Artificial intelligence-aided CT segmentation for body composition analysis: a validation study.

Journal: European radiology experimental
Published Date: Mar 11, 2021

Abstract

BACKGROUND: Body composition is associated with survival outcome in oncological patients, but it is not routinely calculated. Manual segmentation of subcutaneous adipose tissue (SAT) and muscle is time-consuming and therefore limited to a single CT slice. Our goal was to develop an artificial-intelligence (AI)-based method for automated quantification of three-dimensional SAT and muscle volumes from CT images.

Authors

  • Pablo Borrelli
    Department of Clinical Physiology, Sahlgrenska University Hospital, Gothenburg, Sweden.
  • Reza Kaboteh
    Region Västra Götaland, Department of Clinical Physiology, Sahlgrenska University Hospital, Gothenburg, Sweden.
  • Olof Enqvist
    Department of Electrical Engineering, Region Västra Götaland, Chalmers University of Technology, Gothenburg, Sweden.
  • Johannes Ulén
    Eigenvision AB, Malmö, Sweden.
  • Elin Trägårdh
    Department of Clinical Physiology and Nuclear Medicine, Lund University and Skåne University Hospital, Malmö, Sweden.
  • Henrik Kjölhede
    Department of Urology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
  • Lars Edenbrandt
    Department of Molecular and Clinical Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.

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