Deep learning for the rapid automatic quantification and characterization of rotator cuff muscle degeneration from shoulder CT datasets.

Journal: European radiology
PMID: 32696257

Abstract

OBJECTIVES: This study aimed at developing a convolutional neural network (CNN) able to automatically quantify and characterize the level of degeneration of rotator cuff (RC) muscles from shoulder CT images including muscle atrophy and fatty infiltration.

Authors

  • Elham Taghizadeh
    ARTORG Center for Biomedical Engineering Research, University of Bern, Freiburgstrasse 3, CH-3010, Bern, Switzerland.
  • Oskar Truffer
    ARTORG Center for Biomedical Engineering Research, University of Bern, Freiburgstrasse 3, CH-3010, Bern, Switzerland.
  • Fabio Becce
    Department of Diagnostic and Interventional Radiology, Lausanne University Hospital, University of Lausanne, Lausanne 1011, Switzerland.
  • Sylvain Eminian
    Department of Diagnostic and Interventional Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.
  • Stacey Gidoin
    Department of Diagnostic and Interventional Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.
  • Alexandre Terrier
    Laboratory of Biomechanical Orthopedics, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne 1015, Switzerland.
  • Alain Farron
    Service of Orthopedics and Traumatology, Lausanne University Hospital, University of Lausanne, Lausanne 1011, Switzerland.
  • Philippe Büchler
    ARTORG Center for Biomedical Engineering Research, University of Bern, Freiburgstrasse 3, CH-3010, Bern, Switzerland. philippe.buechler@artorg.unibe.ch.

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