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

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.

Fully Automated Segmentation of Connective Tissue Compartments for CT-Based Body Composition Analysis: A Deep Learning Approach.

Objective: Body composition comprises prognostic information in patients with various malignancies and can be opportunistically determined from routine computed tomography (CT) scans. However, accurate assessment of patients with alterations, for example, due to ascites or anasarca, and accurate identification of intermuscular fat remain challenging. In this study, we aimed to develop a fully automated and highly accurate segmentation tool for connective tissue compartments from abdominal CT scans using the open-source Convolutional Neural Network (CNN) DeepMedic.