Spinal cord gray matter segmentation using deep dilated convolutions

This repository contains the source-code for the paper "Spinal cord gray matter segmentation using deep dilated convolutions", available as pre-print on ArXiv.

Note: this repository is made for researchers in deep learning. If you just would like to use the method on your data, this method has been implemented in the Spinal Cord Toolbox (SCT), where you can find pre-trained models on much larger datasets and a user-friendly command-line tool called sct_deepseg_gm.

You can see the MRI ex-vivo segmentation video. Another manuscript is under review for the MRI ex-vivo data.

Requirements Installation

To use this repository, you'll need to install the following requirements:

• Clone the repository
• Install Python requirements with pip install -r pip-requirements.txt
• Open the Jupyter Notebook located at notebooks folder

Notebooks

This repository contains two notebooks:

• TensorFlow model
• Pytorch model (experimental)

Remarks

Some remarks regarding the model:

• This model was trained on a common space with a voxel size of 0.25mm x 0.25mm, so you'll have to resample your data to this space if you want good results;
• This repository contains the model trained on the GM Challenge Dataset (both train and validation), the model is located on the directory called models together with a json file containing the mean/std that was used to standardize the training data;
• For the training procedure, please see the original paper for more information;

Citation

If you use this work in your research, please cite:

@article{arxiv1710.01269,
  author = {Christian S. Perone, Evan Calabrese, Julien Cohen-Adad},
  title = {Spinal cord gray matter segmentation using deep dilated convolutions},
  journal = {arXiv preprint arXiv:1710.01269},
  year = {2017}
}

License

MIT License

Copyright (c) 2017 NeuroPoly

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