Multilabel segmentation and analysis of skeletal muscle and adipose tissue in routine abdominal CT scans.

Journal: Computers in biology and medicine
PMID: 39778239

Abstract

PURPOSE: This paper presents a deep learning-based multi-label segmentation network that extracts a total of three separate adipose tissues and five different muscle tissues in CT slices of the third lumbar vertebra and additionally improves the segmentation of the intermuscular fat.

Authors

  • Robert Kreher
    Department for Simulation and Graphics, University of Magdeburg, Magdeburg, Germany.
  • Georg Hille
    Department of Simulation and Graphics, Otto-von-Guericke University, Magdeburg, Germany. Electronic address: georg.hille@ovgu.de.
  • Bernhard Preim
    Department of Simulation and Graphics, Otto von Guericke University Magdeburg, Germany; Research Campus STIMULATE, Otto von Guericke University Magdeburg, Germany.
  • Mattes Hinnerichs
    Department of Radiology, University Hospital, Magdeburg, Germany.
  • Jan Borggrefe
    Institute for Diagnostic and Interventional Radiology, University Hospital Cologne, Cologne, Germany. jan.borggrefe@uk-koeln.de.
  • Alexey Surov
    Department of Diagnostic and Interventional Radiology, University of Leip-zig, Leipzig, Germany.
  • Sylvia Saalfeld
    Department of Simulation and Graphics, Otto von Guericke University Magdeburg, Germany; Research Campus STIMULATE, Otto von Guericke University Magdeburg, Germany.

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