Semiautomated Pipeline Effectively Assesses Severity and Monitor Disease Progression in Compressed Spinal Cord of...

Degenerative cervical myelopathy (DCM) is a progressive and disabling condition resulting from chronic compression of the spinal cord, leading to functional impairments that can severely affect quality of life. Traditional methods for assessing spinal cord injury and morphometrics rely on subjective visualization of contrast changes and manual segmentation, which are nonstandardized, time-consuming, and inconsistent across patients. This variability limits understanding of DCM pathology and hampers timely clinical intervention.

We introduce a semiautomated pipeline using the Spinal Cord Toolbox, an open-source platform that uses advanced algorithms, including optimization and computational efficiency algorithms, support vector machine, and convolutional neural networks, to streamline the assessment of spinal cord shape, microstructural changes, and gray and white matter integrity. By integrating spinal cord segmentation, anatomical labeling, and registration to a standardized template, the pipeline extracts normalized morphometric measures, providing efficient and reliable analysis of spinal cord pathology in DCM.

We extracted normalized spinal cord morphometrics, including cross-sectional area (CSA), anterior-posterior diameter, right-left diameter, eccentricity, solidity, gray matter CSA, white matter CSA, and regional and tract-based magnetization transfer ratio measures. Our analysis demonstrates that DCM patients exhibit significant reductions in these morphometrics compared with healthy controls, even in regions without visible compression. Furthermore, CSA reductions across the spinal cord highlight areas of severe compression, including at the intervertebral disks, which may not be apparent on standard imaging.

These quantitative measures give clinicians easily interpretable data on the extent of spinal cord injury, even in regions without obvious compression. This enables a comprehensive understanding of DCM pathophysiology. By eliminating the subjectivity of manual segmentation and accounting for intersubject and intrasubject variability, this approach supports consistent cross-subject comparisons and is poised to reshape how clinicians assess and manage DCM.