Automatic Segmentation of the Spinal Cord Nerve Rootlets
Jan Valosek Theo Mathieu Raphaelle Schlienger Olivia S. Kowalczyk Julien Cohen-Adad
Precise identification of spinal nerve rootlets is relevant to delineate
spinal levels for the study of functional activity in the spinal cord. The goal
of this study was to develop an automatic method for the semantic segmentation
of spinal nerve rootlets from T2-weighted magnetic resonance imaging (MRI)
scans. Images from two open-access MRI datasets were used to train a 3D
multi-class convolutional neural network using an active learning approach to
segment C2-C8 dorsal nerve rootlets. Each output class corresponds to a spinal
level. The method was tested on 3T T2-weighted images from datasets unseen
during training to assess inter-site, inter-session, and inter-resolution
variability. The test Dice score was 0.67 +- 0.16 (mean +- standard deviation
across rootlets levels), suggesting a good performance. The method also
demonstrated low inter-vendor and inter-site variability (coefficient of
variation <= 1.41 %), as well as low inter-session variability (coefficient of
variation <= 1.30 %) indicating stable predictions across different MRI
vendors, sites, and sessions. The proposed methodology is open-source and
readily available in the Spinal Cord Toolbox (SCT) v6.2 and higher.