End-to-end automated body composition analyses with integrated quality control for opportunistic assessment of sarcopenia in CT.

ABSTRACT:

Objectives

To develop a pipeline for automated body composition analysis and skeletal muscle assessment with integrated quality control for large-scale application in opportunistic imaging.

Methods

First, a convolutional neural network for extraction of a single slice at the L3/L4 lumbar level was developed on CT scans of 240 patients applying the nnU-Net framework. Second, a 2D competitive dense fully convolutional U-Net for segmentation of visceral and subcutaneous adipose tissue (VAT, SAT), skeletal muscle (SM), and subsequent determination of fatty muscle fraction (FMF) was developed on single CT slices of 1143 patients. For both steps, automated quality control was integrated by a logistic regression model classifying the presence of L3/L4 and a linear regression model

SUBMITTER: Nowak S 

PROVIDER: S-EPMC9038788 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature