Deep Learning for Multi-Tissue Segmentation and Fully Automatic Personalized Biomechanical Models from BACPAC Clinical Lumbar Spine MRI.

Journal: Pain medicine (Malden, Mass.)
Published Date:

Abstract

STUDY DESIGN: In vivo retrospective study of fully automatic quantitative imaging feature extraction from clinically acquired lumbar spine magnetic resonance imaging (MRI).

Authors

  • Madeline Hess
    Department of Radiology and Biomedical Imaging, Center for Intelligent Imaging, University of California, San Francisco, San Francisco, California.
  • Brett Allaire
    Center for Advanced Orthopedic Studies, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA.
  • Kenneth T Gao
    Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States.
  • Radhika Tibrewala
    Department of Radiology and Biomedical Imaging, University of California, San Francisco, California, USA.
  • Gaurav Inamdar
    Department of Radiology and Biomedical Imaging, Center for Intelligent Imaging, University of California, San Francisco, San Francisco, California.
  • Upasana Bharadwaj
    Department of Radiology and Biomedical Imaging, Center for Intelligent Imaging, University of California, San Francisco, San Francisco, California.
  • Cynthia Chin
    Department of Radiology and Biomedical Imaging, Center for Intelligent Imaging, University of California, San Francisco, San Francisco, California.
  • Valentina Pedoia
    Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA 94143, USA.
  • Mary Bouxsein
    Center for Advanced Orthopedic Studies, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA.
  • Dennis Anderson
    Center for Advanced Orthopedic Studies, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA.
  • Sharmila Majumdar
    Department of Radiology and Biomedical Imaging, University of California San Francisco, 1700 4th Street, Byers Hall, Suite 203, Room 203D, San Francisco, CA 94158, USA.