Restaining-based annotation for cancer histology segmentation to overcome annotation-related limitations among pathologists.

ABSTRACT: Numerous cancer histopathology specimens have been collected and digitized over the past few decades. A comprehensive evaluation of the distribution of various cells in tumor tissue sections can provide valuable information for understanding cancer. Deep learning is suitable for achieving these goals; however, the collection of extensive, unbiased training data is hindered, thus limiting the production of accurate segmentation models. This study presents SegPath-the largest annotation dataset (>10 times larger than publicly available annotations)-for the segmentation of hematoxylin and eosin (H&E)-stained sections for eight major cell types in cancer tissue. The SegPath generating pipeline used H&E-stained sections that were destained and subsequently immunofluorescence-stained with carefu

SUBMITTER: Komura D 

PROVIDER: S-EPMC9982301 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature