Automatic spinal cord segmentation: Generalization across MR parameters, sites, vendors and pathologies

Abstract

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.