Deep learning-based thigh muscle segmentation for reproducible fat fraction quantification using fat-water decomposition MRI

Abstract

Time-efficient thigh muscle segmentation is a major challenge in moving from primarily qualitative assessment of thigh muscle MRI in clinical practice, to potentially more accurate and quantitative methods. In this work, we trained a convolutional neural network to automatically segment four clinically relevant muscle groups using fat-water MRI. Compared to cumbersome manual annotation which ordinarily takes at least 5-6 hours, this fully automated method provided sufficiently accurate segmentation within several seconds for each thigh volume. More importantly, it yielded more reproducible fat fraction estimations, which is extremely useful for quantifying fat infiltration in ageing and in diseases like neuromuscular disorders.