Abstract #2099

Automatic segmentation of spinal cord nerve rootlets

Jan Valosek^1,2,3,4, Theo Mathieu¹, Raphaëlle Schlienger⁵, Olivia Kowalczyk^6,7, and Julien Cohen-Adad^1,2,8,9

¹NeuroPoly Lab, Polytechnique Montreal, Montreal, QC, Canada, ²Mila - Quebec AI Institute, Montreal, QC, Canada, ³Department of Neurosurgery, Faculty of Medicine and Dentistry, Palacký University Olomouc, Olomouc, Czech Republic, ⁴Department of Neurology, Faculty of Medicine and Dentistry, Palacký University Olomouc, Olomouc, Czech Republic, ⁵Laboratoire de Neurosciences Cognitives (UMR 7291), CNRS – Aix Marseille Université, Marseille, France, ⁶Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, United Kingdom, ⁷Wellcome Centre for Human Neuroimaging, University College London, London, United Kingdom, ⁸Functional Neuroimaging Unit, CRIUGM, Université de Montréal, Montreal, QC, Canada, ⁹Centre de Recherche du CHU Sainte-Justine, Université de Montréal, Montreal, QC, Canada

Synopsis

Keywords: Analysis/Processing, Spinal Cord, Deep Learning; Nerve Rootlets; Segmentation

Motivation: Precise identification of spinal nerve rootlets is relevant for studying functional activity in the spinal cord.

Goal(s): Our goal was to develop a deep learning-based tool for the automatic segmentation of spinal nerve rootlets from multi-site T2-w images coupled with a method for the automatic identification of spinal levels.

Approach: Active learning was employed to iteratively train a nnUNet model to perform multi-class spinal nerve rootlets segmentation.

Results: The code/model is available on GitHub and is currently being validated by several laboratories worldwide.

Impact: Currently, most spinal cord fMRI studies use vertebral levels for groupwise registration, which is inaccurate. This new tool enables researchers to identify spinal levels via the automatic segmentation of nerve rootlets, improving fMRI analysis pipeline accuracy.

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