Abstract
The objective of this study was to automate the discrimination and quantification of human abdominal body fat compartments into subcutaneous adipose tissue (SAT) and visceral adipose tissue (VAT) from T1-weighted MRI using encoder-decoder convolutional neural networks (CNN) and to apply the algorithm to a diseased patient sample, i.e., patients with amyotrophic lateral sclerosis (ALS). One-hundred-and-fifty-five participants (74 patients with ALS and 81 healthy controls) were split in training (50%), validation (6%), and test (44%) data. SAT and VAT volumes were determined by a novel automated CNN-based algorithm of U-Net like architecture in comparison with an established protocol with semi-automatic assessment as the reference. The dice coefficients between the CNN predicted masks and the reference segmentation were 0.87 ± 0.04 for SAT and 0.64 ± 0.17 for VAT in the control group and 0.87 ± 0.08 for SAT and 0.68 ± 0.15 for VAT in the ALS group. The significantly increased VAT/SAT ratio in the ALS group in comparison to controls confirmed the previous results. In summary, the CNN approach using CNN of U-Net architecture for automated segmentation of abdominal adipose tissue substantially facilitates data processing and offers the opportunity to automatically discriminate abdominal SAT and VAT compartments. Within the research field of neurodegenerative disorders with body composition alterations like ALS, the unbiased analysis of body fat components might pave the way for these parameters as a potential biological marker or a secondary read-out for clinical trials.