Deep learning to segment pelvic bones: large-scale CT datasets and baseline models.

Journal: International journal of computer assisted radiology and surgery
Published Date: Apr 16, 2021

Abstract

PURPOSE: Pelvic bone segmentation in CT has always been an essential step in clinical diagnosis and surgery planning of pelvic bone diseases. Existing methods for pelvic bone segmentation are either hand-crafted or semi-automatic and achieve limited accuracy when dealing with image appearance variations due to the multi-site domain shift, the presence of contrasted vessels, coprolith and chyme, bone fractures, low dose, metal artifacts, etc. Due to the lack of a large-scale pelvic CT dataset with annotations, deep learning methods are not fully explored.

Authors

  • Pengbo Liu
    Institute of Computing Technology, Chinese Academy of Sciences, Chinese Academy of Sciences, Beijing, China.
  • Hu Han
  • Yuanqi Du
    Department of Computer Science, George Mason University, Fairfax, VA 22030, USA.
  • Heqin Zhu
    Institute of Computing Technology, Chinese Academy of Sciences, Chinese Academy of Sciences, Beijing, China.
  • Yinhao Li
  • Feng Gu
    Institute of Computing Technology, Chinese Academy of Sciences, Chinese Academy of Sciences, Beijing, China.
  • Honghu Xiao
    Beijing Jishuitan Hospital, Beijing, China.
  • Jun Li
    Department of Emergency, Zhuhai Integrated Traditional Chinese and Western Medicine Hospital, Zhuhai, 519020, Guangdong Province, China. quanshabai43@163.com.
  • Chunpeng Zhao
    Beijing Jishuitan Hospital, Beijing, China.
  • Li Xiao
    Key Laboratory of Intelligent Information Processing, Advanced Computer Research Center, State Key Laboratory of Computer Architecture, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China.
  • Xinbao Wu
    Department of Orthopedic Trauma, Beijing Jishuitan Hospital, Beijing 100035, China. Electronic address: wuxinbao_jst@126.com.
  • S Kevin Zhou

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