Semi‐automated volumetry of MRI serves as a biomarker in neuromuscular patients. Issue 5 (17th February 2020)

• Record Type: Journal Article
• Authors: Müller, Madlaine; Dohrn, Maike F.; Romanzetti, Sandro; Gadermayr, Michael; Reetz, Kathrin; Krämer, Nils A.; Kuhl, Christiane; Schulz, Jörg B.; Gess, Burkhard
• Is Part Of: Muscle & nerve. Volume 61:Issue 5(2020)
• Page Start: 600
• Page End: 607
• Publication Date: 2020-02-17
• Subjects: biomarker -- MR imaging -- muscle dystrophy -- neuromuscular disorders -- segmentation -- thresholding; Neuromuscular diseases -- Periodicals; Muscles -- Periodicals; Nerves -- Periodicals; 616.74
• DOI: 10.1002/mus.26827
• Languages: English
• ISSNs: 0148-639X

Abstract

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 (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.