A self-supervised strategy for fully automatic segmentation of renal dynamic contrast-enhanced magnetic resonance images.

Journal: Medical physics
Published Date: Aug 16, 2019

Abstract

PURPOSE: An automated accurate segmentation for dynamic contrast-enhanced magnetic resonance (DCE-MR) image sequences is essential for quantification of renal function. A self-supervised strategy is proposed for fully automatic segmentation of the renal DCE-MR images without using manually labeled data.

Authors

  • Wenjian Huang
    Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China.
  • Hao Li
    Department of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Rui Wang
    Department of Clinical Laboratory Medicine Center, Inner Mongolia Autonomous Region People's Hospital, Hohhot, Inner Mongolia, 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
  • Jue Zhang
    Wuhan University Zhongnan Hospital, Wuhan 430071, China.

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