Automatic Segmentation of Muscle Tissue and Inter-muscular Fat in Thigh and Calf MRI Images
This work addresses the need for efficient and accurate quantification of fat infiltration in muscular dystrophies, which is currently done visually by experts, representing an incremental improvement over existing algorithmic solutions.
The paper tackled the problem of automatically segmenting muscle tissue and inter-muscular fat in thigh and calf MRI images to quantify fat infiltration in muscular dystrophies, achieving high Dice Similarity Coefficients of 0.964, 0.933, and 0.917 for muscle-region, inter-muscular adipose tissue, and healthy muscle segmentation, respectively.
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.