Abstract #5391

Semi-automatic, Machine Learning Segmentation of Peripheral Nerves in Healthy Volunteers and Patients

Fabian Balsiger¹, Mirjam Arn², Carolin Steindel², Benedikt Wagner², Marwan El-Koussy², Waldo Valenzuela^1,2, Mauricio Reyes¹, and Olivier Scheidegger^2,3

¹Institute for Surgical Technology and Biomechanics, University of Bern, Bern, Switzerland, ²Support Center for Advanced Neuroimaging (SCAN), Institute for Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland, ³Department of Neurology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland

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.

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