A comprehensive study on automated muscle segmentation for assessing fat infiltration in neuromuscular diseases.

Severity and progression of degenerative neuromuscular diseases can be sensitively captured by evaluating the fat infiltration of muscle tissue in T1-weighted MRI scans of human limbs. For computing the fat fraction, the original muscle needs to be first separated from other tissue. Five conceptionally different approaches were investigated and evaluated with respect to the segmentation of muscles of human thighs. Besides a rather basic thresholding approach, local (level set) as well as global (graph cut) energy-minimizing segmentation approaches with and without a shape prior energy term were examined. For experimental evaluations, a dataset containing 37 subjects was divided into four classes according to the degree of fat infiltration. Results show that the choice of the best method depends on the severity of fat infiltration. In severe cases, the best results were obtained with shape prior based graph cuts, whereas in marginal cases thresholding was sufficient. With the best approach, the worst-case error in fat fraction computation was always below 11% and on average between 2% for tissue showing no fat infiltrations and 6% for heavily infiltrated tissue. The obtained Dice similarity coefficients, measuring the segmentation quality, were on average between 0.85 and 0.92. Although segmentation of heavily infiltrated muscle tissue is extremely difficult, an approach for reasonably segmenting these image data was identified. Especially the negative impact on the calculated fat fraction can be reduced significantly.