Quantification of hepatic steatosis in histologic images by deep learning method.

Journal: Journal of X-ray science and technology
PMID: 31744039

Abstract

OBJECTIVE: To develop and test a novel method for automatic quantification of hepatic steatosis in histologic images based on the deep learning scheme designed to predict the fat ratio directly, which aims to improve accuracy in diagnosis of non-alcoholic fatty liver disease (NAFLD) with objective assessment of the severity of hepatic steatosis instead of subjective visual estimation.

Authors

  • Fan Yang
    School of Electrical Engineering and Automation, Jiangsu Normal University, Xuzhou, China.
  • Xianyuan Jia
    School of Biology & Engineering, Guizhou Medical University, Guiyang, Guizhou Province, China.
  • Pinggui Lei
    Department of Medical Imaging, The Affiliated Hospital of Guizhou Medical University, Guiyang, Guizhou Province, China.
  • Yan He
    School of Biology & Engineering, Guizhou Medical University, Guiyang, Guizhou Province, China.
  • Yining Xiang
    Department of Pathology, The Affiliated Hospital of Guizhou Medical University, Guiyang, Guizhou Province, China.
  • Jun Jiao
    Department of Radiology, The Affiliated Hospital of Guizhou Medical University, Guiyang, Guizhou Province, China.
  • Shi Zhou
    Department of Radiology, The Affiliated Hospital of Guizhou Medical University, Guiyang, Guizhou Province, China.
  • Wei Qian
    Department of Electrical and Computer Engineering, University of Texas at El Paso, 500 West University Avenue, El Paso, TX 79968, USA; Sino-Dutch Biomedical and Information Engineering School, Northeastern University, No.11, Lane 3, Wenhua Road, Heping District, Shenyang, Liaoning 110819, China. Electronic address: wqian@utep.edu.
  • Qinghong Duan
    Department of Radiology, The Affiliated Hospital of Guizhou Medical University, Guiyang, Guizhou Province, China.

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