Diabetes mellitus, muscular dystrophies, and other pathologies are characterized by metabolic impairment that can lead to lower extremity muscle degeneration. While MRI provides access to quantitative biomarkers to characterize muscle quality, analysis requires time-consuming manual image segmentation. To address this problem, we developed an automated segmentation algorithm based on a convolutional neural network that provided high dice similarity coefficient scores (>0.92) in the gastrocnemius medial, gastrocnemius lateral, and soleus muscles. We utilized the automatic segmentations to show volumetric fat fraction was elevated in individuals with diabetic peripheral neuropathy compared to controls in the soleus and gastrocnemius medial muscles (P<0.05).
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