An automatic fresh rib fracture detection and positioning system using deep learning.

Journal: The British journal of radiology
PMID:

Abstract

OBJECTIVE: To evaluate the performance and robustness of a deep learning-based automatic fresh rib fracture detection and positioning system (FRF-DPS).

Authors

  • Ning Li
    Department of Respiratory and Critical Care Medicine, Center for Respiratory Medicine, the Fourth Affiliated Hospital of School of Medicine, and International School of Medicine, International Institutes of Medicine, Zhejiang University, Yiwu, China.
  • Zhe Wu
    School of Automation, Central South University, Changsha, China.
  • Chao Jiang
    Zhejiang Provincial Key Laboratory of Cancer Molecular Cell Biology, Life Sciences Institute, Zhejiang University, Hangzhou, Zhejiang, China.
  • Lulu Sun
    Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO, United States.
  • Bingyao Li
    Department of Radiology, Fushun Central Hospital of Liaoning Province, Fushun, Liaoning Province, China.
  • Jun Guo
    Department of Oncology, Dongfeng Hospital, Hubei University of Medicine, Shiyan, Hubei 442008, P.R. China.
  • Feng Liu
    Department of Vascular and Endovascular Surgery, The First Medical Center of Chinese PLA General Hospital, 100853 Beijing, China.
  • Zhen Zhou
    Deepwise Healthcare, Beijing 100080, China.
  • Haibo Qin
    Department of Radiology, Fushun Central Hospital of Liaoning Province, Fushun, Liaoning Province, China.
  • Weixiong Tan
    Beijing Infervision Technology Co. Ltd., Beijing, 100025, China.
  • Lufeng Tian
    Department of Radiology, Fushun Central Hospital of Liaoning Province, Fushun, Liaoning Province, China.