Robust and efficient abdominal CT segmentation using shape constrained multi-scale attention network.

Journal: Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
Published Date: Jun 1, 2023

Abstract

PURPOSE: Although many deep learning-based abdominal multi-organ segmentation networks have been proposed, the various intensity distributions and organ shapes of the CT images from multi-center, multi-phase with various diseases introduce new challenges for robust abdominal CT segmentation. To achieve robust and efficient abdominal multi-organ segmentation, a new two-stage method is presented in this study.

Authors

  • Nuo Tong
    Key Lab of Intelligent Perception and Image Understanding of Ministry of Education, Xidian University, Xi'an, Shaanxi, 710071, China.
  • Yinan Xu
    College of Electrical Engineering, Zhejiang University of Water Resources and Electric Power, Hangzhou 310018, China.
  • Jinsong Zhang
    Department of Emergency, Jiangsu Province Hospital, The First Affiliated Hospital of Nanjing Medical University, Nanjing, People's Republic of China. zhangjso@njmu.edu.cn.
  • Shuiping Gou
    Key Lab of Intelligent Perception and Image Understanding of Ministry of Education, Xidian University, Xi'an, Shaanxi, 710071, China.
  • Mengbin Li
    Xijing Hospital of the Fourth Military Medical University, Xian, Shaanxi, China.

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