Magnetic resonance neurography (MRN) is increasingly used to diagnose peripheral neuropathy. Here, we propose a semi-automatic multimodal machine learning-based segmentation algorithm to segment peripheral nerves from MRN images. Our algorithm was tested on 9 volunteers and 25 patient cases suffering from sciatic neuropathy. Compared to manual segmentation, Dice coefficients were 0.723 ± 0.202 and 0.443 ± 0.228, respectively, with segmentation times of 5 ± 1 for semi-automatic, and 24 ± 8 minutes for manual segmentation. Our preliminary results suggest that machine learning-based segmentation of the sciatic nerve is possible in healthy and diseased nerves in clinically feasible time.