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

Journal: Genes
Published Date: Apr 16, 2023

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.

Authors

  • Zhuoyu Wen
    Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, TX, USA.
  • Yu-Hsuan Lin
    Children's Research Institute, Departments of Pediatrics and Internal Medicine, Center for Regenerative Science and Medicine, The University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.
  • Shidan Wang
    Quantitative Biomedical Research Center, Department of Clinical Sciences, University of Texas Southwestern Medical Center, 5325 Harry Hines Blvd, Dallas, TX, 75390, USA.
  • Naoto Fujiwara
    Department of Gastrointestinal surgery, Tokyo Medical and Dental University, Tokyo, Japan.
  • Ruichen Rong
    Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, Texas.
  • Kevin W Jin
    Quantitative Biomedical Research Center, Department of Population and Data Sciences, The University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.
  • Donghan M Yang
    Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, Texas.
  • Bo Yao
    Institute of Microanalytical Systems, Department of Chemistry, Zhejiang University, Hangzhou, 310058, China.
  • Shengjie Yang
    Quantitative Biomedical Research Center, Department of Population and Data Sciences, The University of Texas Southwestern Medical Center, Dallas, TX, USA.
  • Tao Wang
    Department of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Yang Xie
    Quantitative Biomedical Research Center, Department of Clinical Sciences, University of Texas Southwestern Medical Center, 5325 Harry Hines Blvd, Dallas, TX, 75390, USA.
  • Yujin Hoshida
    Division of Digestive and Liver Diseases, Department of Internal Medicine, The University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.
  • Hao Zhu
    State Key Laboratory of Advanced Technology for Materials Synthesis and Processing, Wuhan University of Technology Wuhan 430070 PR China chang@whut.edu.cn suntl@whut.edu.cn.
  • Guanghua Xiao

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