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Updated: Aug 1, 2025

A High-Throughput In Situ Method for Estimation of Hepatocyte Nuclear Ploidy in Mice

A High-Throughput In Situ Method for Estimation of Hepatocyte Nuclear Ploidy in Mice

Published on: April 19, 2020

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

Zhuoyu Wen¹, Yu-Hsuan Lin², Shidan Wang¹

¹Quantitative Biomedical Research Center, Department of Population and Data Sciences, The University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.

|April 28, 2023

Summary

This summary is machine-generated.

This study introduces a new computational method to quantify liver cell polyploidy using routine H&E images. This accessible approach automates ploidy analysis, aiding liver disease research.

Keywords:

deep learning hematoxylin-eosin (H&E) histopathology images liver ploidy

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Last Updated: Aug 1, 2025

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Published on: May 10, 2020

Area of Science:

Cell Biology
Computational Pathology
Genomics

Background:

Polyploidy, or whole genome duplication, is common in liver cells but difficult to quantify clinically.
Current methods like flow cytometry and immunofluorescence are costly and time-consuming.
Hematoxylin-eosin (H&E) images are standard in clinical practice but lack automated ploidy analysis.

Purpose of the Study:

To develop an accessible computational algorithm for quantifying hepatic polyploidy.
To enable ploidy analysis using routine H&E histopathology images.
To provide a tool for studying polyploidy's role in liver diseases.

Main Methods:

Developed a deep learning algorithm to segment and classify cell nuclei in H&E images.
Determined cellular ploidy via nuclear distance analysis.
Quantified nuclear ploidy using a Gaussian mixture model.
Automated the analysis of total hepatocytes and ploidy in regions of interest.

Main Results:

Successfully automated hepatic ploidy quantification from H&E images.
The algorithm can determine total hepatocyte counts and detailed ploidy status.
This represents the first automated ploidy analysis method for H&E images.

Conclusions:

The developed algorithm offers an accessible and automated method for hepatic ploidy analysis.
This tool can significantly advance research into polyploidy in human liver disease.
The approach enhances the clinical utility of standard histopathology images.