# Spinal cord gray matter segmentation using deep dilated convolutions

arXiv: https://arxiv.org/abs/1710.01269 · HTML: https://mlsanity.com/paper/1710.01269/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Oct 2017.

## Built on by (most cited first, 5 of 5)

- [Scalable Certified Segmentation via Randomized Smoothing](https://mlsanity.com/paper/2107.00228/) (2021)
- [Probabilistic Robustness Analysis in High Dimensional Space: Application to Semantic Segmentation Network](https://mlsanity.com/paper/2509.11838/) (2025)
- [FedSemiDG: Domain Generalized Federated Semi-supervised Medical Image Segmentation](https://mlsanity.com/paper/2501.07378/) (2025)
- [On the Impact of Interpretability Methods in Active Image Augmentation Method](https://mlsanity.com/paper/2102.12354/) (2021)
- [2D Multi-Class Model for Gray and White Matter Segmentation of the Cervical Spinal Cord at 7T](https://mlsanity.com/paper/2110.06516/) (2021)

## Abstract (arXiv)

Gray matter (GM) tissue changes have been associated with a wide range of neurological disorders and was also recently found relevant as a biomarker for disability in amyotrophic lateral sclerosis. The ability to automatically segment the GM is, therefore, an important task for modern studies of the spinal cord. In this work, we devise a modern, simple and end-to-end fully automated human spinal cord gray matter segmentation method using Deep Learning, that works both on in vivo and ex vivo MRI acquisitions. We evaluate our method against six independently developed methods on a GM segmentation challenge and report state-of-the-art results in 8 out of 10 different evaluation metrics as well as major network parameter reduction when compared to the traditional medical imaging architectures such as U-Nets.