The multimodality cell segmentation challenge: toward universal solutions.

Autor: Ma J; 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., Xie R; 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., Ayyadhury S; Donnelly Centre, University of Toronto, Toronto, Ontario, Canada.; Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada., Ge C; School of Medicine and Pharmacy, Ocean University of China, Qingdao, China., Gupta A; Department of Electronics and Communications Engineering, Indraprastha Institute of Information Technology Delhi (IIITD), New Delhi, India., Gupta R; Laboratory Oncology Unit, Dr. BRAIRCH, All India Institute of Medical Sciences, New Delhi, India., Gu S; Department of Image Reconstruction, Nanjing Anke Medical Technology Co., Nanjing, China., Zhang Y; Shanghai Artificial Intelligence Laboratory, Shanghai, China., Lee G; Graduate School of AI, KAIST, Seoul, South Korea., Kim J; Graduate School of AI, KAIST, Seoul, South Korea., Lou W; Shenzhen Research Institute of Big Data, Shenzhen, China.; Chinese University of Hong Kong (Shenzhen), Shenzhen, China., Li H; Shenzhen Research Institute of Big Data, Shenzhen, China., Upschulte E; Institute of Neuroscience and Medicine (INM-1) and Helmholtz AI, Research Center Jülich, Jülich, Germany., Dickscheid T; 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., de Almeida JG; European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, UK.; Champalimaud Foundation - Centre for the Unknown, Lisbon, Portugal., Wang Y; Department of Bioengineering, Stanford University, Palo Alto, CA, USA., Han L; Tandon School of Engineering, New York University, New York, NY, USA., Yang X; School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China., Labagnara M; Laboratory of the Physics of Biological Systems, Institute of Physics, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland., Gligorovski V; Laboratory of the Physics of Biological Systems, Institute of Physics, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland., Scheder M; Laboratory of the Physics of Biological Systems, Institute of Physics, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland., Rahi SJ; Laboratory of the Physics of Biological Systems, Institute of Physics, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland., Kempster C; School of Biological Sciences, University of Reading, Reading, UK., Pollitt A; School of Biological Sciences, University of Reading, Reading, UK., Espinosa L; Laboratoire de Chimie Bactérienne, CNRS-Université Aix-Marseille UMR, Institut de Microbiologie de la Méditerranée, Marseille, France., Mignot T; Laboratoire de Chimie Bactérienne, CNRS-Université Aix-Marseille UMR, Institut de Microbiologie de la Méditerranée, Marseille, France., Middeke JM; 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., Eckardt JN; 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., Li W; Department of Automation, University of Science and Technology of China, Hefei, China., Li Z; Institute of Advanced Technology, University of Science and Technology of China, Hefei, China., Cai X; Department of Computer Science and Technology, Nanjing University, Nanjing, China., Bai B; School of EECS, The University of Queensland, Brisbane, Queensland, Australia., Greenwald NF; School of Medicine, Stanford University, Palo Alto, CA, USA., Van Valen D; Division of Computing and Mathematical Science, Caltech, Pasadena, CA, USA.; Howard Hughes Medical Institute, Chevy Chase, MD, USA., Weisbart E; Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA., Cimini BA; Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA., Cheung T; Peter Munk Cardiac Centre, University Health Network, Toronto, Ontario, Canada.; Department of Computer Science, University of Waterloo, Waterloo, Ontario, Canada., Brück O; Hematoscope Laboratory, Comprehensive Cancer Center & Center of Diagnostics, Helsinki University Hospital, Helsinki, Finland.; Department of Oncology, University of Helsinki, Helsinki, Finland., Bader GD; 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., Wang B; Peter Munk Cardiac Centre, University Health Network, Toronto, Ontario, Canada. bowang@vectorinstitute.ai.; Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario, Canada. bowang@vectorinstitute.ai.; Vector Institute, Toronto, Ontario, Canada. bowang@vectorinstitute.ai.; Department of Computer Science, University of Toronto, Toronto, Ontario, Canada. bowang@vectorinstitute.ai.; UHN AI Hub, University Health Network, Toronto, Ontario, Canada. bowang@vectorinstitute.ai.
Jazyk: angličtina
Zdroj: Nature methods [Nat Methods] 2024 Jun; Vol. 21 (6), pp. 1103-1113. Date of Electronic Publication: 2024 Mar 26.
DOI: 10.1038/s41592-024-02233-6
Abstrakt: 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.
(© 2024. The Author(s), under exclusive licence to Springer Nature America, Inc.)
Databáze: MEDLINE
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