Abstract #1026
Automatic spinal cord segmentation: Generalization across MR parameters, sites, vendors and pathologies
Sandrine Bedard1, Naga Karthik Enamundram1,2, Merve Kaptan3,4, Falk Eippert3, Nawal Kinany5,6, Ilaria Ricchi5,6, Dimitri Van De Ville5,6, Patrick Freund7,8, Markus Hupp7, Lisa Eunyoung Lee9,10, Anthony Traboulsee11, Roger Tam12, Alexandre Prat13,14, Zachary Vavasour12, Shannon Kolind11, Jiwon Oh9,10, Christoph S. Aigner15, and Julien Cohen-Adad1,2,16,17
1NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montréal, Montréal, QC, Canada, 2Mila - Quebec AI Institute, Montréal, QC, Canada, 3Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany, 4Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, United States, 5Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland, 6Neuro-X Institute, École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland, 7Spinal Cord Injury Center, Balgrist University Hospital, University of Zürich, Zürich, Switzerland, 8Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany, 9Department of Medicine (Neurology), University of Toronto, Toronto, ON, Canada, 10BARLO Multiple Sclerosis Centre & Keenan Research Centre, St. Michael's Hospital, Toronto, ON, Canada, 11University of British Columbia, Vancouver, BC, Canada, 12School of Biomedical Engineering, Faculties of Applied Science and Medicine, University of British Columbia, Vancouver, BC, Canada, 13Department of neuroscience, Université de Montréal, Montréal, QC, Canada, 14Neuroimmunology research laboratory, University of Montreal Hospital Research Centre (CRCHUM), Montréal, QC, Canada, 15Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany, 16Functional Neuroimaging Unit, CRIUGM, Université de Montréal, Montréal, QC, Canada, 17Centre de Recherche du CHU Sainte-Justine, Université de Montréal, Montréal, QC, Canada
Synopsis
Keywords: Segmentation, Spinal Cord, Segmentation; Deep Learning; Morphometrics; Atrophy; Variability; Reproducibility; Vendors
Motivation: Spinal cord cross-sectional area (CSA) is an important biomarker for neurodegenerative and traumatic diseases. However, CSA measurements vary across MRI contrasts and imaging protocols, limiting its use in multi-center studies.
Goal(s): The goal is to evaluate CSA variability using a novel contrast-agnostic segmentation method.
Approach: We compared this method to the Spinal Cord Toolbox's DeepSeg, analyzing CSA across different sites, and MRI vendors. Additionally, we compared the segmentations in diverse datasets and pathologies.
Results: The contrast-agnostic segmentation showed lower CSA variability, and superior performance in most cases, except for intramedullary cord compression, where the Spinal Cord Toolbox's DeepSeg was more accurate.
Impact: The contrast-agnostic method yields reliable spinal cord CSA measurements, independent of MRI contrasts and vendors. This, combined with a soft segmentation output, can potentially detect subtle spinal cord atrophy in prospective multi-center cohorts.
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