Deep-learning-based accurate hepatic steatosis quantification for histological assessment of liver biopsies.

ABSTRACT: Hepatic steatosis droplet quantification with histology biopsies has high clinical significance for risk stratification and management of patients with fatty liver diseases and in the decision to use donor livers for transplantation. However, pathology reviewing processes, when conducted manually, are subject to a high inter- and intra-reader variability, due to the overwhelmingly large number and significantly varying appearance of steatosis instances. This process is challenging as there is a large number of overlapped steatosis droplets with either missing or weak boundaries. In this study, we propose a deep-learning-based region-boundary integrated network for precise steatosis quantification with whole slide liver histopathology images. The proposed model consists of two sequential st

SUBMITTER: Roy M 

PROVIDER: S-EPMC7502534 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature