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

Abstract

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 (r_left = 0.79, p_left < .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.

Keywords: MR imaging; biomarker; muscle dystrophy; neuromuscular disorders; segmentation; thresholding.