Spinal cord and gray matter multiclass segmentation at 7T - T2Star

As spinal cord images acquired at 7T usually present with higher spatial resolution, new details, different contrasts and artifacts, the model was trained to refine white matter /gray matter segmentation.

The model was trained in the Center for Magnetic Resonance in Biology and Medicine (CRMBM-CEMEREM, UMR 7339, CNRS, Aix-Marseille University - France, using ivadomed. It relies on high-resolution(0.175 x 0.175 mm2) 7T T2*-weighted images from CRMBM-CEMEREM acquired on healthy and pathological subjects (multiple sclerosis, amyotrophic lateral sclerosis).

The convolutional neural network segments the spinal cord (SC) and the gray matter (GM) using 2D U-Net and this model was implemented in SCT as seg_gm_sc_7t_t2star tool.

Installation:

sct_deepseg -install-task seg_gm_sc_7t_t2star

Launch segmentation:

sct_deepseg -i NIFTI_IMAGE -task seg_gm_sc_7t_t2star

Citation:

If you find this useful, please cite our work as follows:

@misc{medina20212d,
      title={2D Multi-Class Model for Gray and White Matter Segmentation of the Cervical Spinal Cord at 7T}, 
      author={Nilser J. Laines Medina and Charley Gros and Julien Cohen-Adad and Virginie Callot and Arnaud Le Troter},
      year={2021},
      eprint={2110.06516},
      archivePrefix={arXiv},
      primaryClass={eess.IV}
}

N. J. Laines Medina, C. Gros, J. Cohen-Adad, V. Callot, and A. Le Troter, “2D Multi-Class Model for Gray and White Matter Segmentation of the Cervical Spinal Cord at 7T,” Oct. 2021. [Online]. Available: http://arxiv.org/abs/2110.06516

Acknowledgments

M. Seif and J. Vannesjö from Balgrist University Hospital (Zürich, Switzerland) and A. Seifert from the Biomedical Engineering and Imaging Institute (Mount Sinai, NY, USA) for providing external 7T SC MR dataset