Automatic Cervical Cross-Sectional Area Measurement in 3T MR Images: Case of Healthy Human Spinal Cord

Precise measurement of Spinal Cord Cross-Sectional Area (SC-CSA) is crucial for monitoring SC health state and diagnosing neurodegenerative diseases such as Multiple Sclerosis (MS), Devic Neuromyelitis Optica (DNMO), and Amyotrophic Lateral Sclerosis (ALS) progression. However, accurate measurement of SC-CSA is challenging due to many factors such as SC curvature, patient positioning, segmentation methods, and the limitations of Magnetic Resonance Imaging (MRI) resolution. Traditional methods that rely on vertebral levels as reference suffer from intersubject variability and limited reproducibility. In this study, we propose a novel landmarks-based method for automated SC-CSA measurement. The proposed approach, relying on consecutive spinal nerves’ midpoints as anatomical reference, offers more precise spinal segment delimitation and consistent SC-CSA measurements. We validated the effectiveness and robustness of our method using two public datasets comprising MR Images of healthy individuals covering all cervical levels and for three different head positions. Experimental results demonstrated that the proposed SC-CSA measurement method could be considered as a useful tool for potential clinical applications