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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 Valenzuela1,2, Mauricio Reyes1, and Olivier Scheidegger2,3

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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