mlsanityData to 7 Oct 2026

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

arXiv 2104.10029 ↗First posted 20 Apr 2021cs.CV eess.IV stat.APcited by 10

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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.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 26 of its 103 references that are in the map. First posted on arXiv 20 Apr 2021.

Builds on

26 of 103 references.

References · 26

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14 more
  1. 2017Transfer Learning for Domain Adaptation in MRI: Application in Brain Lesion Segmentationcited by 66
  2. 2018Asymmetric Loss Functions and Deep Densely Connected Networks for Highly Imbalanced Medical Image Segmentation: Application to Multiple Sclerosis Lesion Detectioncited by 22
  3. 2018One-shot domain adaptation in multiple sclerosis lesion segmentation using convolutional neural networkscited by 20
  4. 2017Improving automated multiple sclerosis lesion segmentation with a cascaded 3D convolutional neural network approachcited by 19
  5. 2020A Contrast-Adaptive Method for Simultaneous Whole-Brain and Lesion Segmentation in Multiple Sclerosiscited by 17
  6. 2018Multi-branch Convolutional Neural Network for Multiple Sclerosis Lesion Segmentationcited by 16
  7. 2020Unsupervised Domain Adaptation via CycleGAN for White Matter Hyperintensity Segmentation in Multicenter MR Imagescited by 15
  8. 2020Geometric Loss for Deep Multiple Sclerosis lesion Segmentationcited by 8
  9. 2016Deep Multi-scale Location-aware 3D Convolutional Neural Networks for Automated Detection of Lacunes of Presumed Vascular Origincited by 7
  10. 2018Multiple Sclerosis Lesion Segmentation from Brain MRI via Fully Convolutional Neural Networkscited by 7
  11. 2020Multiple Sclerosis Lesion Activity Segmentation with Attention-Guided Two-Path CNNscited by 5
  12. 20204D Deep Learning for Multiple Sclerosis Lesion Activity Segmentationcited by 3
  13. 2020A Longitudinal Method for Simultaneous Whole-Brain and Lesion Segmentation in Multiple Sclerosiscited by 3
  14. 2020Multiple Sclerosis Lesion Segmentation -- A Survey of Supervised CNN-Based Methodscited by 3

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 2; 2023: 2; 2024: 4; 2025: 1; 2026: 1.

10 papers.

  1. 2023CoactSeg: Learning from Heterogeneous Data for New Multiple Sclerosis Lesion Segmentationcited by 9
  2. 2023Improving Multiple Sclerosis Lesion Segmentation Across Clinical Sites: A Federated Learning Approach with Noise-Resilient Trainingcited by 4
  3. 2022MS Lesion Segmentation: Revisiting Weighting Mechanisms for Federated Learningcited by 3
  4. 2022TW-BAG: Tensor-wise Brain-aware Gate Network for Inpainting Disrupted Diffusion Tensor Imagingcited by 3
  5. 2024Symmetry Awareness Encoded Deep Learning Framework for Brain Imaging Analysiscited by 3
  6. 2025UNISELF: A Unified Network with Instance Normalization and Self-Ensembled Lesion Fusion for Multiple Sclerosis Lesion Segmentationcited by 2
  7. 2024P-Count: Persistence-based Counting of White Matter Hyperintensities in Brain MRIcited by 1
  8. 2024Exploiting XAI maps to improve MS lesion segmentation and detection in MRIcited by 1
  9. 2024Instance-level quantitative saliency in multiple sclerosis lesion segmentationcited by 0
  10. 2026Rethinking Evaluation of Multiple Sclerosis (MS) Lesion Segmentation Modelscited by 0