Deep learning-based automated detection and segmentation of bone and traumatic bone marrow lesions from MRI following an acute ACL tear.

Journal: Computers in biology and medicine
Published Date: Jun 20, 2024

Abstract

INTRODUCTION: Traumatic bone marrow lesions (BML) are frequently identified on knee MRI scans in patients following an acute full-thickness, complete ACL tear. BMLs coincide with regions of elevated localized bone loss, and studies suggest these may act as a precursor to the development of post-traumatic osteoarthritis. This study addresses the labour-intensive manual assessment of BMLs by using a 3D U-Net for automated identification and segmentation from MRI scans.

Authors

  • Callie E Stirling
    Department of Biomedical Engineering, Schulich School of Engineering, University of Calgary, Calgary, Canada; McCaig Institute for Bone and Joint Health, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.
  • Nathan J Neeteson
    Department of Biomedical Engineering, Schulich School of Engineering, University of Calgary, Calgary, Canada; McCaig Institute for Bone and Joint Health, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.
  • Richard E A Walker
    McCaig Institute for Bone and Joint Health, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada; Department of Radiology, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada.
  • Steven K Boyd
    Department of Biomedical Engineering, Schulich School of Engineering, University of Calgary, Calgary, Canada; McCaig Institute for Bone and Joint Health, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada; Department of Radiology, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada. Electronic address: skboyd@ucalgary.ca.

Keywords

External Resources

Popular Topics
Recent Journals