Keywords: Machine Learning/Artificial Intelligence, SegmentationSeveral methods to segment the spinal cord have emerged over the past decade. However, they are dependent on the image contrast, resulting in differences of spinal cord cross-sectional area (CSA), a relevant biomarker in neurodegenerative diseases. We propose a novel method using deep learning that produces the same segmentation regardless of the MRI contrast. Moreover, the segmentation is “soft” (non-binary) and can therefore encode partial volume information. CSA computed with this contrast-agnostic soft segmentation method has lower intra- and inter-subject variability, making it particularly relevant for multi-center studies.
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