Whole-cell segmentation of tissue images with human-level performance using large-scale data annotation and deep learning.
IF: 68.164
Cited by: 178


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.



MeSH terms

Data Curation
Deep Learning
Image Processing, Computer-Assisted


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

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