1
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Chandrasekaran SN, Cimini BA, Goodale A, Miller L, Kost-Alimova M, Jamali N, Doench JG, Fritchman B, Skepner A, Melanson M, Kalinin AA, Arevalo J, Haghighi M, Caicedo JC, Kuhn D, Hernandez D, Berstler J, Shafqat-Abbasi H, Root DE, Swalley SE, Garg S, Singh S, Carpenter AE. Three million images and morphological profiles of cells treated with matched chemical and genetic perturbations. Nat Methods 2024:10.1038/s41592-024-02241-6. [PMID: 38594452 DOI: 10.1038/s41592-024-02241-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Accepted: 03/11/2024] [Indexed: 04/11/2024]
Abstract
The identification of genetic and chemical perturbations with similar impacts on cell morphology can elucidate compounds' mechanisms of action or novel regulators of genetic pathways. Research on methods for identifying such similarities has lagged due to a lack of carefully designed and well-annotated image sets of cells treated with chemical and genetic perturbations. Here we create such a Resource dataset, CPJUMP1, in which each perturbed gene's product is a known target of at least two chemical compounds in the dataset. We systematically explore the directionality of correlations among perturbations that target the same protein encoded by a given gene, and we find that identifying matches between chemical and genetic perturbations is a challenging task. Our dataset and baseline analyses provide a benchmark for evaluating methods that measure perturbation similarities and impact, and more generally, learn effective representations of cellular state from microscopy images. Such advancements would accelerate the applications of image-based profiling of cellular states, such as uncovering drug mode of action or probing functional genomics.
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Affiliation(s)
| | - Beth A Cimini
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Amy Goodale
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Lisa Miller
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | - Nasim Jamali
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - John G Doench
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | - Adam Skepner
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | | | - John Arevalo
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | | | | | | | | | | | - David E Root
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
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2
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Kalinin AA, Arevalo J, Vulliard L, Serrano E, Tsang H, Bornholdt M, Rajwa B, Carpenter AE, Way GP, Singh S. A versatile information retrieval framework for evaluating profile strength and similarity. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.01.587631. [PMID: 38617315 PMCID: PMC11014546 DOI: 10.1101/2024.04.01.587631] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/16/2024]
Abstract
In profiling assays, thousands of biological properties are measured in a single test, yielding biological discoveries by capturing the state of a cell population, often at the single-cell level. However, for profiling datasets, it has been challenging to evaluate the phenotypic activity of a sample and the phenotypic consistency among samples, due to profiles' high dimensionality, heterogeneous nature, and non-linear properties. Existing methods leave researchers uncertain where to draw boundaries between meaningful biological response and technical noise. Here, we developed a statistical framework that uses the well-established mean average precision (mAP) as a single, data-driven metric to bridge this gap. We validated the mAP framework against established metrics through simulations and real-world data applications, revealing its ability to capture subtle and meaningful biological differences in cell state. Specifically, we used mAP to assess both phenotypic activity for a given perturbation (or a sample) as well as consistency within groups of perturbations (or samples) across diverse high-dimensional datasets. We evaluated the framework on different profile types (image, protein, and mRNA profiles), perturbation types (CRISPR gene editing, gene overexpression, and small molecules), and profile resolutions (single-cell and bulk). Our open-source software allows this framework to be applied to identify interesting biological phenomena and promising therapeutics from large-scale profiling data.
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Affiliation(s)
| | - John Arevalo
- Imaging Platform, Broad Institute of MIT and Harvard, Cambridge MA, USA
| | - Loan Vulliard
- Systems Immunology and Single-Cell Biology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Erik Serrano
- Department of Biomedical Informatics, University of Colorado School of Medicine, Aurora CO, USA
| | - Hillary Tsang
- Imaging Platform, Broad Institute of MIT and Harvard, Cambridge MA, USA
| | - Michael Bornholdt
- Imaging Platform, Broad Institute of MIT and Harvard, Cambridge MA, USA
| | - Bartek Rajwa
- Bindley Bioscience Center, Purdue University, West Lafayette IN, USA
| | - Anne E. Carpenter
- Imaging Platform, Broad Institute of MIT and Harvard, Cambridge MA, USA
| | - Gregory P. Way
- Department of Biomedical Informatics, University of Colorado School of Medicine, Aurora CO, USA
| | - Shantanu Singh
- Imaging Platform, Broad Institute of MIT and Harvard, Cambridge MA, USA
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3
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Ma J, Xie R, Ayyadhury S, Ge C, Gupta A, Gupta R, Gu S, Zhang Y, Lee G, Kim J, Lou W, Li H, Upschulte E, Dickscheid T, de Almeida JG, Wang Y, Han L, Yang X, Labagnara M, Gligorovski V, Scheder M, Rahi SJ, Kempster C, Pollitt A, Espinosa L, Mignot T, Middeke JM, Eckardt JN, Li W, Li Z, Cai X, Bai B, Greenwald NF, Van Valen D, Weisbart E, Cimini BA, Cheung T, Brück O, Bader GD, Wang B. The multimodality cell segmentation challenge: toward universal solutions. Nat Methods 2024:10.1038/s41592-024-02233-6. [PMID: 38532015 DOI: 10.1038/s41592-024-02233-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Accepted: 03/04/2024] [Indexed: 03/28/2024]
Abstract
Cell segmentation is a critical step for quantitative single-cell analysis in microscopy images. Existing cell segmentation methods are often tailored to specific modalities or require manual interventions to specify hyper-parameters in different experimental settings. Here, we present a multimodality cell segmentation benchmark, comprising more than 1,500 labeled images derived from more than 50 diverse biological experiments. The top participants developed a Transformer-based deep-learning algorithm that not only exceeds existing methods but can also be applied to diverse microscopy images across imaging platforms and tissue types without manual parameter adjustments. This benchmark and the improved algorithm offer promising avenues for more accurate and versatile cell analysis in microscopy imaging.
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Affiliation(s)
- Jun Ma
- Peter Munk Cardiac Centre, University Health Network, Toronto, Ontario, Canada
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario, Canada
- Vector Institute, Toronto, Ontario, Canada
| | - Ronald Xie
- Peter Munk Cardiac Centre, University Health Network, Toronto, Ontario, Canada
- Vector Institute, Toronto, Ontario, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
| | - Shamini Ayyadhury
- Donnelly Centre, University of Toronto, Toronto, Ontario, Canada
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada
| | - Cheng Ge
- School of Medicine and Pharmacy, Ocean University of China, Qingdao, China
| | - Anubha Gupta
- Department of Electronics and Communications Engineering, Indraprastha Institute of Information Technology Delhi (IIITD), New Delhi, India
| | - Ritu Gupta
- Laboratory Oncology Unit, Dr. BRAIRCH, All India Institute of Medical Sciences, New Delhi, India
| | - Song Gu
- Department of Image Reconstruction, Nanjing Anke Medical Technology Co., Nanjing, China
| | - Yao Zhang
- Shanghai Artificial Intelligence Laboratory, Shanghai, China
| | - Gihun Lee
- Graduate School of AI, KAIST, Seoul, South Korea
| | - Joonkee Kim
- Graduate School of AI, KAIST, Seoul, South Korea
| | - Wei Lou
- Shenzhen Research Institute of Big Data, Shenzhen, China
- Chinese University of Hong Kong (Shenzhen), Shenzhen, China
| | - Haofeng Li
- Shenzhen Research Institute of Big Data, Shenzhen, China
| | - Eric Upschulte
- Institute of Neuroscience and Medicine (INM-1) and Helmholtz AI, Research Center Jülich, Jülich, Germany
| | - Timo Dickscheid
- Institute of Neuroscience and Medicine (INM-1) and Helmholtz AI, Research Center Jülich, Jülich, Germany
- Faculty of Mathematics and Natural Sciences - Institute of Computer Science, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - José Guilherme de Almeida
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, UK
- Champalimaud Foundation - Centre for the Unknown, Lisbon, Portugal
| | - Yixin Wang
- Department of Bioengineering, Stanford University, Palo Alto, CA, USA
| | - Lin Han
- Tandon School of Engineering, New York University, New York, NY, USA
| | - Xin Yang
- School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China
| | - Marco Labagnara
- Laboratory of the Physics of Biological Systems, Institute of Physics, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Vojislav Gligorovski
- Laboratory of the Physics of Biological Systems, Institute of Physics, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Maxime Scheder
- Laboratory of the Physics of Biological Systems, Institute of Physics, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Sahand Jamal Rahi
- Laboratory of the Physics of Biological Systems, Institute of Physics, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Carly Kempster
- School of Biological Sciences, University of Reading, Reading, UK
| | - Alice Pollitt
- School of Biological Sciences, University of Reading, Reading, UK
| | - Leon Espinosa
- Laboratoire de Chimie Bactérienne, CNRS-Université Aix-Marseille UMR, Institut de Microbiologie de la Méditerranée, Marseille, France
| | - Tâm Mignot
- Laboratoire de Chimie Bactérienne, CNRS-Université Aix-Marseille UMR, Institut de Microbiologie de la Méditerranée, Marseille, France
| | - Jan Moritz Middeke
- Department of Internal Medicine I, University Hospital Dresden, Technical University Dresden, Dresden, Germany
- Else Kroener Fresenius Center for Digital Health, Technical University Dresden, Dresden, Germany
| | - Jan-Niklas Eckardt
- Department of Internal Medicine I, University Hospital Dresden, Technical University Dresden, Dresden, Germany
- Else Kroener Fresenius Center for Digital Health, Technical University Dresden, Dresden, Germany
| | - Wangkai Li
- Department of Automation, University of Science and Technology of China, Hefei, China
| | - Zhaoyang Li
- Institute of Advanced Technology, University of Science and Technology of China, Hefei, China
| | - Xiaochen Cai
- Department of Computer Science and Technology, Nanjing University, Nanjing, China
| | - Bizhe Bai
- School of EECS, The University of Queensland, Brisbane, Queensland, Australia
| | | | - David Van Valen
- Division of Computing and Mathematical Science, Caltech, Pasadena, CA, USA
- Howard Hughes Medical Institute, Chevy Chase, MD, USA
| | - Erin Weisbart
- Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Beth A Cimini
- Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Trevor Cheung
- Peter Munk Cardiac Centre, University Health Network, Toronto, Ontario, Canada
- Department of Computer Science, University of Waterloo, Waterloo, Ontario, Canada
| | - Oscar Brück
- Hematoscope Laboratory, Comprehensive Cancer Center & Center of Diagnostics, Helsinki University Hospital, Helsinki, Finland
- Department of Oncology, University of Helsinki, Helsinki, Finland
| | - Gary D Bader
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
- Donnelly Centre, University of Toronto, Toronto, Ontario, Canada
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, Ontario, Canada
- CIFAR Multiscale Human Program, CIFAR, Toronto, Ontario, Canada
| | - Bo Wang
- Peter Munk Cardiac Centre, University Health Network, Toronto, Ontario, Canada.
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario, Canada.
- Vector Institute, Toronto, Ontario, Canada.
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada.
- UHN AI Hub, University Health Network, Toronto, Ontario, Canada.
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4
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Chen W, Miao C, Zhang Z, Fung CSH, Wang R, Chen Y, Qian Y, Cheng L, Yip KY, Tsui SKW, Cao Q. Commonly used software tools produce conflicting and overly-optimistic AUPRC values. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.02.578654. [PMID: 38370825 PMCID: PMC10871236 DOI: 10.1101/2024.02.02.578654] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/20/2024]
Abstract
The precision-recall curve (PRC) and the area under it (AUPRC) are useful for quantifying classification performance. They are commonly used in situations with imbalanced classes, such as cancer diagnosis and cell type annotation. We evaluated 10 popular tools for plotting PRC and computing AUPRC, which were collectively used in >3,000 published studies. We found the AUPRC values computed by the tools rank classifiers differently and some tools produce overly-optimistic results.
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Affiliation(s)
- Wenyu Chen
- School of Biomedical Sciences, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR, China
| | - Chen Miao
- School of Biomedical Sciences, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR, China
| | - Zhenghao Zhang
- Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR, China
| | - Cathy Sin-Hang Fung
- School of Biomedical Sciences, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR, China
| | - Ran Wang
- School of Biomedical Sciences, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR, China
| | - Yizhen Chen
- Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR, China
| | - Yan Qian
- The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Lixin Cheng
- Shenzhen People’s Hospital, First Affiliated Hospital of Southern University of Science and Technology, Second Clinical Medicine College of Jinan University, Shenzhen, China
| | - Kevin Y. Yip
- Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR, China
- Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, USA
| | - Stephen Kwok-Wing Tsui
- School of Biomedical Sciences, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR, China
- Hong Kong Bioinformatics Centre, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR, China
| | - Qin Cao
- School of Biomedical Sciences, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR, China
- Hong Kong Bioinformatics Centre, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR, China
- Shenzhen Research Institute, The Chinese University of Hong Kong, Shenzhen, China
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5
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Where imaging and metrics meet. Nat Methods 2024; 21:151. [PMID: 38347133 DOI: 10.1038/s41592-024-02187-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/15/2024]
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6
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Reinke A, Tizabi MD, Baumgartner M, Eisenmann M, Heckmann-Nötzel D, Kavur AE, Rädsch T, Sudre CH, Acion L, Antonelli M, Arbel T, Bakas S, Benis A, Buettner F, Cardoso MJ, Cheplygina V, Chen J, Christodoulou E, Cimini BA, Farahani K, Ferrer L, Galdran A, van Ginneken B, Glocker B, Godau P, Hashimoto DA, Hoffman MM, Huisman M, Isensee F, Jannin P, Kahn CE, Kainmueller D, Kainz B, Karargyris A, Kleesiek J, Kofler F, Kooi T, Kopp-Schneider A, Kozubek M, Kreshuk A, Kurc T, Landman BA, Litjens G, Madani A, Maier-Hein K, Martel AL, Meijering E, Menze B, Moons KGM, Müller H, Nichyporuk B, Nickel F, Petersen J, Rafelski SM, Rajpoot N, Reyes M, Riegler MA, Rieke N, Saez-Rodriguez J, Sánchez CI, Shetty S, Summers RM, Taha AA, Tiulpin A, Tsaftaris SA, Van Calster B, Varoquaux G, Yaniv ZR, Jäger PF, Maier-Hein L. Understanding metric-related pitfalls in image analysis validation. Nat Methods 2024; 21:182-194. [PMID: 38347140 DOI: 10.1038/s41592-023-02150-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Accepted: 12/12/2023] [Indexed: 02/15/2024]
Abstract
Validation metrics are key for tracking scientific progress and bridging the current chasm between artificial intelligence research and its translation into practice. However, increasing evidence shows that, particularly in image analysis, metrics are often chosen inadequately. Although taking into account the individual strengths, weaknesses and limitations of validation metrics is a critical prerequisite to making educated choices, the relevant knowledge is currently scattered and poorly accessible to individual researchers. Based on a multistage Delphi process conducted by a multidisciplinary expert consortium as well as extensive community feedback, the present work provides a reliable and comprehensive common point of access to information on pitfalls related to validation metrics in image analysis. Although focused on biomedical image analysis, the addressed pitfalls generalize across application domains and are categorized according to a newly created, domain-agnostic taxonomy. The work serves to enhance global comprehension of a key topic in image analysis validation.
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Affiliation(s)
- Annika Reinke
- German Cancer Research Center (DKFZ) Heidelberg, Division of Intelligent Medical Systems, Heidelberg, Germany.
- German Cancer Research Center (DKFZ) Heidelberg, HI Helmholtz Imaging, Heidelberg, Germany.
- Faculty of Mathematics and Computer Science, Heidelberg University, Heidelberg, Germany.
| | - Minu D Tizabi
- German Cancer Research Center (DKFZ) Heidelberg, Division of Intelligent Medical Systems, Heidelberg, Germany.
- National Center for Tumor Diseases (NCT), NCT Heidelberg, a partnership between DKFZ and University Medical Center Heidelberg, Heidelberg, Germany.
| | - Michael Baumgartner
- German Cancer Research Center (DKFZ) Heidelberg, Division of Medical Image Computing, Heidelberg, Germany
| | - Matthias Eisenmann
- German Cancer Research Center (DKFZ) Heidelberg, Division of Intelligent Medical Systems, Heidelberg, Germany
| | - Doreen Heckmann-Nötzel
- German Cancer Research Center (DKFZ) Heidelberg, Division of Intelligent Medical Systems, Heidelberg, Germany
- National Center for Tumor Diseases (NCT), NCT Heidelberg, a partnership between DKFZ and University Medical Center Heidelberg, Heidelberg, Germany
| | - A Emre Kavur
- German Cancer Research Center (DKFZ) Heidelberg, Division of Intelligent Medical Systems, Heidelberg, Germany
- German Cancer Research Center (DKFZ) Heidelberg, Division of Medical Image Computing, Heidelberg, Germany
- German Cancer Research Center (DKFZ) Heidelberg, HI Applied Computer Vision Lab, Heidelberg, Germany
| | - Tim Rädsch
- German Cancer Research Center (DKFZ) Heidelberg, Division of Intelligent Medical Systems, Heidelberg, Germany
- German Cancer Research Center (DKFZ) Heidelberg, HI Helmholtz Imaging, Heidelberg, Germany
| | - Carole H Sudre
- MRC Unit for Lifelong Health and Ageing at UCL and Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
- School of Biomedical Engineering and Imaging Science, King's College London, London, UK
| | - Laura Acion
- Instituto de Cálculo, CONICET - Universidad de Buenos Aires, Buenos Aires, Argentina
| | - Michela Antonelli
- School of Biomedical Engineering and Imaging Science, King's College London, London, UK
- Centre for Medical Image Computing, University College London, London, UK
| | - Tal Arbel
- Centre for Intelligent Machines and MILA (Quebec Artificial Intelligence Institute), McGill University, Montréal, Quebec, Canada
| | - Spyridon Bakas
- Division of Computational Pathology, Dept of Pathology & Laboratory Medicine, Indiana University School of Medicine, Indianapolis, IN, USA
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
| | - Arriel Benis
- Department of Digital Medical Technologies, Holon Institute of Technology, Holon, Israel
- European Federation for Medical Informatics, Le Mont-sur-Lausanne, Switzerland
| | - Florian Buettner
- German Cancer Consortium (DKTK), partner site Frankfurt/Mainz, a partnership between DKFZ and UCT Frankfurt-Marburg, Frankfurt am Main, Germany
- German Cancer Research Center (DKFZ) Heidelberg, Heidelberg, Germany
- Goethe University Frankfurt, Department of Medicine, Frankfurt am Main, Germany
- Goethe University Frankfurt, Department of Informatics, Frankfurt am Main, Germany
- Frankfurt Cancer Insititute, Frankfurt am Main, Germany
| | - M Jorge Cardoso
- School of Biomedical Engineering and Imaging Science, King's College London, London, UK
| | - Veronika Cheplygina
- Department of Computer Science, IT University of Copenhagen, Copenhagen, Denmark
| | - Jianxu Chen
- Leibniz-Institut für Analytische Wissenschaften - ISAS - e.V., Dortmund, Germany
| | - Evangelia Christodoulou
- German Cancer Research Center (DKFZ) Heidelberg, Division of Intelligent Medical Systems, Heidelberg, Germany
| | - Beth A Cimini
- Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Keyvan Farahani
- Center for Biomedical Informatics and Information Technology, National Cancer Institute, Bethesda, MD, USA
| | - Luciana Ferrer
- Instituto de Investigación en Ciencias de la Computación (ICC), CONICET-UBA, Ciudad Autónoma de Buenos Aires, Buenos Aires, Argentina
| | - Adrian Galdran
- Universitat Pompeu Fabra, Barcelona, Spain
- University of Adelaide, Adelaide, South Australia, Australia
| | - Bram van Ginneken
- Fraunhofer MEVIS, Bremen, Germany
- Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Ben Glocker
- Department of Computing, Imperial College London, South Kensington Campus, London, UK
| | - Patrick Godau
- German Cancer Research Center (DKFZ) Heidelberg, Division of Intelligent Medical Systems, Heidelberg, Germany
- Faculty of Mathematics and Computer Science, Heidelberg University, Heidelberg, Germany
- National Center for Tumor Diseases (NCT), NCT Heidelberg, a partnership between DKFZ and University Medical Center Heidelberg, Heidelberg, Germany
| | - Daniel A Hashimoto
- Department of Surgery, Perelman School of Medicine, Philadelphia, PA, USA
- General Robotics Automation Sensing and Perception Laboratory, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA
| | - Michael M Hoffman
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
- Vector Institute for Artificial Intelligence, Toronto, Ontario, Canada
| | - Merel Huisman
- Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Fabian Isensee
- German Cancer Research Center (DKFZ) Heidelberg, Division of Medical Image Computing, Heidelberg, Germany
- German Cancer Research Center (DKFZ) Heidelberg, HI Applied Computer Vision Lab, Heidelberg, Germany
| | - Pierre Jannin
- Laboratoire Traitement du Signal et de l'Image - UMR_S 1099, Université de Rennes 1, Rennes, France
- INSERM, Paris, France
| | - Charles E Kahn
- Department of Radiology and Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, PA, USA
| | - Dagmar Kainmueller
- Max-Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Biomedical Image Analysis and HI Helmholtz Imaging, Berlin, Germany
- University of Potsdam, Digital Engineering Faculty, Potsdam, Germany
| | - Bernhard Kainz
- Department of Computing, Faculty of Engineering, Imperial College London, London, UK
- Department AIBE, Friedrich-Alexander-Universität (FAU), Erlangen-Nürnberg, Germany
| | | | - Jens Kleesiek
- Translational Image-guided Oncology (TIO), Institute for AI in Medicine (IKIM), University Medicine Essen, Essen, Germany
| | | | | | - Annette Kopp-Schneider
- German Cancer Research Center (DKFZ) Heidelberg, Division of Biostatistics, Heidelberg, Germany
| | - Michal Kozubek
- Centre for Biomedical Image Analysis and Faculty of Informatics, Masaryk University, Brno, Czech Republic
| | - Anna Kreshuk
- Cell Biology and Biophysics Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany
| | - Tahsin Kurc
- Department of Biomedical Informatics, Stony Brook University, Health Science Center, Stony Brook, NY, USA
| | | | - Geert Litjens
- Department of Pathology, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Amin Madani
- Department of Surgery, University Health Network, Philadelphia, PA, USA
| | - Klaus Maier-Hein
- German Cancer Research Center (DKFZ) Heidelberg, Division of Medical Image Computing, Heidelberg, Germany
- Pattern Analysis and Learning Group, Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany
| | - Anne L Martel
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
- Physical Sciences, Sunnybrook Research Institute, Toronto, Ontario, Canada
| | - Erik Meijering
- School of Computer Science and Engineering, University of New South Wales, UNSW Sydney, Kensington, New South Wales, Australia
| | - Bjoern Menze
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
| | - Karel G M Moons
- Julius Center for Health Sciences and Primary Care, UMC Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Henning Müller
- Information Systems Institute, University of Applied Sciences Western Switzerland (HES-SO), Sierre, Switzerland
- Medical Faculty, University of Geneva, Geneva, Switzerland
| | - Brennan Nichyporuk
- MILA (Quebec Artificial Intelligence Institute), Montréal, Quebec, Canada
| | - Felix Nickel
- Department of General, Visceral and Thoracic Surgery, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Jens Petersen
- German Cancer Research Center (DKFZ) Heidelberg, Division of Medical Image Computing, Heidelberg, Germany
| | | | - Nasir Rajpoot
- Tissue Image Analytics Laboratory, Department of Computer Science, University of Warwick, Coventry, UK
| | - Mauricio Reyes
- ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland
- Department of Radiation Oncology, University Hospital Bern, University of Bern, Bern, Switzerland
| | - Michael A Riegler
- Simula Metropolitan Center for Digital Engineering, Oslo, Norway
- UiT The Arctic University of Norway, Tromsø, Norway
| | | | - Julio Saez-Rodriguez
- Institute for Computational Biomedicine, Heidelberg University, Heidelberg, Germany
- Faculty of Medicine, Heidelberg University Hospital, Heidelberg, Germany
| | - Clara I Sánchez
- Informatics Institute, Faculty of Science, University of Amsterdam, Amsterdam, the Netherlands
| | | | - Ronald M Summers
- National Institutes of Health Clinical Center, Bethesda, MD, USA
| | - Abdel A Taha
- Institute of Information Systems Engineering, TU Wien, Vienna, Austria
| | - Aleksei Tiulpin
- Research Unit of Health Sciences and Technology, Faculty of Medicine, University of Oulu, Oulu, Finland
- Neurocenter Oulu, Oulu University Hospital, Oulu, Finland
| | | | - Ben Van Calster
- Department of Development and Regeneration and EPI-centre, KU Leuven, Leuven, Belgium
- Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, the Netherlands
| | - Gaël Varoquaux
- Parietal project team, INRIA Saclay-Île de France, Palaiseau, France
| | - Ziv R Yaniv
- National Institute of Allergy and Infectious Diseases, Bethesda, MD, USA
| | - Paul F Jäger
- German Cancer Research Center (DKFZ) Heidelberg, HI Helmholtz Imaging, Heidelberg, Germany.
- German Cancer Research Center (DKFZ) Heidelberg, Interactive Machine Learning Group, Heidelberg, Germany.
| | - Lena Maier-Hein
- German Cancer Research Center (DKFZ) Heidelberg, Division of Intelligent Medical Systems, Heidelberg, Germany.
- German Cancer Research Center (DKFZ) Heidelberg, HI Helmholtz Imaging, Heidelberg, Germany.
- Faculty of Mathematics and Computer Science, Heidelberg University, Heidelberg, Germany.
- National Center for Tumor Diseases (NCT), NCT Heidelberg, a partnership between DKFZ and University Medical Center Heidelberg, Heidelberg, Germany.
- Faculty of Medicine, Heidelberg University Hospital, Heidelberg, Germany.
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7
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Gogoberidze N, Cimini BA. Defining the boundaries: challenges and advances in identifying cells in microscopy images. Curr Opin Biotechnol 2024; 85:103055. [PMID: 38142646 DOI: 10.1016/j.copbio.2023.103055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2023] [Revised: 11/28/2023] [Accepted: 11/28/2023] [Indexed: 12/26/2023]
Abstract
Segmentation, or the outlining of objects within images, is a critical step in the measurement and analysis of cells within microscopy images. While improvements continue to be made in tools that rely on classical methods for segmentation, deep learning-based tools increasingly dominate advances in the technology. Specialist models such as Cellpose continue to improve in accuracy and user-friendliness, and segmentation challenges such as the Multi-Modality Cell Segmentation Challenge continue to push innovation in accuracy across widely varying test data as well as efficiency and usability. Increased attention on documentation, sharing, and evaluation standards is leading to increased user-friendliness and acceleration toward the goal of a truly universal method.
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Affiliation(s)
| | - Beth A Cimini
- Imaging Platform, Broad Institute, Cambridge, MA 02142, USA.
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8
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Wang Y, Zhao J, Xu H, Han C, Tao Z, Zhao D, Zhou D, Tong G, Liu D, Ji Z. A systematic evaluation of computation methods for cell segmentation. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.28.577670. [PMID: 38352578 PMCID: PMC10862744 DOI: 10.1101/2024.01.28.577670] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/22/2024]
Abstract
Cell segmentation is a fundamental task in analyzing biomedical images. Many computational methods have been developed for cell segmentation, but their performances are not well understood in various scenarios. We systematically evaluated the performance of 18 segmentation methods to perform cell nuclei and whole cell segmentation using light microscopy and fluorescence staining images. We found that general-purpose methods incorporating the attention mechanism exhibit the best overall performance. We identified various factors influencing segmentation performances, including training data and cell morphology, and evaluated the generalizability of methods across image modalities. We also provide guidelines for choosing the optimal segmentation methods in various real application scenarios. We developed Seggal, an online resource for downloading segmentation models already pre-trained with various tissue and cell types, which substantially reduces the time and effort for training cell segmentation models.
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Affiliation(s)
- Yuxing Wang
- Department of Computer Engineering, Rochester Institute of Technology, Rochester, NY, USA
- Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC, USA
| | - Junhan Zhao
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Department of Biostatistics, Harvard T.H.Chan School of Public Health, Boston, MA, USA
| | - Hongye Xu
- Department of Computer Engineering, Rochester Institute of Technology, Rochester, NY, USA
| | - Cheng Han
- Department of Computer Engineering, Rochester Institute of Technology, Rochester, NY, USA
| | - Zhiqiang Tao
- Department of Computer Engineering, Rochester Institute of Technology, Rochester, NY, USA
| | - Dongfang Zhao
- Department of Computer Science & eScience Institute, University of Washington, Seattle, WA, USA
| | - Dawei Zhou
- Department of Computer Science, Virginia Polytechnic Institute and State University, Blacksburg, VA, USA
| | - Gang Tong
- Department of Electrical and Computer Engineering, University of Rochester, Rochester, NY, USA
| | - Dongfang Liu
- Department of Computer Engineering, Rochester Institute of Technology, Rochester, NY, USA
| | - Zhicheng Ji
- Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC, USA
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9
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Goyal V, Schaub NJ, Voss TC, Hotaling NA. Unbiased image segmentation assessment toolkit for quantitative differentiation of state-of-the-art algorithms and pipelines. BMC Bioinformatics 2023; 24:388. [PMID: 37828466 PMCID: PMC10568754 DOI: 10.1186/s12859-023-05486-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Accepted: 09/18/2023] [Indexed: 10/14/2023] Open
Abstract
BACKGROUND Image segmentation pipelines are commonly used in microscopy to identify cellular compartments like nucleus and cytoplasm, but there are few standards for comparing segmentation accuracy across pipelines. The process of selecting a segmentation assessment pipeline can seem daunting to researchers due to the number and variety of metrics available for evaluating segmentation quality. RESULTS Here we present automated pipelines to obtain a comprehensive set of 69 metrics to evaluate segmented data and propose a selection methodology for models based on quantitative analysis, dimension reduction or unsupervised classification techniques and informed selection criteria. CONCLUSION We show that the metrics used here can often be reduced to a small number of metrics that give a more complete understanding of segmentation accuracy, with different groups of metrics providing sensitivity to different types of segmentation error. These tools are delivered as easy to use python libraries, command line tools, Common Workflow Language Tools, and as Web Image Processing Pipeline interactive plugins to ensure a wide range of users can access and use them. We also present how our evaluation methods can be used to observe the changes in segmentations across modern machine learning/deep learning workflows and use cases.
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Affiliation(s)
- Vishakha Goyal
- Information Research Technology Branch (ITRB), National Center for Advancing Translational Science (NCATS), National Institutes of Health (NIH), 9800 Medical Center Dr, Rockville, MD, 20850, USA
- Axle Research and Technologies, 6116 Executive Blvd #400, Rockville, MD, 20852, USA
| | - Nick J Schaub
- Information Research Technology Branch (ITRB), National Center for Advancing Translational Science (NCATS), National Institutes of Health (NIH), 9800 Medical Center Dr, Rockville, MD, 20850, USA
- Axle Research and Technologies, 6116 Executive Blvd #400, Rockville, MD, 20852, USA
| | - Ty C Voss
- Information Research Technology Branch (ITRB), National Center for Advancing Translational Science (NCATS), National Institutes of Health (NIH), 9800 Medical Center Dr, Rockville, MD, 20850, USA
- Axle Research and Technologies, 6116 Executive Blvd #400, Rockville, MD, 20852, USA
| | - Nathan A Hotaling
- Information Research Technology Branch (ITRB), National Center for Advancing Translational Science (NCATS), National Institutes of Health (NIH), 9800 Medical Center Dr, Rockville, MD, 20850, USA.
- Axle Research and Technologies, 6116 Executive Blvd #400, Rockville, MD, 20852, USA.
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