Deep-Learning-Based Hepatic Ploidy Quantification Using H&E Histopathology Images.

ABSTRACT: Polyploidy, the duplication of the entire genome within a single cell, is a significant characteristic of cells in many tissues, including the liver. The quantification of hepatic ploidy typically relies on flow cytometry and immunofluorescence (IF) imaging, which are not widely available in clinical settings due to high financial and time costs. To improve accessibility for clinical samples, we developed a computational algorithm to quantify hepatic ploidy using hematoxylin-eosin (H&E) histopathology images, which are commonly obtained during routine clinical practice. Our algorithm uses a deep learning model to first segment and classify different types of cell nuclei in H&E images. It then determines cellular ploidy based on the relative distance between identified hepatocyte nuclei and

SUBMITTER: Wen Z 

PROVIDER: S-EPMC10137944 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature