Intelligent Recognition and Segmentation of Blunt Craniocerebral Injury CT Images Based on DeepLabV3+ Model.

Journal: Fa yi xue za zhi
PMID:

Abstract

OBJECTIVES: To achieve intelligent recognition and segmentation of common craniocerebral injuries (hereinafter referred to as "segmentation") by training convolutional neural network DeepLabV3+ model based on CT images of blunt craniocerebral injury (BCI), and to explore the value of deep learning in automated diagnosis of BCI in forensic medicine.

Authors

  • Hao-Jie Qin
    College of Basic Medicine and Forensic Medicine, Henan University of Science and Technology, Institute of Medical Aspects of Specific Environments, Judicial Expertise Center, Luoyang 471000, Henan Province, China.
  • Yuan-Yuan Liu
    College of Pharmacy, Fujian University of Traditional Chinese Medicine, Fuzhou 350122, China.
  • En-Hao Fu
    College of Basic Medicine and Forensic Medicine, Henan University of Science and Technology, Institute of Medical Aspects of Specific Environments, Judicial Expertise Center, Luoyang 471000, Henan Province, China.
  • Ya-Wen Liu
    Shanghai Key Laboratory of Forensic Medicine, Key Laboratory of Forensic Science, Ministry of Justice, Shanghai Forensic Service Platform, Academy of Forensic Science, Shanghai 200063, China.
  • Zhi-Ling Tian
    Shanghai Key Laboratory of Forensic Medicine, Key Laboratory of Forensic Science, Ministry of Justice, Shanghai Forensic Service Platform, Academy of Forensic Science, Shanghai 200063, China.
  • He-Wen Dong
    Shanghai Key Laboratory of Forensic Medicine, Key Laboratory of Forensic Science, Ministry of Justice, Shanghai Forensic Service Platform, Academy of Forensic Science, Shanghai 200063, China.
  • Tai-Ang Liu
    School of Materials Science and Engineering, Shanghai University, NO99, Shangda Road, Baoshan District, Shanghai, China.
  • Dong-Hua Zou
    Shanghai Key Laboratory of Forensic Medicine, Key Laboratory of Forensic Science, Ministry of Justice, Shanghai Forensic Service Platform, Academy of Forensic Science, Shanghai 200063, China.
  • Yi-Bin Cheng
    Shanghai Key Laboratory of Forensic Medicine, Key Laboratory of Forensic Science, Ministry of Justice, Shanghai Forensic Service Platform, Academy of Forensic Science, Shanghai 200063, China.
  • Ning-Guo Liu
    Shanghai Key Laboratory of Forensic Medicine, Key Laboratory of Forensic Science, Ministry of Justice, Shanghai Forensic Service Platform, Academy of Forensic Science, Shanghai 200063, China.