mlsanityData to 13 Oct 2026

Benchmarking and Explaining Deep Learning Cortical Lesion MRI Segmentation in Multiple Sclerosis

arXiv 2507.12092 ↗First posted 16 Jul 2025eess.IV cs.CVcited by 1

Not on the map. Why?

Abstract (arXiv)

Cortical lesions (CLs) have emerged as valuable biomarkers in multiple sclerosis (MS), offering high diagnostic specificity and prognostic relevance. However, their routine clinical integration remains limited due to subtle magnetic resonance imaging (MRI) appearance, challenges in expert annotation, and a lack of standardized automated methods. We propose a comprehensive multi-centric benchmark of CL detection and segmentation in MRI. A total of 656 MRI scans, including clinical trial and research data from four institutions, were acquired at 3T and 7T using MP2RAGE and MPRAGE sequences with expert-consensus annotations. We rely on the self-configuring nnU-Net framework, designed for medical imaging segmentation, and propose adaptations tailored to the improved CL detection. We evaluated model generalization through out-of-distribution testing, demonstrating strong lesion detection capabilities with an F1-score of 0.64 and 0.5 in and out of the domain, respectively. We also analyze internal model features and model errors for a better understanding of AI decision-making. Our study examines how data variability, lesion ambiguity, and protocol differences impact model performance, offering future recommendations to address these barriers to clinical adoption. To reinforce the reproducibility, the implementation and models will be publicly accessible and ready to use at https://github.com/Medical-Image-Analysis-Laboratory/ and https://doi.org/10.5281/zenodo.15911797.

Key facts. Cited by 1 paper in the map (data through 13 Oct 2026). Builds on 17 of its 42 references that are in the map. First posted on arXiv 16 Jul 2025.

Builds on

17 of 42 references.

References · 17

  1. 2018UMAP: Uniform Manifold Approximation and Projection for Dimension Reductioncited by 2,870
  2. 20163D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotationcited by 1,475
  3. 2024nnU-Net Revisited: A Call for Rigorous Validation in 3D Medical Image Segmentationcited by 237
  4. 2021SynthSeg: Segmentation of brain MRI scans of any contrast and resolution without retrainingcited by 236
  5. 2019Automated brain extraction of multi-sequence MRI using artificial neural networkscited by 133
  6. 2019Machine Learning with Multi-Site Imaging Data: An Empirical Study on the Impact of Scanner Effectscited by 41
  7. 2022On-the-Fly Test-time Adaptation for Medical Image Segmentationcited by 34
  8. 2021CarveMix: A Simple Data Augmentation Method for Brain Lesion Segmentationcited by 30
  9. 2022blob loss: instance imbalance aware loss functions for semantic segmentationcited by 18
  10. 2024Advancing Medical Image Segmentation with Mini-Net: A Lightweight Solution Tailored for Efficient Segmentation of Medical Imagescited by 9
  11. 2022Novel structural-scale uncertainty measures and error retention curves: application to multiple sclerosiscited by 8
  12. 2023Tackling Bias in the Dice Similarity Coefficient: Introducing nDSC for White Matter Lesion Segmentationcited by 8
5 more
  1. 2023Structural-Based Uncertainty in Deep Learning Across Anatomical Scales: Analysis in White Matter Lesion Segmentationcited by 7
  2. 2024LV-UNet: A Lightweight and Vanilla Model for Medical Image Segmentationcited by 6
  3. 2024Segmenting Small Stroke Lesions with Novel Labeling Strategiescited by 2
  4. 2022Cortical lesions, central vein sign, and paramagnetic rim lesions in multiple sclerosis: emerging machine learning techniques and future avenuescited by 1
  5. 2024Exploiting XAI maps to improve MS lesion segmentation and detection in MRIcited by 1

Built on by

Cited by year (2026 to 13 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Component-Adaptive and Lesion-Level Supervision for Improved Small Structure Segmentation in Brain MRIcited by 0