End-to-end deep learning model for segmentation and severity staging of anterior cruciate ligament injuries from MRI.

Journal: Diagnostic and interventional imaging
PMID: 36328943

Abstract

PURPOSE: The purpose of this study was to develop a semi-supervised segmentation and classification deep learning model for the diagnosis of anterior cruciate ligament (ACL) tears on MRI based on a semi-supervised framework, double-linear layers U-Net (DCLU-Net).

Authors

  • Nguyen Tan Dung
    Department of Rehabilitation, Da Nang Hospital of C, Da Nang City 50000, Vietnam.
  • Ngo Huu Thuan
    Department of Radiology, Da Nang C Hospital, Da Nang city 50000, Viet Nam; Department of Medical Imaging, Da Nang University of Medical Technology and Pharmacy, Da Nang city, 50000, Viet Nam.
  • Truong Van Dung
    Department of Rehabilitation, Da Nang C Hospital, Da Nang City 50000, Viet Nam.
  • Le Van Nho
    Faculty of Medicine, Da Nang University of Medical Technology and Pharmacy, Da Nang City, 50000, Viet Nam.
  • Nguyen Minh Tri
    Advance Program in Computer Science, University of Science, Ho Chi Minh City 70000, Viet Nam; YRDx-AI Lab, Ho Chi Minh City 70000, Viet Nam.
  • Vu Pham Thao Vy
    YRDx-AI Lab, Ho Chi Minh City 70000, Viet Nam; International Master/Ph.D. Program in Medicine, College of Medicine, Taipei Medical University, Taipei 110, Taiwan.
  • Le Ngoc Hoang
    YRDx-AI Lab, Ho Chi Minh City 70000, Viet Nam; Graduate Institute of Biomedical Materials and Tissue Engineering, College of Biomedical Engineering, Taipei Medical University, Taipei 110, Taiwan.
  • Nguyen Thuan Phat
    YRDx-AI Lab, Ho Chi Minh City 70000, Viet Nam; Department of Computer Science, Vietnamese German University, Ho Chi Minh City 70000, Viet Nam.
  • Dang Anh Chuong
    YRDx-AI Lab, Ho Chi Minh City 70000, Viet Nam.
  • Luong Huu Dang
    Department of Otolaryngology, Faculty of Medicine, University of Medicine and Pharmacy at Ho Chi Minh City, Ho Chi Minh City 70000, Vietnam.

Keywords

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