  Magnetic resonance imaging (MRI) of thigh and calf muscles is one of the most
effective techniques for estimating fat infiltration into muscular dystrophies.
The infiltration of adipose tissue into the diseased muscle region varies in
its severity across, and within, patients. In order to efficiently quantify the
infiltration of fat, accurate segmentation of muscle and fat is needed. An
estimation of the amount of infiltrated fat is typically done visually by
experts. Several algorithmic solutions have been proposed for automatic
segmentation. While these methods may work well in mild cases, they struggle in
moderate and severe cases due to the high variability in the intensity of
infiltration, and the tissue's heterogeneous nature. To address these
challenges, we propose a deep-learning approach, producing robust results with
high Dice Similarity Coefficient (DSC) of 0.964, 0.917 and 0.933 for
muscle-region, healthy muscle and inter-muscular adipose tissue (IMAT)
segmentation, respectively.
