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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Bibliographic Details
Published in:	Nature biotechnology Vol. 40; no. 4; pp. 555 - 565
Main Authors:	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
Format:	Journal Article
Language:	English
Published:	New York Nature Publishing Group US 01.04.2022 Nature Publishing Group
Subjects:	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
Online Access:	Get full text
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