# Multiple Sclerosis Lesion Analysis in Brain Magnetic Resonance Images: Techniques and Clinical Applications

arXiv: https://arxiv.org/abs/2104.10029 · HTML: https://mlsanity.com/paper/2104.10029/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 103 references that are in the map.
- First posted on arXiv 20 Apr 2021.

## Builds on (most cited first, 26 of 26)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation](https://mlsanity.com/paper/1606.06650/) (2016)
- [Domain Adaptation for Medical Image Analysis: A Survey](https://mlsanity.com/paper/2102.09508/) (2021)
- [A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promises](https://mlsanity.com/paper/2008.09104/) (2020)
- [Weakly- and Semi-Supervised Learning of a DCNN for Semantic Image Segmentation](https://mlsanity.com/paper/1502.02734/) (2015)
- [Exploring Uncertainty Measures in Deep Networks for Multiple Sclerosis Lesion Detection and Segmentation](https://mlsanity.com/paper/1808.01200/) (2018)
- [Unsupervised domain adaptation in brain lesion segmentation with adversarial networks](https://mlsanity.com/paper/1612.08894/) (2016)
- [HeMIS: Hetero-Modal Image Segmentation](https://mlsanity.com/paper/1607.05194/) (2016)
- [Transfer Learning for Domain Adaptation in MRI: Application in Brain Lesion Segmentation](https://mlsanity.com/paper/1702.07841/) (2017)
- [Asymmetric Loss Functions and Deep Densely Connected Networks for Highly Imbalanced Medical Image Segmentation: Application to Multiple Sclerosis Lesion Detection](https://mlsanity.com/paper/1803.11078/) (2018)
- [One-shot domain adaptation in multiple sclerosis lesion segmentation using convolutional neural networks](https://mlsanity.com/paper/1805.12415/) (2018)
- [Improving automated multiple sclerosis lesion segmentation with a cascaded 3D convolutional neural network approach](https://mlsanity.com/paper/1702.04869/) (2017)
- [A Contrast-Adaptive Method for Simultaneous Whole-Brain and Lesion Segmentation in Multiple Sclerosis](https://mlsanity.com/paper/2005.05135/) (2020)
- [Multi-branch Convolutional Neural Network for Multiple Sclerosis Lesion Segmentation](https://mlsanity.com/paper/1811.02942/) (2018)
- [Unsupervised Domain Adaptation via CycleGAN for White Matter Hyperintensity Segmentation in Multicenter MR Images](https://mlsanity.com/paper/2009.04985/) (2020)
- [Geometric Loss for Deep Multiple Sclerosis lesion Segmentation](https://mlsanity.com/paper/2009.13755/) (2020)
- [Deep Multi-scale Location-aware 3D Convolutional Neural Networks for Automated Detection of Lacunes of Presumed Vascular Origin](https://mlsanity.com/paper/1610.07442/) (2016)
- [Multiple Sclerosis Lesion Segmentation from Brain MRI via Fully Convolutional Neural Networks](https://mlsanity.com/paper/1803.09172/) (2018)
- [Multiple Sclerosis Lesion Activity Segmentation with Attention-Guided Two-Path CNNs](https://mlsanity.com/paper/2008.02001/) (2020)
- [4D Deep Learning for Multiple Sclerosis Lesion Activity Segmentation](https://mlsanity.com/paper/2004.09216/) (2020)
- [A Longitudinal Method for Simultaneous Whole-Brain and Lesion Segmentation in Multiple Sclerosis](https://mlsanity.com/paper/2008.05117/) (2020)
- [Multiple Sclerosis Lesion Segmentation -- A Survey of Supervised CNN-Based Methods](https://mlsanity.com/paper/2012.08317/) (2020)

## Built on by (most cited first, 10 of 10)

- [CoactSeg: Learning from Heterogeneous Data for New Multiple Sclerosis Lesion Segmentation](https://mlsanity.com/paper/2307.04513/) (2023)
- [Improving Multiple Sclerosis Lesion Segmentation Across Clinical Sites: A Federated Learning Approach with Noise-Resilient Training](https://mlsanity.com/paper/2308.16376/) (2023)
- [MS Lesion Segmentation: Revisiting Weighting Mechanisms for Federated Learning](https://mlsanity.com/paper/2205.01509/) (2022)
- [TW-BAG: Tensor-wise Brain-aware Gate Network for Inpainting Disrupted Diffusion Tensor Imaging](https://mlsanity.com/paper/2210.17076/) (2022)
- [Symmetry Awareness Encoded Deep Learning Framework for Brain Imaging Analysis](https://mlsanity.com/paper/2407.08948/) (2024)
- [UNISELF: A Unified Network with Instance Normalization and Self-Ensembled Lesion Fusion for Multiple Sclerosis Lesion Segmentation](https://mlsanity.com/paper/2508.03982/) (2025)
- [P-Count: Persistence-based Counting of White Matter Hyperintensities in Brain MRI](https://mlsanity.com/paper/2403.13996/) (2024)
- [Exploiting XAI maps to improve MS lesion segmentation and detection in MRI](https://mlsanity.com/paper/2409.03772/) (2024)
- [Instance-level quantitative saliency in multiple sclerosis lesion segmentation](https://mlsanity.com/paper/2406.09335/) (2024)
- [Rethinking Evaluation of Multiple Sclerosis (MS) Lesion Segmentation Models](https://mlsanity.com/paper/2605.09666/) (2026)

## Abstract (arXiv)

Multiple sclerosis (MS) is a chronic inflammatory and degenerative disease of the central nervous system, characterized by the appearance of focal lesions in the white and gray matter that topographically correlate with an individual patient's neurological symptoms and signs. Magnetic resonance imaging (MRI) provides detailed in-vivo structural information, permitting the quantification and categorization of MS lesions that critically inform disease management. Traditionally, MS lesions have been manually annotated on 2D MRI slices, a process that is inefficient and prone to inter-/intra-observer errors. Recently, automated statistical imaging analysis techniques have been proposed to detect and segment MS lesions based on MRI voxel intensity. However, their effectiveness is limited by the heterogeneity of both MRI data acquisition techniques and the appearance of MS lesions. By learning complex lesion representations directly from images, deep learning techniques have achieved remarkable breakthroughs in the MS lesion segmentation task. Here, we provide a comprehensive review of state-of-the-art automatic statistical and deep-learning MS segmentation methods and discuss current and future clinical applications. Further, we review technical strategies, such as domain adaptation, to enhance MS lesion segmentation in real-world clinical settings.