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

• URL: http://arxiv.org/abs/2507.12092v1
• Date: Wed, 16 Jul 2025 09:56:11 GMT
• Title: Benchmarking and Explaining Deep Learning Cortical Lesion MRI Segmentation in Multiple Sclerosis
• Authors: Nataliia Molchanova, Alessandro Cagol, Mario Ocampo-Pineda, Po-Jui Lu, Matthias Weigel, Xinjie Chen, Erin Beck, Charidimos Tsagkas, Daniel Reich, Colin Vanden Bulcke, Anna Stolting, Serena Borrelli, Pietro Maggi, Adrien Depeursinge, Cristina Granziera, Henning Mueller, Pedro M. Gordaliza, Meritxell Bach Cuadra,
• Abstract summary: Cortical lesions (CLs) have emerged as valuable biomarkers in multiple sclerosis (MS)<n>We propose a comprehensive benchmark of CL detection and segmentation in MRI.<n>We rely on the self-configuring nnU-Net framework, designed for medical imaging segmentation, and propose adaptations tailored to the improved CL detection.
• Score: 28.192924379673862
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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