Deep-learning-assisted detection and segmentation of rib fractures from CT scans: Development and validation of FracNet.

Journal: EBioMedicine
Published Date: Dec 1, 2020

Abstract

BACKGROUND: Diagnosis of rib fractures plays an important role in identifying trauma severity. However, quickly and precisely identifying the rib fractures in a large number of CT images with increasing number of patients is a tough task, which is also subject to the qualification of radiologist. We aim at a clinically applicable automatic system for rib fracture detection and segmentation from CT scans.

Authors

  • Liang Jin
    Radiology Department, Huadong Hospital, Affiliated with Fudan University, Shanghai, China.
  • Jiancheng Yang
    Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai, P.R. China.
  • Kaiming Kuang
    Dianei Technology, Shanghai, P.R. China.
  • Bingbing Ni
    Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai, P.R. China.
  • Yiyi Gao
    Radiology Department, Huadong Hospital, affiliated to Fudan University, Shanghai, China.
  • Yingli Sun
    Central Laboratory, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, China.
  • Pan Gao
    College of Information Science and Technology, Shihezi University, Shihezi 832003, China.
  • Weiling Ma
    Radiology Department, Huadong Hospital, affiliated to Fudan University, Shanghai, China.
  • Mingyu Tan
    Radiology Department, Huadong Hospital, affiliated to Fudan University, Shanghai, China.
  • Hui Kang
    Dianei Technology, Shanghai, P.R. China.
  • Jiajun Chen
    Dianei Technology, Shanghai, P.R. China.
  • Ming Li
    Radiology Department, Huadong Hospital, Affiliated with Fudan University, Shanghai, China.

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