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

arXiv: https://arxiv.org/abs/2507.12092 · HTML: https://mlsanity.com/paper/2507.12092/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 42 references that are in the map.
- First posted on arXiv 16 Jul 2025.

## Builds on (most cited first, 17 of 17)

- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation](https://mlsanity.com/paper/1606.06650/) (2016)
- [nnU-Net Revisited: A Call for Rigorous Validation in 3D Medical Image Segmentation](https://mlsanity.com/paper/2404.09556/) (2024)
- [SynthSeg: Segmentation of brain MRI scans of any contrast and resolution without retraining](https://mlsanity.com/paper/2107.09559/) (2021)
- [Automated brain extraction of multi-sequence MRI using artificial neural networks](https://mlsanity.com/paper/1901.11341/) (2019)
- [Machine Learning with Multi-Site Imaging Data: An Empirical Study on the Impact of Scanner Effects](https://mlsanity.com/paper/1910.04597/) (2019)
- [On-the-Fly Test-time Adaptation for Medical Image Segmentation](https://mlsanity.com/paper/2203.05574/) (2022)
- [CarveMix: A Simple Data Augmentation Method for Brain Lesion Segmentation](https://mlsanity.com/paper/2108.06883/) (2021)
- [blob loss: instance imbalance aware loss functions for semantic segmentation](https://mlsanity.com/paper/2205.08209/) (2022)
- [Advancing Medical Image Segmentation with Mini-Net: A Lightweight Solution Tailored for Efficient Segmentation of Medical Images](https://mlsanity.com/paper/2405.17520/) (2024)
- [Novel structural-scale uncertainty measures and error retention curves: application to multiple sclerosis](https://mlsanity.com/paper/2211.04825/) (2022)
- [Tackling Bias in the Dice Similarity Coefficient: Introducing nDSC for White Matter Lesion Segmentation](https://mlsanity.com/paper/2302.05432/) (2023)
- [Structural-Based Uncertainty in Deep Learning Across Anatomical Scales: Analysis in White Matter Lesion Segmentation](https://mlsanity.com/paper/2311.08931/) (2023)
- [LV-UNet: A Lightweight and Vanilla Model for Medical Image Segmentation](https://mlsanity.com/paper/2408.16886/) (2024)
- [Segmenting Small Stroke Lesions with Novel Labeling Strategies](https://mlsanity.com/paper/2408.02929/) (2024)
- [Cortical lesions, central vein sign, and paramagnetic rim lesions in multiple sclerosis: emerging machine learning techniques and future avenues](https://mlsanity.com/paper/2201.07463/) (2022)
- [Exploiting XAI maps to improve MS lesion segmentation and detection in MRI](https://mlsanity.com/paper/2409.03772/) (2024)

## Built on by (most cited first, 1 of 1)

- [Component-Adaptive and Lesion-Level Supervision for Improved Small Structure Segmentation in Brain MRI](https://mlsanity.com/paper/2604.08015/) (2026)

## 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.