Deep-learning-based Segmentation of Skeletal Muscle Mass in Routine Abdominal CT Scans.

Journal: In vivo (Athens, Greece)
PMID: 35738592

Abstract

BACKGROUND: For prediction of many types of clinical outcome, the skeletal muscle mass can be used as an independent biomarker. Manual segmentation of the skeletal muscles is time-consuming, therefore we present a deeplearning-based approach for the identification of muscle mass at the L3 level in clinical routine computed tomographic (CT) data.

Authors

  • Robert Kreher
    Department for Simulation and Graphics, University of Magdeburg, Magdeburg, Germany.
  • Mattes Hinnerichs
    Department of Radiology, University Hospital, Magdeburg, Germany.
  • Bernhard Preim
    Department of Simulation and Graphics, Otto von Guericke University Magdeburg, Germany; Research Campus STIMULATE, Otto von Guericke University Magdeburg, Germany.
  • Sylvia Saalfeld
    Department of Simulation and Graphics, Otto von Guericke University Magdeburg, Germany; Research Campus STIMULATE, Otto von Guericke University Magdeburg, Germany.
  • Alexey Surov
    Department of Diagnostic and Interventional Radiology, University of Leip-zig, Leipzig, Germany.

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