Semi‐automated volumetry of MRI serves as a biomarker in neuromuscular patients

Background Muscle MRI is of increasing importance for neuromuscular patients to detect changes in muscle volume, fat‐infiltration, and edema. We developed a method for semi‐automated segmentation of muscle MRI datasets. Methods An active contour‐evolution algorithm implemented within the ITK‐SNAP software was used to segment T1‐weighted MRI, and to quantify muscle volumes of neuromuscular patients (n = 65). Results Semi‐automated compared with manual segmentation was shown to be accurate and time‐efficient. Muscle volumes and ratios of thigh/lower leg volume were lower in myopathy patients than in controls (P < .0001; P < .05). We found a decrease of lower leg muscle volume in neuropathy patients compared with controls (P < .01), which correlated with clinical parameters. In myopathy patients, muscle volume showed a positive correlation with muscle strength (rleft = 0.79, pleft < .0001). Muscle volumes were independent of body mass index and age. Conclusions Our method allows for exact and time‐efficient quantification of muscle volumes with possible use as a biomarker in neuromuscular patients.