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The multimodality cell segmentation challenge: toward universal solutions.
Ma, Jun; Xie, Ronald; Ayyadhury, Shamini; Ge, Cheng; Gupta, Anubha; Gupta, Ritu; Gu, Song; Zhang, Yao; Lee, Gihun; Kim, Joonkee; Lou, Wei; Li, Haofeng; Upschulte, Eric; Dickscheid, Timo; de Almeida, José Guilherme; Wang, Yixin; Han, Lin; Yang, Xin; Labagnara, Marco; Gligorovski, Vojislav; Scheder, Maxime; Rahi, Sahand Jamal; Kempster, Carly; Pollitt, Alice; Espinosa, Leon; Mignot, Tâm; Middeke, Jan Moritz; Eckardt, Jan-Niklas; Li, Wangkai; Li, Zhaoyang; Cai, Xiaochen; Bai, Bizhe; Greenwald, Noah F; Van Valen, David; Weisbart, Erin; Cimini, Beth A; Cheung, Trevor; Brück, Oscar; Bader, Gary D; Wang, Bo.
Affiliation
  • Ma J; Peter Munk Cardiac Centre, University Health Network, Toronto, Ontario, Canada.
  • Xie R; Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario, Canada.
  • Ayyadhury S; Vector Institute, Toronto, Ontario, Canada.
  • Ge C; Peter Munk Cardiac Centre, University Health Network, Toronto, Ontario, Canada.
  • Gupta A; Vector Institute, Toronto, Ontario, Canada.
  • Gupta R; Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada.
  • Gu S; Donnelly Centre, University of Toronto, Toronto, Ontario, Canada.
  • Zhang Y; Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada.
  • Lee G; School of Medicine and Pharmacy, Ocean University of China, Qingdao, China.
  • Kim J; Department of Electronics and Communications Engineering, Indraprastha Institute of Information Technology Delhi (IIITD), New Delhi, India.
  • Lou W; Laboratory Oncology Unit, Dr. BRAIRCH, All India Institute of Medical Sciences, New Delhi, India.
  • Li H; Department of Image Reconstruction, Nanjing Anke Medical Technology Co., Nanjing, China.
  • Upschulte E; Shanghai Artificial Intelligence Laboratory, Shanghai, China.
  • Dickscheid T; Graduate School of AI, KAIST, Seoul, South Korea.
  • de Almeida JG; Graduate School of AI, KAIST, Seoul, South Korea.
  • Wang Y; Shenzhen Research Institute of Big Data, Shenzhen, China.
  • Han L; Chinese University of Hong Kong (Shenzhen), Shenzhen, China.
  • Yang X; Shenzhen Research Institute of Big Data, Shenzhen, China.
  • Labagnara M; Institute of Neuroscience and Medicine (INM-1) and Helmholtz AI, Research Center Jülich, Jülich, Germany.
  • Gligorovski V; Institute of Neuroscience and Medicine (INM-1) and Helmholtz AI, Research Center Jülich, Jülich, Germany.
  • Scheder M; Faculty of Mathematics and Natural Sciences - Institute of Computer Science, Heinrich Heine University Düsseldorf, Düsseldorf, Germany.
  • Rahi SJ; European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, UK.
  • Kempster C; Champalimaud Foundation - Centre for the Unknown, Lisbon, Portugal.
  • Pollitt A; Department of Bioengineering, Stanford University, Palo Alto, CA, USA.
  • Espinosa L; Tandon School of Engineering, New York University, New York, NY, USA.
  • Mignot T; School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China.
  • Middeke JM; Laboratory of the Physics of Biological Systems, Institute of Physics, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
  • Eckardt JN; Laboratory of the Physics of Biological Systems, Institute of Physics, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
  • Li W; Laboratory of the Physics of Biological Systems, Institute of Physics, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
  • Li Z; Laboratory of the Physics of Biological Systems, Institute of Physics, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
  • Cai X; School of Biological Sciences, University of Reading, Reading, UK.
  • Bai B; School of Biological Sciences, University of Reading, Reading, UK.
  • Greenwald NF; Laboratoire de Chimie Bactérienne, CNRS-Université Aix-Marseille UMR, Institut de Microbiologie de la Méditerranée, Marseille, France.
  • Van Valen D; Laboratoire de Chimie Bactérienne, CNRS-Université Aix-Marseille UMR, Institut de Microbiologie de la Méditerranée, Marseille, France.
  • Weisbart E; Department of Internal Medicine I, University Hospital Dresden, Technical University Dresden, Dresden, Germany.
  • Cimini BA; Else Kroener Fresenius Center for Digital Health, Technical University Dresden, Dresden, Germany.
  • Cheung T; Department of Internal Medicine I, University Hospital Dresden, Technical University Dresden, Dresden, Germany.
  • Brück O; Else Kroener Fresenius Center for Digital Health, Technical University Dresden, Dresden, Germany.
  • Bader GD; Department of Automation, University of Science and Technology of China, Hefei, China.
  • Wang B; Institute of Advanced Technology, University of Science and Technology of China, Hefei, China.
Nat Methods ; 21(6): 1103-1113, 2024 Jun.
Article in En | MEDLINE | ID: mdl-38532015
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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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Algorithms / Image Processing, Computer-Assisted / Single-Cell Analysis / Deep Learning Limits: Animals / Humans Language: En Journal: Nat Methods Journal subject: TECNICAS E PROCEDIMENTOS DE LABORATORIO Year: 2024 Type: Article Affiliation country: Canada
Fulltext
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Algorithms / Image Processing, Computer-Assisted / Single-Cell Analysis / Deep Learning Limits: Animals / Humans Language: En Journal: Nat Methods Journal subject: TECNICAS E PROCEDIMENTOS DE LABORATORIO Year: 2024 Type: Article Affiliation country: Canada