Glenoid segmentation from computed tomography scans based on a 2-stage deep learning model for glenoid bone loss evaluation.

Journal: Journal of shoulder and elbow surgery
PMID: 37308073

Abstract

BACKGROUND: The best-fitting circle drawn by computed tomography (CT) reconstruction of the en face view of the glenoid bone to measure the bone defect is widely used in clinical application. However, there are still some limitations in practical application, which can prevent the achievement of accurate measurements. This study aimed to accurately and automatically segment the glenoid from CT scans based on a 2-stage deep learning model and to quantitatively measure the glenoid bone defect.

Authors

  • Qingqing Zhao
    Department of Radiology, Peking University Third Hospital, Beijing 100191, China.
  • Quanlong Feng
    Deepwise Healthcare, Beijing, China.
  • Jianlun Zhang
    Peking University Third Hospital, Beijing, China.
  • Jingxu Xu
    Department of Research Collaboration, R&D Center, Beijing Deepwise & League of PHD Technology Co., Ltd., Beijing, China.
  • Zifeng Wu
    Australian Institute for Machine Learning, Australia.
  • Chencui Huang
    Department of Research Collaboration, R&D Center, Beijing Deepwise & League of PHD Technology Co., Ltd., Beijing, China.
  • Huishu Yuan
    Department of Radiology, Peking University Third Hospital, Beijing 10019, China.

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