Whole-cell segmentation of tissue images with human-level performance using large-scale data annotation and deep learning

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 ce...

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Vydané v:	Nature biotechnology Ročník 40; číslo 4; s. 555 - 565
Hlavní autori:	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
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York Nature Publishing Group US 01.04.2022 Nature Publishing Group
Predmet:	631/114/1564 631/114/794 631/1647/245 Agriculture Algorithms Annotations Bioinformatics Biomedical and Life Sciences Biomedical Engineering/Biotechnology Biomedicine Biotechnology Cell lineage Cell morphology Cytology Data Curation Datasets Deep Learning Feature extraction Human performance Humans Image processing Image Processing, Computer-Assisted - methods Image segmentation Life Sciences Localization Machine learning Tissue analysis Training
ISSN:	1087-0156, 1546-1696, 1546-1696
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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. Deep learning algorithms perform as well as humans in identifying cells in tissue images.
AbstractList	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. Deep learning algorithms perform as well as humans in identifying cells in tissue images. 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.Deep learning algorithms perform as well as humans in identifying cells in tissue images. 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. A major 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. 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.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.
Author	Fullaway, Christine Camacho Miller, Geneva McIntosh, Brianna J. Soon, Erin Risom, Tyler Camplisson, Isabella Warshawsky, Shiri Bendall, Sean C. Van Valen, David Fong, Mara Cui, Sunny Abraham, Zion Greenbaum, Shirley Greenwald, Noah F. Schwartz, Morgan Sarah Kong, Alex Hollmann, Travis Moseley, Jackson Graf, William Keren, Leeat Singh, Jaiveer Kagel, Adam Leow, Ke Xuan Moen, Erick Bar-Tal, Omer Angelo, Michael Chaudhry, Gautam Dougherty, Thomas Pavelchek, Cole
AuthorAffiliation	4. Present address: Washington University School of Medicine in St. Louis 2. Department of Pathology, Stanford University 3. Division of Biology and Bioengineering, California Institute of Technology 7. Department of Molecular Cell Biology, Weizmann Institute of Science 9. Immunology Program, Stanford University 8. Department of Cognitive, Linguistic, and Psychological Sciences, Brown University 5. Department of Electrical Engineering, California Institute of Technology 6. Present address: Department of Computer Science, Princeton University 1. Cancer Biology Program, Stanford University 10. Department of Pathology, Memorial Sloan Kettering Cancer Center
AuthorAffiliation_xml	– name: 8. Department of Cognitive, Linguistic, and Psychological Sciences, Brown University – name: 6. Present address: Department of Computer Science, Princeton University – name: 9. Immunology Program, Stanford University – name: 5. Department of Electrical Engineering, California Institute of Technology – name: 10. Department of Pathology, Memorial Sloan Kettering Cancer Center – name: 1. Cancer Biology Program, Stanford University – name: 3. Division of Biology and Bioengineering, California Institute of Technology – name: 4. Present address: Washington University School of Medicine in St. Louis – name: 2. Department of Pathology, Stanford University – name: 7. Department of Molecular Cell Biology, Weizmann Institute of Science
Author_xml	– sequence: 1 givenname: Noah F. orcidid: 0000-0002-7836-4379 surname: Greenwald fullname: Greenwald, Noah F. organization: Cancer Biology Program, Stanford University, Department of Pathology, Stanford University – sequence: 2 givenname: Geneva surname: Miller fullname: Miller, Geneva organization: Division of Biology and Bioengineering, California Institute of Technology – sequence: 3 givenname: Erick surname: Moen fullname: Moen, Erick organization: Division of Biology and Bioengineering, California Institute of Technology – sequence: 4 givenname: Alex surname: Kong fullname: Kong, Alex organization: Department of Pathology, Stanford University – sequence: 5 givenname: Adam surname: Kagel fullname: Kagel, Adam organization: Department of Pathology, Stanford University – sequence: 6 givenname: Thomas surname: Dougherty fullname: Dougherty, Thomas organization: Division of Biology and Bioengineering, California Institute of Technology – sequence: 7 givenname: Christine Camacho surname: Fullaway fullname: Fullaway, Christine Camacho organization: Department of Pathology, Stanford University – sequence: 8 givenname: Brianna J. orcidid: 0000-0003-3626-625X surname: McIntosh fullname: McIntosh, Brianna J. organization: Cancer Biology Program, Stanford University – sequence: 9 givenname: Ke Xuan surname: Leow fullname: Leow, Ke Xuan organization: Cancer Biology Program, Stanford University, Department of Pathology, Stanford University – sequence: 10 givenname: Morgan Sarah surname: Schwartz fullname: Schwartz, Morgan Sarah organization: Division of Biology and Bioengineering, California Institute of Technology – sequence: 11 givenname: Cole orcidid: 0000-0001-9249-6637 surname: Pavelchek fullname: Pavelchek, Cole organization: Division of Biology and Bioengineering, California Institute of Technology, Washington University School of Medicine in St. Louis – sequence: 12 givenname: Sunny surname: Cui fullname: Cui, Sunny organization: Department of Electrical Engineering, California Institute of Technology, Department of Computer Science, Princeton University – sequence: 13 givenname: Isabella surname: Camplisson fullname: Camplisson, Isabella organization: Division of Biology and Bioengineering, California Institute of Technology – sequence: 14 givenname: Omer orcidid: 0000-0003-1622-3674 surname: Bar-Tal fullname: Bar-Tal, Omer organization: Department of Molecular Cell Biology, Weizmann Institute of Science – sequence: 15 givenname: Jaiveer surname: Singh fullname: Singh, Jaiveer organization: Department of Pathology, Stanford University – sequence: 16 givenname: Mara surname: Fong fullname: Fong, Mara organization: Department of Pathology, Stanford University, Department of Cognitive, Linguistic and Psychological Sciences, Brown University – sequence: 17 givenname: Gautam orcidid: 0000-0003-2240-9846 surname: Chaudhry fullname: Chaudhry, Gautam organization: Department of Pathology, Stanford University – sequence: 18 givenname: Zion surname: Abraham fullname: Abraham, Zion organization: Department of Pathology, Stanford University – sequence: 19 givenname: Jackson surname: Moseley fullname: Moseley, Jackson organization: Department of Pathology, Stanford University – sequence: 20 givenname: Shiri surname: Warshawsky fullname: Warshawsky, Shiri organization: Department of Pathology, Stanford University – sequence: 21 givenname: Erin surname: Soon fullname: Soon, Erin organization: Department of Pathology, Stanford University, Immunology Program, Stanford University – sequence: 22 givenname: Shirley orcidid: 0000-0002-0680-7652 surname: Greenbaum fullname: Greenbaum, Shirley organization: Department of Pathology, Stanford University – sequence: 23 givenname: Tyler surname: Risom fullname: Risom, Tyler organization: Department of Pathology, Stanford University – sequence: 24 givenname: Travis orcidid: 0000-0003-1599-0433 surname: Hollmann fullname: Hollmann, Travis organization: Department of Pathology, Memorial Sloan Kettering Cancer Center – sequence: 25 givenname: Sean C. orcidid: 0000-0003-1341-2453 surname: Bendall fullname: Bendall, Sean C. organization: Department of Pathology, Stanford University – sequence: 26 givenname: Leeat orcidid: 0000-0002-6799-6303 surname: Keren fullname: Keren, Leeat organization: Department of Molecular Cell Biology, Weizmann Institute of Science – sequence: 27 givenname: William orcidid: 0000-0002-4264-9479 surname: Graf fullname: Graf, William organization: Division of Biology and Bioengineering, California Institute of Technology – sequence: 28 givenname: Michael orcidid: 0000-0003-1531-5067 surname: Angelo fullname: Angelo, Michael email: mangelo0@stanford.edu organization: Department of Pathology, Stanford University – sequence: 29 givenname: David orcidid: 0000-0001-7534-7621 surname: Van Valen fullname: Van Valen, David email: vanvalen@caltech.edu organization: Division of Biology and Bioengineering, California Institute of Technology
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/34795433$$D View this record in MEDLINE/PubMed
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Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 Authorship Contributions These authors contributed equally to this work N.F.G., L.K., M.A., and D.V.V. conceived the project. E.M. and D.V.V. conceived the human-in-the-loop approach. L.K. and M.A. conceived the whole-cell segmentation approach. G.M., T.D., E.M., W.G., and D.V.V. developed DeepCell Label. G.M., N.F.G., E.M., I.C., W.G., and D.V.V. developed the human-in-the-loop pipeline. M.S., C.P., W.G., and D.V.V. developed Mesmer’s deep learning architecture. W.G., N.F.G., and D.V.V. developed model training software. C.P. and W.G. developed cloud deployment. M.S., S.C., W.G., and D.V.V. developed metrics software. W.G. developed plug-ins. N.F.G., A.Kong, A.Kagel, J.S., and O.B-T. developed the multiplex image analysis pipeline. A.Kagel and G.M. developed the pathologist evaluation software. N.F.G., G.M., and T.H. supervised training data creation. N.F.G., C.C.F., B.M., K.L., M.F., G.C., Z.A., J.M., and S.W. performed quality control on the training data. E.S., S.G., and T.R. generated MIBI-TOF data for morphological analyses. S.B. helped with experimental design. N.F.G., W.G., and D.V.V. trained the models. N.F.G., W.G., G.M., and D.V.V. performed data analysis. N.F.G., G.M., M.A., and D.V.V. wrote the manuscript. M.A. and D.V.V. supervised the project. All authors provided feedback on the manuscript.
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Snippet	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... 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... A major 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...
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SubjectTerms	631/114/1564 631/114/794 631/1647/245 Agriculture Algorithms Annotations Bioinformatics Biomedical and Life Sciences Biomedical Engineering/Biotechnology Biomedicine Biotechnology Cell lineage Cell morphology Cytology Data Curation Datasets Deep Learning Feature extraction Human performance Humans Image processing Image Processing, Computer-Assisted - methods Image segmentation Life Sciences Localization Machine learning Tissue analysis Training
Title	Whole-cell segmentation of tissue images with human-level performance using large-scale data annotation and deep learning
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