Resolution-agnostic tissue segmentation in whole-slide histopathology images with convolutional neural networks.

ABSTRACT: Modern pathology diagnostics is being driven toward large scale digitization of microscopic tissue sections. A prerequisite for its safe implementation is the guarantee that all tissue present on a glass slide can also be found back in the digital image. Whole-slide scanners perform a tissue segmentation in a low resolution overview image to prevent inefficient high-resolution scanning of empty background areas. However, currently applied algorithms can fail in detecting all tissue regions. In this study, we developed convolutional neural networks to distinguish tissue from background. We collected 100 whole-slide images of 10 tissue samples-staining categories from five medical centers for development and testing. Additionally, eight more images of eight unfamiliar categories were collect

SUBMITTER: Bandi P 

PROVIDER: S-EPMC6924324 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature