# Automatic segmentation of the spinal cord and intramedullary multiple sclerosis lesions with convolutional neural networks

arXiv: https://arxiv.org/abs/1805.06349 · HTML: https://mlsanity.com/paper/1805.06349/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 May 2018.

## Built on by (most cited first, 14 of 14)

- [Label fusion and training methods for reliable representation of inter-rater uncertainty](https://mlsanity.com/paper/2202.07550/) (2022)
- [Applications of Deep Learning Techniques for Automated Multiple Sclerosis Detection Using Magnetic Resonance Imaging: A Review](https://mlsanity.com/paper/2105.04881/) (2021)
- [Impact of individual rater style on deep learning uncertainty in medical imaging segmentation](https://mlsanity.com/paper/2105.02197/) (2021)
- [Towards contrast-agnostic soft segmentation of the spinal cord](https://mlsanity.com/paper/2310.15402/) (2023)
- [RootPainter3D: Interactive-machine-learning enables rapid and accurate contouring for radiotherapy](https://mlsanity.com/paper/2106.11942/) (2021)
- [Team NeuroPoly: Description of the Pipelines for the MICCAI 2021 MS New Lesions Segmentation Challenge](https://mlsanity.com/paper/2109.05409/) (2021)
- [Automatic Segmentation of the Spinal Cord Nerve Rootlets](https://mlsanity.com/paper/2402.00724/) (2024)
- [SCIsegV2: A Universal Tool for Segmentation of Intramedullary Lesions in Spinal Cord Injury](https://mlsanity.com/paper/2407.17265/) (2024)
- [2D Multi-Class Model for Gray and White Matter Segmentation of the Cervical Spinal Cord at 7T](https://mlsanity.com/paper/2110.06516/) (2021)
- [Harmonizing output imbalance for defect segmentation on extremely-imbalanced photovoltaic module cells images](https://mlsanity.com/paper/2211.05295/) (2022)
- [Localise to segment: crop to improve organ at risk segmentation accuracy](https://mlsanity.com/paper/2304.04606/) (2023)
- [Full-Head Segmentation of MRI with Abnormal Brain Anatomy: Model and Data Release](https://mlsanity.com/paper/2501.18716/) (2025)
- [Monitoring morphometric drift in lifelong learning segmentation of the spinal cord](https://mlsanity.com/paper/2505.01364/) (2025)
- [Virtual-reality based patient-specific simulation of spine surgical procedures: A fast, highly automated and high-fidelity system for surgical education and planning](https://mlsanity.com/paper/2604.26781/) (2026)

## Abstract (arXiv)

The spinal cord is frequently affected by atrophy and/or lesions in multiple sclerosis (MS) patients. Segmentation of the spinal cord and lesions from MRI data provides measures of damage, which are key criteria for the diagnosis, prognosis, and longitudinal monitoring in MS. Automating this operation eliminates inter-rater variability and increases the efficiency of large-throughput analysis pipelines. Robust and reliable segmentation across multi-site spinal cord data is challenging because of the large variability related to acquisition parameters and image artifacts. The goal of this study was to develop a fully-automatic framework, robust to variability in both image parameters and clinical condition, for segmentation of the spinal cord and intramedullary MS lesions from conventional MRI data. Scans of 1,042 subjects (459 healthy controls, 471 MS patients, and 112 with other spinal pathologies) were included in this multi-site study (n=30). Data spanned three contrasts (T1-, T2-, and T2*-weighted) for a total of 1,943 volumes. The proposed cord and lesion automatic segmentation approach is based on a sequence of two Convolutional Neural Networks (CNNs). To deal with the very small proportion of spinal cord and/or lesion voxels compared to the rest of the volume, a first CNN with 2D dilated convolutions detects the spinal cord centerline, followed by a second CNN with 3D convolutions that segments the spinal cord and/or lesions. When compared against manual segmentation, our CNN-based approach showed a median Dice of 95% vs. 88% for PropSeg, a state-of-the-art spinal cord segmentation method. Regarding lesion segmentation on MS data, our framework provided a Dice of 60%, a relative volume difference of -15%, and a lesion-wise detection sensitivity and precision of 83% and 77%, respectively. The proposed framework is open-source and readily available in the Spinal Cord Toolbox.