DOI: 10.1111/tpj.12738] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/25/2014] [Revised: 10/24/2014] [Accepted: 11/17/2014] [Indexed: 06/04/2023]
Abstract
Quantifying the anatomical data acquired from three-dimensional (3D) images has become increasingly important in recent years. Visualization and image segmentation are essential for acquiring accurate and detailed anatomical data from images; however, plant tissues such as leaves are difficult to image by confocal or multi-photon laser scanning microscopy because their airspaces generate optical aberrations. To overcome this problem, we established a staining method based on Nile Red in silicone-oil solution. Our staining method enables color differentiation between lipid bilayer membranes and airspaces, while minimizing any damage to leaf development. By repeated applications of our staining method we performed time-lapse imaging of a leaf over 5 days. To counteract the drastic decline in signal-to-noise ratio at greater tissue depths, we also developed a local thresholding method (direction-selective local thresholding, DSLT) and an automated iterative segmentation algorithm. The segmentation algorithm uses the DSLT to extract the anatomical structures. Using the proposed methods, we accurately segmented 3D images of intact leaves to single-cell resolution, and measured the airspace volumes in intact leaves.
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Affiliation(s)
- Takashi Kawase
- Department of Botany, Graduate School of Science, Kyoto University, Kyoto, 606-8502, Japan
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90
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Irshad H, Veillard A, Roux L, Racoceanu D. Methods for nuclei detection, segmentation, and classification in digital histopathology: a review-current status and future potential. IEEE Rev Biomed Eng 2014; 7:97-114. [PMID: 24802905 DOI: 10.1109/rbme.2013.2295804] [Citation(s) in RCA: 280] [Impact Index Per Article: 28.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Digital pathology represents one of the major evolutions in modern medicine. Pathological examinations constitute the gold standard in many medical protocols, and also play a critical and legal role in the diagnosis process. In the conventional cancer diagnosis, pathologists analyze biopsies to make diagnostic and prognostic assessments, mainly based on the cell morphology and architecture distribution. Recently, computerized methods have been rapidly evolving in the area of digital pathology, with growing applications related to nuclei detection, segmentation, and classification. In cancer research, these approaches have played, and will continue to play a key (often bottleneck) role in minimizing human intervention, consolidating pertinent second opinions, and providing traceable clinical information. Pathological studies have been conducted for numerous cancer detection and grading applications, including brain, breast, cervix, lung, and prostate cancer grading. Our study presents, discusses, and extracts the major trends from an exhaustive overview of various nuclei detection, segmentation, feature computation, and classification techniques used in histopathology imagery, specifically in hematoxylin-eosin and immunohistochemical staining protocols. This study also enables us to measure the challenges that remain, in order to reach robust analysis of whole slide images, essential high content imaging with diagnostic biomarkers and prognosis support in digital pathology.
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91
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Emerson T, Kirby M, Bethel K, Kolatkar A, Luttgen M, O'Hara S, Newton P, Kuhn P. Fourier-ring descriptor to characterize rare circulating cells from images generated using immunofluorescence microscopy. Comput Med Imaging Graph 2014; 40:70-87. [PMID: 25456146 DOI: 10.1016/j.compmedimag.2014.10.003] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2014] [Revised: 05/22/2014] [Accepted: 10/06/2014] [Indexed: 12/26/2022]
Abstract
We address the problem of subclassification of rare circulating cells using data driven feature selection from images of candidate circulating tumor cells from patients diagnosed with breast, prostate, or lung cancer. We determine a set of low level features which can differentiate among candidate cell types. We have implemented an image representation based on concentric Fourier rings (FRDs) which allow us to exploit size variations and morphological differences among cells while being rotationally invariant. We discuss potential clinical use in the context of treatment monitoring for cancer patients with metastatic disease.
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Affiliation(s)
- Tegan Emerson
- Department of Mathematics, Colorado State University, 841 Oval Drive, Fort Collins, CO 80523, United States.
| | - Michael Kirby
- Department of Mathematics, Colorado State University, 841 Oval Drive, Fort Collins, CO 80523, United States.
| | - Kelly Bethel
- The Scripps Clinic, Department of Pathology, 10666 N Torrey Pines Road, La Jolla, CA 92037, United States.
| | - Anand Kolatkar
- The Scripps Research Institute, The Kuhn Lab, 10550 N Torrey Pines Road, La Jolla, CA 92037, United States.
| | - Madelyn Luttgen
- The Scripps Research Institute, The Kuhn Lab, 10550 N Torrey Pines Road, La Jolla, CA 92037, United States.
| | - Stephen O'Hara
- DigitalGlobe, Image Mining Group, 1601 Dry Creek Drive, Longmont, CO 80503, United States.
| | - Paul Newton
- Department of Aerospace and Mechanical Engineering, University of Southern California Viterbi School of Engineering, Los Angeles, CA 90089, United States.
| | - Peter Kuhn
- The Kuhn Lab, Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089, United States.
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92
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El-Labban A, Zisserman A, Toyoda Y, Bird A, Hyman A. Temporal models for mitotic phase labelling. Med Image Anal 2014; 18:977-88. [DOI: 10.1016/j.media.2014.05.003] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2013] [Revised: 05/03/2014] [Accepted: 05/07/2014] [Indexed: 11/28/2022]
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93
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He Y, Meng Y, Gong H, Chen S, Zhang B, Ding W, Luo Q, Li A. An automated three-dimensional detection and segmentation method for touching cells by integrating concave points clustering and random walker algorithm. PLoS One 2014; 9:e104437. [PMID: 25111442 PMCID: PMC4128780 DOI: 10.1371/journal.pone.0104437] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2013] [Accepted: 07/14/2014] [Indexed: 11/25/2022] Open
Abstract
Characterizing cytoarchitecture is crucial for understanding brain functions and neural diseases. In neuroanatomy, it is an important task to accurately extract cell populations' centroids and contours. Recent advances have permitted imaging at single cell resolution for an entire mouse brain using the Nissl staining method. However, it is difficult to precisely segment numerous cells, especially those cells touching each other. As presented herein, we have developed an automated three-dimensional detection and segmentation method applied to the Nissl staining data, with the following two key steps: 1) concave points clustering to determine the seed points of touching cells; and 2) random walker segmentation to obtain cell contours. Also, we have evaluated the performance of our proposed method with several mouse brain datasets, which were captured with the micro-optical sectioning tomography imaging system, and the datasets include closely touching cells. Comparing with traditional detection and segmentation methods, our approach shows promising detection accuracy and high robustness.
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Affiliation(s)
- Yong He
- Britton Chance Center for Biomedical Photonics, Huazhong University of Science and Technology-Wuhan National Laboratory for Optoelectronics, Wuhan, Hubei, China
- MoE Key Laboratory for Biomedical Photonics, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Yunlong Meng
- Britton Chance Center for Biomedical Photonics, Huazhong University of Science and Technology-Wuhan National Laboratory for Optoelectronics, Wuhan, Hubei, China
- MoE Key Laboratory for Biomedical Photonics, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Hui Gong
- Britton Chance Center for Biomedical Photonics, Huazhong University of Science and Technology-Wuhan National Laboratory for Optoelectronics, Wuhan, Hubei, China
- MoE Key Laboratory for Biomedical Photonics, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Shangbin Chen
- Britton Chance Center for Biomedical Photonics, Huazhong University of Science and Technology-Wuhan National Laboratory for Optoelectronics, Wuhan, Hubei, China
- MoE Key Laboratory for Biomedical Photonics, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Bin Zhang
- Britton Chance Center for Biomedical Photonics, Huazhong University of Science and Technology-Wuhan National Laboratory for Optoelectronics, Wuhan, Hubei, China
- MoE Key Laboratory for Biomedical Photonics, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Wenxiang Ding
- Britton Chance Center for Biomedical Photonics, Huazhong University of Science and Technology-Wuhan National Laboratory for Optoelectronics, Wuhan, Hubei, China
- MoE Key Laboratory for Biomedical Photonics, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Qingming Luo
- Britton Chance Center for Biomedical Photonics, Huazhong University of Science and Technology-Wuhan National Laboratory for Optoelectronics, Wuhan, Hubei, China
- MoE Key Laboratory for Biomedical Photonics, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Anan Li
- Britton Chance Center for Biomedical Photonics, Huazhong University of Science and Technology-Wuhan National Laboratory for Optoelectronics, Wuhan, Hubei, China
- MoE Key Laboratory for Biomedical Photonics, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan, Hubei, China
- * E-mail:
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94
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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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95
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Weng MK, Natarajan K, Scholz D, Ivanova VN, Sachinidis A, Hengstler JG, Waldmann T, Leist M. Lineage-specific regulation of epigenetic modifier genes in human liver and brain. PLoS One 2014; 9:e102035. [PMID: 25054330 PMCID: PMC4108363 DOI: 10.1371/journal.pone.0102035] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2014] [Accepted: 06/13/2014] [Indexed: 12/14/2022] Open
Abstract
Despite an abundance of studies on chromatin states and dynamics, there is an astonishing dearth of information on the expression of genes responsible for regulating histone and DNA modifications. We used here a set of 156 defined epigenetic modifier genes (EMG) and profiled their expression pattern in cells of different lineages. As reference value, expression data from human embryonic stem cells (hESC) were used. Hepatocyte-like cells were generated from hESC, and their EMG expression was compared to primary human liver cells. In parallel, we generated postmitotic human neurons (Lu d6), and compared their relative EMG expression to human cortex (Ctx). Clustering analysis of all cell types showed that neuronal lineage samples grouped together (94 similarly regulated EMG), as did liver cells (61 similarly-regulated), while the two lineages were clearly distinct. The general classification was followed by detailed comparison of the major EMG groups; genes that were higher expressed in differentiated cells than in hESC included the acetyltransferase KAT2B and the methyltransferase SETD7. Neuro-specific EMGs were the histone deacetylases HDAC5 and HDAC7, and the arginine-methyltransferase PRMT8. Comparison of young (Lu d6) and more aged (Ctx) neuronal samples suggested a maturation-dependent switch in the expression of functionally homologous proteins. For instance, the ratio of the histone H3 K27 methyltransfereases, EZH1 to EZH2, was high in Ctx and low in Lu d6. The same was observed for the polycomb repressive complex 1 (PRC1) subunits CBX7 and CBX8. A large proportion of EMGs in differentiated cells was very differently expressed than in hESC, and absolute levels were significantly higher in neuronal samples than in hepatic cells. Thus, there seem to be distinct qualitative and quantitative differences in EMG expression between cell lineages.
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Affiliation(s)
- Matthias K. Weng
- Doerenkamp-Zbinden Chair for In Vitro Toxicology and Biomedicine, University of Konstanz, Konstanz, Germany
- * E-mail:
| | - Karthick Natarajan
- Center of Physiology and Pathophysiology, Institute of Neurophysiology, University of Cologne (UKK), Cologne, Germany
| | - Diana Scholz
- Doerenkamp-Zbinden Chair for In Vitro Toxicology and Biomedicine, University of Konstanz, Konstanz, Germany
| | - Violeta N. Ivanova
- Nycomed-Chair for Bioinformatics and Information Mining, Dept. of Computer and Information Science, University of Konstanz, Konstanz, Germany
| | - Agapios Sachinidis
- Center of Physiology and Pathophysiology, Institute of Neurophysiology, University of Cologne (UKK), Cologne, Germany
| | - Jan G. Hengstler
- Leibniz Research Centre for Working Environment and Human Factors (IfADo), Technical University of Dortmund, Dortmund, Germany
| | - Tanja Waldmann
- Doerenkamp-Zbinden Chair for In Vitro Toxicology and Biomedicine, University of Konstanz, Konstanz, Germany
| | - Marcel Leist
- Doerenkamp-Zbinden Chair for In Vitro Toxicology and Biomedicine, University of Konstanz, Konstanz, Germany
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96
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Labate D, Laezza F, Negi P, Ozcan B, Papadakis M. Efficient processing of fluorescence images using directional multiscale representations. MATHEMATICAL MODELLING OF NATURAL PHENOMENA 2014; 9:177-193. [PMID: 28804225 PMCID: PMC5553129 DOI: 10.1051/mmnp/20149512] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
Recent advances in high-resolution fluorescence microscopy have enabled the systematic study of morphological changes in large populations of cells induced by chemical and genetic perturbations, facilitating the discovery of signaling pathways underlying diseases and the development of new pharmacological treatments. In these studies, though, due to the complexity of the data, quantification and analysis of morphological features are for the vast majority handled manually, slowing significantly data processing and limiting often the information gained to a descriptive level. Thus, there is an urgent need for developing highly efficient automated analysis and processing tools for fluorescent images. In this paper, we present the application of a method based on the shearlet representation for confocal image analysis of neurons. The shearlet representation is a newly emerged method designed to combine multiscale data analysis with superior directional sensitivity, making this approach particularly effective for the representation of objects defined over a wide range of scales and with highly anisotropic features. Here, we apply the shearlet representation to problems of soma detection of neurons in culture and extraction of geometrical features of neuronal processes in brain tissue, and propose it as a new framework for large-scale fluorescent image analysis of biomedical data.
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Affiliation(s)
- D. Labate
- Dept. of Mathematics, University of Houston, Houston, Texas 77204, USA
| | - F. Laezza
- Dept. of Pharmacology and Toxicology, UT Medical Branch, Galveston, TX 77555, USA
| | - P. Negi
- Dept. of Mathematics, University of Houston, Houston, Texas 77204, USA
| | - B. Ozcan
- Dept. of Mathematics, University of Houston, Houston, Texas 77204, USA
| | - M. Papadakis
- Dept. of Mathematics, University of Houston, Houston, Texas 77204, USA
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97
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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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98
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Schmitz C, Eastwood BS, Tappan SJ, Glaser JR, Peterson DA, Hof PR. Current automated 3D cell detection methods are not a suitable replacement for manual stereologic cell counting. Front Neuroanat 2014; 8:27. [PMID: 24847213 PMCID: PMC4019880 DOI: 10.3389/fnana.2014.00027] [Citation(s) in RCA: 48] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2013] [Accepted: 04/16/2014] [Indexed: 12/27/2022] Open
Abstract
Stereologic cell counting has had a major impact on the field of neuroscience. A major bottleneck in stereologic cell counting is that the user must manually decide whether or not each cell is counted according to three-dimensional (3D) stereologic counting rules by visual inspection within hundreds of microscopic fields-of-view per investigated brain or brain region. Reliance on visual inspection forces stereologic cell counting to be very labor-intensive and time-consuming, and is the main reason why biased, non-stereologic two-dimensional (2D) “cell counting” approaches have remained in widespread use. We present an evaluation of the performance of modern automated cell detection and segmentation algorithms as a potential alternative to the manual approach in stereologic cell counting. The image data used in this study were 3D microscopic images of thick brain tissue sections prepared with a variety of commonly used nuclear and cytoplasmic stains. The evaluation compared the numbers and locations of cells identified unambiguously and counted exhaustively by an expert observer with those found by three automated 3D cell detection algorithms: nuclei segmentation from the FARSIGHT toolkit, nuclei segmentation by 3D multiple level set methods, and the 3D object counter plug-in for ImageJ. Of these methods, FARSIGHT performed best, with true-positive detection rates between 38 and 99% and false-positive rates from 3.6 to 82%. The results demonstrate that the current automated methods suffer from lower detection rates and higher false-positive rates than are acceptable for obtaining valid estimates of cell numbers. Thus, at present, stereologic cell counting with manual decision for object inclusion according to unbiased stereologic counting rules remains the only adequate method for unbiased cell quantification in histologic tissue sections.
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Affiliation(s)
- Christoph Schmitz
- Department of Neuroanatomy, Ludwig-Maximilians-University of Munich Munich, Germany
| | | | - Susan J Tappan
- MBF Bioscience Williston, VT, USA ; MBF Laboratories Williston, VT, USA
| | - Jack R Glaser
- MBF Bioscience Williston, VT, USA ; MBF Laboratories Williston, VT, USA
| | - Daniel A Peterson
- Center for Stem Cell and Regenerative Medicine, Rosalind Franklin University of Medicine and Science North Chicago, IL, USA
| | - Patrick R Hof
- Fishberg Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai New York, NY, USA
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99
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Guo Y, Xu X, Wang Y, Wang Y, Xia S, Yang Z. An image processing pipeline to detect and segment nuclei in muscle fiber microscopic images. Microsc Res Tech 2014; 77:547-59. [PMID: 24777764 DOI: 10.1002/jemt.22373] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2014] [Revised: 03/21/2014] [Accepted: 04/15/2014] [Indexed: 02/01/2023]
Abstract
Muscle fiber images play an important role in the medical diagnosis and treatment of many muscular diseases. The number of nuclei in skeletal muscle fiber images is a key bio-marker of the diagnosis of muscular dystrophy. In nuclei segmentation one primary challenge is to correctly separate the clustered nuclei. In this article, we developed an image processing pipeline to automatically detect, segment, and analyze nuclei in microscopic image of muscle fibers. The pipeline consists of image pre-processing, identification of isolated nuclei, identification and segmentation of clustered nuclei, and quantitative analysis. Nuclei are initially extracted from background by using local Otsu's threshold. Based on analysis of morphological features of the isolated nuclei, including their areas, compactness, and major axis lengths, a Bayesian network is trained and applied to identify isolated nuclei from clustered nuclei and artifacts in all the images. Then a two-step refined watershed algorithm is applied to segment clustered nuclei. After segmentation, the nuclei can be quantified for statistical analysis. Comparing the segmented results with those of manual analysis and an existing technique, we find that our proposed image processing pipeline achieves good performance with high accuracy and precision. The presented image processing pipeline can therefore help biologists increase their throughput and objectivity in analyzing large numbers of nuclei in muscle fiber images.
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Affiliation(s)
- Yanen Guo
- Key laboratory of Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou, China; Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, Zhejiang University, Hangzhou, China
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100
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Iwabuchi S, Kakazu Y, Koh JY, Charles Harata N. Evaluation of the effectiveness of Gaussian filtering in distinguishing punctate synaptic signals from background noise during image analysis. J Neurosci Methods 2014; 223:92-113. [DOI: 10.1016/j.jneumeth.2013.12.003] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2013] [Revised: 11/28/2013] [Accepted: 12/03/2013] [Indexed: 01/22/2023]
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