This repository contains the code for deep learning-based segmentation of the spinal nerve rootlets. The code is based on the nnUNet framework.
If you find this work and/or code useful for your research, please refer to the following preprint:
@misc{valosek2024automatic,
title={Automatic Segmentation of the Spinal Cord Nerve Rootlets},
author={Jan Valosek and Theo Mathieu and Raphaelle Schlienger and Olivia S. Kowalczyk and Julien Cohen-Adad},
year={2024},
eprint={2402.00724},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
The model was trained on T2-weighted images and provides semantic (i.e., level-specific) segmentation of the dorsal spinal nerve rootlets.
- Spinal Cord Toolbox (SCT) v6.2 or higher -- follow the installation instructions here
- conda
- Python
Once the dependencies are installed, download the latest rootlets model:
sct_deepseg -install-task seg_spinal_rootlets_t2w
To segment a single image, run the following command:
sct_deepseg -i <INPUT> -o <OUTPUT> -task seg_spinal_rootlets_t2w
For example:
sct_deepseg -i sub-001_T2w.nii.gz -o sub-001_T2w_label-rootlets_dseg.nii.gz -task seg_spinal_rootlets_t2w