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 the 3rd lumbar vertebrae from critically ill, liver cirrhosis, pancreatic cancer, and clear cell renal cell carcinoma patients, as well as renal and liver donors, were manually analyzed for body composition. Ninety percent of scans were used for developing and validating a neural network for the automated segmentation of skeletal muscle

SUBMITTER: Paris MT 

PROVIDER: S-EPMC7374050 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature