Automatic segmentation of hepatic metastases on DWI images based on a deep learning method: assessment of tumor treatment response according to the RECIST 1.1 criteria.

Journal: BMC cancer
PMID: 36476181

Abstract

BACKGROUND: Evaluation of treated tumors according to Response Evaluation Criteria in Solid Tumors (RECIST) criteria is an important but time-consuming task in medical imaging. Deep learning methods are expected to automate the evaluation process and improve the efficiency of imaging interpretation.

Authors

  • Xiang Liu
    College of Agricultural Science and Engineering, Hohai University, Nanjing 210098, China; Anhui Provincial Key Laboratory of Environmental Pollution Control and Resource Reuse, Anhui Jianzhu University, Hefei 230009, China.
  • Rui Wang
    Department of Clinical Laboratory Medicine Center, Inner Mongolia Autonomous Region People's Hospital, Hohhot, Inner Mongolia, China.
  • Zemin Zhu
    Department of Hepatobiliary and Pancreatic Surgery, Zhuzhou Central Hospital, Zhuzhou, 412000, China.
  • Kexin Wang
    Clifford Hospital, Guangzhou University of Chinese Medicine, Guangzhou, China.
  • Yue Gao
    Institute of Medical Technology, Peking University Health Science Center, Beijing, China.
  • Jialun Li
    Beijing Smart Tree Medical Technology Co. Ltd., Beijing, 100011, China.
  • Yaofeng Zhang
    Beijing Smart Tree Medical Technology co. Ltd., Beijing, China.
  • Xiangpeng Wang
    Beijing Smart Tree Medical Technology Co. Ltd., Beijing, China.
  • Xiaodong Zhang
    The Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu 611731, China.
  • XiaoYing Wang

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