Journal article
Automated body composition analysis of clinically acquired computed tomography scans using neural networks
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.
METHODS: CT scans of …
Authors
Paris MT; Tandon P; Heyland DK; Furberg H; Premji T; Low G; Mourtzakis M
Journal
Clinical Nutrition, Vol. 39, No. 10, pp. 3049–3055
Publisher
Elsevier
Publication Date
10 2020
DOI
10.1016/j.clnu.2020.01.008
ISSN
0261-5614
Fields of Research (FoR)
Medical Subject Headings (MeSH)
Adipose TissueAdiposityAdultAgedAutomationBody CompositionDeep LearningEuropeFemaleHumansLumbar VertebraeMaleMiddle AgedMuscle, SkeletalNeural Networks, ComputerNorth AmericaPredictive Value of TestsRadiographic Image Interpretation, Computer-AssistedReproducibility of ResultsSarcopeniaTomography, X-Ray Computed