Abstract #0306

Automatic segmentation of T2-weighted hyperintense lesions in spinal cord injury

Jan Valosek^1,2,3,4, Naga Karthik Enamundram^1,2, Maxime Bouthillier^1,5, Simon Schading-Sassenhausen⁶, Lynn Farner⁶, Dario Pfyffer^6,7, Andrew C. Smith⁸, Kenneth A. Weber II⁷, Patrick Freund^6,9, and Julien Cohen-Adad^1,2,10,11

¹NeuroPoly Lab, Institute of Biomedical Engineering, 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, ⁵Centre Hospitalier de l’Université de Montréal, University of Montreal, Montreal, QC, Canada, ⁶Spinal Cord Injury Center, Balgrist University Hospital, University of Zürich, Zürich, Switzerland, ⁷Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Standford, CA, United States, ⁸Department of Physical Medicine and Rehabilitation Physical Therapy Program, University of Colorado School of Medicine, Aurora, CO, United States, ⁹Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany, ¹⁰Functional Neuroimaging Unit, CRIUGM, Université de Montréal, Montreal, QC, Canada, ¹¹Centre de Recherche du CHU Sainte-Justine, Université de Montréal, Montreal, QC, Canada

Synopsis

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

Motivation: Morphometric analysis of the intramedullary lesion following spinal cord injury will assist in understanding the extent of the injury and choosing the best therapeutic strategy for rehabilitation.

Goal(s): Our objective was to develop a deep learning-based tool for the segmentation of T2-weighted hyperintense spinal cord injury lesions.

Approach: A nnUNet model was trained to segment both the spinal cord and lesions from two different datasets.

Results: Compared to existing methods, our model achieved the best segmentation performance for both cord and lesions. The code/model is available on GitHub and will soon be part of the Spinal Cord Toolbox.

Impact: Automatic segmentation of spinal cord injury lesions replaces the tedious process of manual annotation and enables the extraction of relevant lesion morphometrics in large cohorts. The proposed model generalizes across lesion etiologies (traumatic/ischemic), scanner manufacturers and heterogeneous image resolutions.

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