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

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 cha

SUBMITTER: Greenwald NF 

PROVIDER: S-EPMC9010346 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature