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

Authors

• Zhuoyu Wen
• Yu-Hsuan Lin
• Shidan Wang
• Naoto Fujiwara
• Ruichen Rong
• Kevin W Jin
• Donghan M Yang
• Bo Yao
• Shengjie Yang
• Tao Wang
• Yang Xie
• Yujin Hoshida
• Hao Zhu
• Guanghua Xiao

Language

English

Publication Date

4-16-2023

Journal

Genes

DOI

10.3390/genes14040921

PMID

37107679

PMCID

PMC10137944

PubMedCentral® Posted Date

4-16-2023

PubMedCentral® Full Text Version

Post-print

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 determines nuclear ploidy using a fitted Gaussian mixture model. The algorithm can establish the total number of hepatocytes and their detailed ploidy information in a region of interest (ROI) on H&E images. This is the first successful attempt to automate ploidy analysis on H&E images. Our algorithm is expected to serve as an important tool for studying the role of polyploidy in human liver disease.

Keywords

Humans, Eosine Yellowish-(YS), Hematoxylin, Deep Learning, Liver, Ploidies, Polyploidy, deep learning, hematoxylin-eosin (H&E) histopathology images, ploidy, liver

Published Open-Access

yes

Recommended Citation

Wen, Zhuoyu; Lin, Yu-Hsuan; Wang, Shidan; et al., "Deep-Learning-Based Hepatic Ploidy Quantification Using H&E Histopathology Images" (2023). Faculty, Staff and Student Publications. 6752.
https://digitalcommons.library.tmc.edu/uthgsbs_docs/6752