Automatic Segmentation of the Spinal Cord and Spinal Canal Coupled With Vertebral Labeling.

Quantifying spinal cord (SC) atrophy in neurodegenerative and traumatic diseases brings important diagnosis and prognosis information for the clinician. We recently developed the PropSeg method, which allows for fast, accurate and automatic segmentation of the SC on different types of MRI contrast (e.g., T1-, T2- and T2(∗) -weighted sequences) and any field of view. However, comparing measurements from the SC between subjects is hindered by the lack of a generic coordinate system for the SC. In this paper, we present a new framework combining PropSeg and a vertebral level identification method, thereby enabling direct inter- and intra-subject comparison of SC measurements for large cohort studies as well as for longitudinal studies. Our segmentation method is based on the multi-resolution propagation of tubular deformable models. Coupled with an automatic intervertebral disk identification method, our segmentation pipeline provides quantitative metrics of the SC and spinal canal such as cross-sectional areas and volumes in a generic coordinate system based on vertebral levels. This framework was validated on 17 healthy subjects and on one patient with SC injury against manual segmentation. Results have been compared with an existing active surface method and show high local and global accuracy for both SC and spinal canal (Dice coefficients =0.91 ± 0.02) segmentation. Having a robust and automatic framework for SC segmentation and vertebral-based normalization opens the door to bias-free measurement of SC atrophy in large cohorts.