Automated body composition analysis of clinically acquired computed tomography scans using neural networks.

Journal: Clinical nutrition (Edinburgh, Scotland)
Published Date: Oct 1, 2020

Abstract

BACKGROUND & AIMS: The quantity and quality of skeletal muscle and adipose tissue is an important prognostic factor for clinical outcomes across several illnesses. Clinically acquired computed tomography (CT) scans are commonly used for quantification of body composition, but manual analysis is laborious and costly. The primary aim of this study was to develop an automated body composition analysis framework using CT scans.

Authors

  • Michael T Paris
    Department of Kinesiology, University of Waterloo, Waterloo, Ontario, Canada, m2paris@uwaterloo.ca.
  • Puneeta Tandon
    Department of Gastroenterology, University of Alberta, Edmonton, AB, Canada.
  • Daren K Heyland
    Department of Critical Care, Kingston General Hospital, Kingston, ON, Canada; Clinical Evaluation Research Unit, Queens University, Kingston, ON, Canada.
  • Helena Furberg
    Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
  • Tahira Premji
    Department of Kinesiology, University of Waterloo, Waterloo, ON, Canada.
  • Gavin Low
    Department of Radiology, University of Alberta, Edmonton, AB, Canada.
  • Marina Mourtzakis
    Department of Kinesiology, University of Waterloo, Waterloo, ON, Canada. Electronic address: mmourtzakis@uwaterloo.ca.

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