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Whole-cell segmentation of tissue images with human-level performance using large-scale data annotation and deep learning.
Greenwald, Noah F; Miller, Geneva; Moen, Erick; Kong, Alex; Kagel, Adam; Dougherty, Thomas; Fullaway, Christine Camacho; McIntosh, Brianna J; Leow, Ke Xuan; Schwartz, Morgan Sarah; Pavelchek, Cole; Cui, Sunny; Camplisson, Isabella; Bar-Tal, Omer; Singh, Jaiveer; Fong, Mara; Chaudhry, Gautam; Abraham, Zion; Moseley, Jackson; Warshawsky, Shiri; Soon, Erin; Greenbaum, Shirley; Risom, Tyler; Hollmann, Travis; Bendall, Sean C; Keren, Leeat; Graf, William; Angelo, Michael; Van Valen, David.
Afiliação
  • Greenwald NF; Cancer Biology Program, Stanford University, Stanford, CA, USA.
  • Miller G; Department of Pathology, Stanford University, Stanford, CA, USA.
  • Moen E; Division of Biology and Bioengineering, California Institute of Technology, Pasadena, CA, USA.
  • Kong A; Division of Biology and Bioengineering, California Institute of Technology, Pasadena, CA, USA.
  • Kagel A; Department of Pathology, Stanford University, Stanford, CA, USA.
  • Dougherty T; Department of Pathology, Stanford University, Stanford, CA, USA.
  • Fullaway CC; Division of Biology and Bioengineering, California Institute of Technology, Pasadena, CA, USA.
  • McIntosh BJ; Department of Pathology, Stanford University, Stanford, CA, USA.
  • Leow KX; Cancer Biology Program, Stanford University, Stanford, CA, USA.
  • Schwartz MS; Cancer Biology Program, Stanford University, Stanford, CA, USA.
  • Pavelchek C; Department of Pathology, Stanford University, Stanford, CA, USA.
  • Cui S; Division of Biology and Bioengineering, California Institute of Technology, Pasadena, CA, USA.
  • Camplisson I; Division of Biology and Bioengineering, California Institute of Technology, Pasadena, CA, USA.
  • Bar-Tal O; Washington University School of Medicine in St. Louis, St. Louis, MO, USA.
  • Singh J; Department of Electrical Engineering, California Institute of Technology, Pasadena, CA, USA.
  • Fong M; Department of Computer Science, Princeton University, Princeton, NJ, USA.
  • Chaudhry G; Division of Biology and Bioengineering, California Institute of Technology, Pasadena, CA, USA.
  • Abraham Z; Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel.
  • Moseley J; Department of Pathology, Stanford University, Stanford, CA, USA.
  • Warshawsky S; Department of Pathology, Stanford University, Stanford, CA, USA.
  • Soon E; Department of Cognitive, Linguistic and Psychological Sciences, Brown University, Providence, RI, USA.
  • Greenbaum S; Department of Pathology, Stanford University, Stanford, CA, USA.
  • Risom T; Department of Pathology, Stanford University, Stanford, CA, USA.
  • Hollmann T; Department of Pathology, Stanford University, Stanford, CA, USA.
  • Bendall SC; Department of Pathology, Stanford University, Stanford, CA, USA.
  • Keren L; Department of Pathology, Stanford University, Stanford, CA, USA.
  • Graf W; Immunology Program, Stanford University, Stanford, CA, USA.
  • Angelo M; Department of Pathology, Stanford University, Stanford, CA, USA.
  • Van Valen D; Department of Pathology, Stanford University, Stanford, CA, USA.
Nat Biotechnol ; 40(4): 555-565, 2022 04.
Article em En | MEDLINE | ID: mdl-34795433
ABSTRACT
A principal challenge in the analysis of tissue imaging data is cell segmentation-the task of identifying the precise boundary of every cell in an image. To address this problem we constructed TissueNet, a dataset for training segmentation models that contains more than 1 million manually labeled cells, an order of magnitude more than all previously published segmentation training datasets. We used TissueNet to train Mesmer, a deep-learning-enabled segmentation algorithm. We demonstrated that Mesmer is more accurate than previous methods, generalizes to the full diversity of tissue types and imaging platforms in TissueNet, and achieves human-level performance. Mesmer enabled the automated extraction of key cellular features, such as subcellular localization of protein signal, which was challenging with previous approaches. We then adapted Mesmer to harness cell lineage information in highly multiplexed datasets and used this enhanced version to quantify cell morphology changes during human gestation. All code, data and models are released as a community resource.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Aprendizado Profundo Limite: Humans Idioma: En Revista: Nat Biotechnol Assunto da revista: BIOTECNOLOGIA Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Estados Unidos
Texto completo
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PubMed Links
Buscar no Google

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Aprendizado Profundo Limite: Humans Idioma: En Revista: Nat Biotechnol Assunto da revista: BIOTECNOLOGIA Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Estados Unidos