# Automatic Segmentation of the Spinal Cord Nerve Rootlets

arXiv: https://arxiv.org/abs/2402.00724 · HTML: https://mlsanity.com/paper/2402.00724/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 46 references that are in the map.
- First posted on arXiv 1 Feb 2024.

## Builds on (most cited first, 5 of 5)

- [A Survey on Active Learning and Human-in-the-Loop Deep Learning for Medical Image Analysis](https://mlsanity.com/paper/1910.02923/) (2019)
- [Automatic segmentation of the spinal cord and intramedullary multiple sclerosis lesions with convolutional neural networks](https://mlsanity.com/paper/1805.06349/) (2018)
- [Stacked Hourglass Network with a Multi-level Attention Mechanism: Where to Look for Intervertebral Disc Labeling](https://mlsanity.com/paper/2108.06554/) (2021)
- [Spine intervertebral disc labeling using a fully convolutional redundant counting model](https://mlsanity.com/paper/2003.04387/) (2020)
- [HCA-Net: Hierarchical Context Attention Network for Intervertebral Disc Semantic Labeling](https://mlsanity.com/paper/2311.12486/) (2023)

## Built on by (most cited first, 1 of 1)

- [Rootlets-based registration to the spinal cord PAM50 template](https://mlsanity.com/paper/2505.00115/) (2025)

## Abstract (arXiv)

Precise identification of spinal nerve rootlets is relevant to delineate spinal levels for the study of functional activity in the spinal cord. The goal of this study was to develop an automatic method for the semantic segmentation of spinal nerve rootlets from T2-weighted magnetic resonance imaging (MRI) scans. Images from two open-access MRI datasets were used to train a 3D multi-class convolutional neural network using an active learning approach to segment C2-C8 dorsal nerve rootlets. Each output class corresponds to a spinal level. The method was tested on 3T T2-weighted images from datasets unseen during training to assess inter-site, inter-session, and inter-resolution variability. The test Dice score was 0.67 +- 0.16 (mean +- standard deviation across testing images and rootlets levels), suggesting a good performance. The method also demonstrated low inter-vendor and inter-site variability (coefficient of variation <= 1.41 %), as well as low inter-session variability (coefficient of variation <= 1.30 %) indicating stable predictions across different MRI vendors, sites, and sessions. The proposed methodology is open-source and readily available in the Spinal Cord Toolbox (SCT) v6.2 and higher.