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Abstract #1625

Minimum sample size to detect spinal cord atrophy with automatic soft segmentation

Sandrine Bédard1, Enamundram Naga Karthik1,2, Jan Valošek1,2,3,4, and Julien Cohen-Adad1,2,5,6
1NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montréal, Montréal, QC, Canada, 2Mila - Quebec AI Institute, Montréal, 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, 5Functional Neuroimaging Unit, CRIUGM, Université de Montréal, Montréal, QC, Canada, 6Centre de Recherche du CHU Sainte-Justine, Université de Montréal, Montréal, QC, Canada

Synopsis

Keywords: Spinal Cord, Spinal Cord

Motivation: Spinal cord atrophy, a key biomarker in neurodegenerative diseases like multiple sclerosis, is challenging to measure reliably across varying MRI contrasts and protocols in multi-center studies.

Goal(s): To evaluate a novel, contrast-agnostic “soft” segmentation method’s sensitivity for atrophy detection by calculating spinal cord cross-sectional area (CSA) at specific vertebral levels.

Approach: We applied rescaling and transformations on MRI scans to simulate atrophy and used our segmentation model to calculate CSA, comparing results with previous methods through a scan-rescan experiment.

Results: The contrast-agnostic model, significantly reduced sample sizes needed for detecting spinal cord atrophy, especially with soft segmentations.

Impact: Reducing the required sample size will allow for early spinal cord atrophy detection, especially in multi-center and multi-contrast studies.

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