A Deep Network Ensemble for Segmentation of Cervical Spinal Cord and Neural Foramina

The automated interpretation of spinal imaging using machine learning has emerged as a promising method for standardizing the assessment and diagnosis of numerous spinal column pathologies. While magnetic resonance images (MRIs) of the lumbar spine have been extensively studied in this context, the cervical spine remains vastly understudied. Our objective was to develop a method for automatically delineating cervical spinal cord and neural foramina on axial MRIs using machine learning. In this study, we train a state-of-the-art algorithm, namely a multi-resolution ensemble of deep U-Nets, to delineate cervical spinal cord and neural foramina on 50 axial T2-weighted MRI-series segmented by a team of expert clinicians. We then evaluate algorithm performance against two independent human raters using 50 separate MRI-series. Dice coefficients, Hausdorff coefficients, and average surface distances (ASDs) were computed for this final set between the algorithm and each rater, and between raters, in order to evaluate algorithm performance for each segmentation task. The resulting cervical cord Dice coefficients were 0.76 (auto vs human, average) and 0.87 (human vs human), and the cervical foramina Dice coefficients were 0.57 (auto vs human, average) and 0.59 (human vs human). Hausdorff coefficients and ASDs reflected similar results. We conclude that the algorithm achieved a higher degree of consistency with human raters for cervical cord than for cervical foramina, and that cervical foramina are challenging to segment accurately for both humans and machine. Further technical development in machine learning is necessary to accurately segment the highly anatomically variable neural foramina of the human spine.