An externally validated deep learning model for the accurate segmentation of the lumbar paravertebral muscles.

Journal: European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society
PMID: 35852607

Abstract

PURPOSE: Imaging studies about the relevance of muscles in spinal disorders, and sarcopenia in general, require the segmentation of the muscles in the images which is very labour-intensive if performed manually and poses a practical limit to the number of investigated subjects. This study aimed at developing a deep learning-based tool able to fully automatically perform an accurate segmentation of the lumbar muscles in axial MRI scans, and at validating the new tool on an external dataset.

Authors

  • Frank Niemeyer
    Institute of Orthopedic Research and Biomechanics, Center for Trauma Research Ulm, Ulm University, Ulm, Germany.
  • Annika Zanker
    Center for Trauma Research Ulm, Institute of Orthopaedic Research and Biomechanics, Ulm University, Ulm, Germany.
  • René Jonas
    Center for Trauma Research Ulm, Institute of Orthopaedic Research and Biomechanics, Ulm University, Ulm, Germany.
  • Youping Tao
    From the Institute for Orthopaedic Research and Biomechanics, University Hospital Ulm, Ulm, Germany.
  • Fabio Galbusera
    Laboratory of Biological Structures Mechanics, IRCCS Istituto Ortopedico Galeazzi, Via Galeazzi 4, 20161, Milan, Italy. fabio.galbusera@grupposandonato.it.
  • Hans-Joachim Wilke
    Institute of Orthopaedic Research and Biomechanics, Trauma Research Center Ulm, University Hospital Ulm, Helmholtzstraße 14, Ulm 89081, Germany. Electronic address: hans-joachim.wilke@uni-ulm.de.

Keywords

External Resources

Popular Topics
Recent Journals