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

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.