# Multiple Sclerosis Lesion Segmentation -- A Survey of Supervised CNN-Based Methods

arXiv: https://arxiv.org/abs/2012.08317 · HTML: https://mlsanity.com/paper/2012.08317/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Dec 2020.

## Built on by (most cited first, 3 of 3)

- [Multiple Sclerosis Lesion Analysis in Brain Magnetic Resonance Images: Techniques and Clinical Applications](https://mlsanity.com/paper/2104.10029/) (2021)
- [Transformer based Models for Unsupervised Anomaly Segmentation in Brain MR Images](https://mlsanity.com/paper/2207.02059/) (2022)
- [Ensemble CNN and Uncertainty Modeling to Improve Automatic Identification/Segmentation of Multiple Sclerosis Lesions in Magnetic Resonance Imaging](https://mlsanity.com/paper/2108.11791/) (2021)

## Abstract (arXiv)

Lesion segmentation is a core task for quantitative analysis of MRI scans of Multiple Sclerosis patients. The recent success of deep learning techniques in a variety of medical image analysis applications has renewed community interest in this challenging problem and led to a burst of activity for new algorithm development. In this survey, we investigate the supervised CNN-based methods for MS lesion segmentation. We decouple these reviewed works into their algorithmic components and discuss each separately. For methods that provide evaluations on public benchmark datasets, we report comparisons between their results.