Malignancy diagnosis of liver lesion in contrast enhanced ultrasound using an end-to-end method based on deep learning.

Journal: BMC medical imaging
Published Date: Mar 21, 2024

Abstract

BACKGROUND: Contrast-enhanced ultrasound (CEUS) is considered as an efficient tool for focal liver lesion characterization, given it allows real-time scanning and provides dynamic tissue perfusion information. An accurate diagnosis of liver lesions with CEUS requires a precise interpretation of CEUS images. However,it is a highly experience dependent task which requires amount of training and practice. To help improve the constrains, this study aims to develop an end-to-end method based on deep learning to make malignancy diagnosis of liver lesions using CEUS.

Authors

  • Hongyu Zhou
    Institute for AI in Medicine and Faculty of Medicine, Macau University of Science and Technology, Macau, China; National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou, China.
  • Jianmin Ding
    Department of Pathology and Laboratory Medicine, University of Texas Health Science Center-Houston, Medical School, Houston, TX, USA.
  • Yan Zhou
    Department of Computer Science, University of Texas at Dallas, Richardson, Texas 75080, United States.
  • Yandong Wang
    School of Automation, Central South University, Changsha 410083, China.
  • Lei Zhao
    Brain Cognition and Brain-Computer Intelligence Integration Group, Kunming University of Science and Technology, Kunming, China.
  • Cho-Chiang Shih
    Philips Ultrasound R&D Research, Shanghai, China.
  • Jingping Xu
    Philips Ultrasound R&D Research, Shanghai, China.
  • Jianan Wang
    School of Food Science, Henan Institute of Science and Technology, Xinxiang, 453003 China.
  • Ling Tong
  • Zhouye Chen
    Philips Ultrasound R&D Research, Shanghai, China.
  • Qizhong Lin
    Philips Ultrasound R&D Research, Shanghai, China.
  • Xiang Jing
    The Third Central Hospital of Tianjin, 83 Jintang Road, Hedong District, Tianjin, 300170, China. dr.jingxiang@vip.163.com.

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