A fully automated sex estimation for proximal femur X-ray images through deep learning detection and classification.

Journal: Legal medicine (Tokyo, Japan)
Published Date:

Abstract

PURPOSE: To develop a fully automated deep learning pipeline using digital radiographs to detect the proximal femur region for accurate automated sex estimation.

Authors

  • Yuan Li
    NHC Key Lab of Hormones and Development and Tianjin Key Lab of Metabolic Diseases, Tianjin Medical University Chu Hsien-I Memorial Hospital & Institute of Endocrinology, Tianjin, China.
  • Chaoqun Niu
    College of Computer Science, Sichuan University, Chengdu 610065 Sichuan, PR China.
  • Jian Wang
    Veterinary Diagnostic Center, Shanghai Animal Disease Control Center, Shanghai, China.
  • Yong Xu
    Department of Psychiatry, The First Hospital of Shanxi Medical University, Taiyuan, China.
  • Hao Dai
    Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, FL.
  • Tu Xiong
    Department of Radiology, The People's Hospital of Leshan, Leshan, Sichuan 614000, PR China.
  • Dong Yu
    State Key Laboratory of Genetic Engineering, School of Life Sciences and Shanghai Cancer Hospital/Cancer Institute, Fudan University, Shanghai 200438, China.
  • Huili Guo
    Department of Radiology, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, PR China.
  • Weibo Liang
    Department of Forensic Genetics, West China School of Preclinical and Forensic Medicine, Sichuan University, Chengdu, 610041, Sichuan, People's Republic of China.
  • Zhenhua Deng
    Department of Forensic Pathology, West China School of Preclinical and Forensic Medicine, Sichuan University, No. three, 17 South Renmin Road, Wuhou District, Chengdu City, 610041, Sichuan, People's Republic of China. fydzh63@163.com.
  • Jiancheng Lv
    Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu 610065, P. R. China.
  • Lin Zhang
    Laboratory of Molecular Translational Medicine, Centre for Translational Medicine, Key Laboratory of Birth Defects and Related Diseases of Women and Children, Ministry of Education, Clinical Research Center for Birth Defects of Sichuan Province, West China Second Hospital, Sichuan University, Chengdu, Sichuan, 610041, China. Electronic address: zhanglin@scu.edu.cn.