8/ The method is open-source and available as part of the Spinal Cord Toolbox (SCT)🚀
Have questions or feedback? Let us know! We’d love to hear how EPISeg can support your research.

7/ This work wouldn’t have been possible without the amazing contributions from multi-site collaborators, open-source community, @PolyNeuro , @mila-quebec.bsky.social and @StanfordPain . We hope EPISeg helps research paving the way for new insights into spinal cord function and dysfunction.

6/ What sets EPISeg apart?
• Trained via active learning, improving robustness over multiple iterations.
• Works across different scanner protocols and resolutions.
• Handles clinical cases, including data from patients with myelopathy and fibromyalgia.

5/ To build EPISeg, we created an open-access dataset of SC fMRI along with the segmentations from 15 sites, covering 406 participants with varying scanner setups and conditions.

4/ Our solution: EPISeg uses a deep learning model trained on diverse, multi-center dataset of gradient-echo EPI images to perform fully automated SC segmentation. It’s fast, accurate, and robust to:
• Low-resolution images
• Distortions
• Signal drop-outs
• Motion artifacts

3/ Until now, spinal cord (SC) segmentation on EPI data required time-consuming manual corrections prone to user bias and errors.

2/ Spinal cord fMRI is critical for studying sensation, movement, and autonomic function. However, preprocessing SC fMRI data like segmenting the spinal cord is challenging due to low spatial resolution, susceptibility artifacts, motion and ghosting artifacts and poor SC contrast.

6/ What sets EPISeg apart?
• Trained via active learning, improving robustness over multiple iterations.
• Works across different scanner protocols and resolutions.
• Handles clinical cases, including data from patients with myelopathy and fibromyalgia.

5/ To build EPISeg, we created an open-access dataset of SC fMRI along with the segmentations from 15 sites, covering 406 participants with varying scanner setups and conditions.

4/ Our solution: EPISeg uses a deep learning model trained on diverse, multi-center dataset of gradient-echo EPI images to perform fully automated SC segmentation. It’s fast, accurate, and robust to:
• Low-resolution images
• Distortions
• Signal drop-outs
• Motion artifacts

3/ Until now, spinal cord (SC) segmentation on EPI data required time-consuming manual corrections prone to user bias and errors.

2/ Spinal cord fMRI is critical for studying sensation, movement, and autonomic function. However, preprocessing SC fMRI data like segmenting the spinal cord is challenging due to low spatial resolution, susceptibility artifacts, motion and ghosting artifacts and poor SC contrast

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