Can deep learning reduce the time and effort required for manual segmentation in 3D reconstruction of MRI in rotator cuff tears?

Journal: PloS one
Published Date: Oct 10, 2022

Abstract

BACKGROUND/PURPOSE: The use of MRI as a diagnostic tool has gained popularity in the field of orthopedics. Although 3-dimensional (3D) MRI offers more intuitive visualization and can better facilitate treatment planning than 2-dimensional (2D) MRI, manual segmentation for 3D visualization is time-consuming and lacks reproducibility. Recent advancements in deep learning may provide a solution to this problem through the process of automatic segmentation. The purpose of this study was to develop automated semantic segmentation on 2D MRI images of rotator cuff tears by using a convolutional neural network to visualize 3D models of related anatomic structures.

Authors

  • Hyojune Kim
    Department of Orthopedic Surgery, Daejeon Eulji Medical Center, Eulji University School of Medicine, Daejeon, Republic of Korea.
  • Keewon Shin
    Department of Biomedical Engineering, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
  • Hoyeon Kim
    Department of Mechanical Engineering, Southern Methodist University, Dallas, TX, United Stated of America.
  • Eui-Sup Lee
    Department of Orthopedic Surgery, Asan Medical Center, College of Medicine, University of Ulsan College of Medicine, Seoul, Republic of Korea.
  • Seok Won Chung
    a Department of Orthopaedic Surgery and.
  • Kyoung Hwan Koh
    Department of Orthopedic Surgery, Asan Medical Center, College of Medicine, University of Ulsan College of Medicine, Seoul, Republic of Korea.
  • Namkug Kim
    Department of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.

Keywords

External Resources

Popular Topics
Recent Journals