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A comparative study of deep learning for cortical lesion MRI segmentation with explainability analysis in multiple sclerosis.

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LICENSE, under Apache-2.0 · at the source

Overview

Authors: Nataliia Molchanova1,2,3,4, Alessandro Cagol5,6,7,8, Mario Ocampo–Pineda5,6,7, Po–Jui Lu5,6,7, Matthias Weigel5,6,7,9, Xinjie Chen5,6,7, Erin S. Beck10,11, Charidimos Tsagkas11, Daniel S. Reich11, Colin Vanden Bulcke12, Anna Stölting12, Serena Borrelli12,13, Pietro Maggi12, Sebastian Baez Lugo14, Delphine Ribes Lemay14, Adrien Depeursinge3, Cristina Granziera5,6,7, Henning Müller3,15, Pedro M. Gordaliza4,2,1, Meritxell Bach Cuadra4,2,1
ORCID iDs: Nataliia Molchanova, Anna Stölting, Serena Borrelli
15 affiliations
  1. Faculty of Biology and Medicine, University of Lausanne (UNIL), Lausanne, Switzerland
  2. Radiology Department, Lausanne University Hospital (CHUV), Lausanne, Switzerland
  3. MedGIFT, Institute of Informatics, School of Management, HES–SO Valais–Wallis University of Applied Sciences and Arts Western Switzerland, Sierre, Switzerland
  4. CIBM Center for Biomedical Imaging, Lausanne, Switzerland
  5. Translational Imaging in Neurology (ThINK) Basel, Department of Medicine and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
  6. Multiple Sclerosis Center, Department of Neurology, University Hospital Basel, Basel, Switzerland
  7. Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
  8. Dipartimento di Scienze della Salute, Università degli Studi di Genova, Genova, Italy
  9. Division of Radiological Physics, Department of Radiology, University Hospital Basel, Basel, Switzerland
  10. Department of Neurology, Icahn School of Medicine at Mount Sinai, New York City, USA
  11. Translational Neuroradiology Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, USA
  12. Neuroinflammation Imaging Lab (NIL), Université catholique de Louvain, Brussels, Belgium
  13. Department of Neurology, Hôpital Erasme, Hôpital Universitaire de Bruxelles, Université libre de Bruxelles, Brussels, Belgium
  14. EPFL+ECAL Lab, École polytechnique fédérale de Lausanne (EPFL), Lausanne, Switzerland
  15. Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland
Institutions: Centre Hospitalier Universitaire Vaudois (Switzerland); HES-SO Valais-Wallis (Switzerland); Centre d'Imagerie BioMedicale (Switzerland); University of Lausanne (Switzerland); University of Basel (Switzerland); University Hospital of Basel (Switzerland); University of Genoa (Italy); National Institutes of Health (United States); National Institute of Neurological Disorders and Stroke (United States); Icahn School of Medicine at Mount Sinai (United States); UCLouvain (Belgium); Université Libre de Bruxelles (Belgium); Erasmus Hospital (Belgium); École Polytechnique Fédérale de Lausanne (Switzerland); HES-SO University of Applied Sciences and Arts Western Switzerland (Switzerland); University of Geneva (Switzerland)
Journal: NeuroImage. Clinical, volume 50, article 104007
Dates: received 18 December 2025; accepted 17 May 2026; published online 23 May 2026; in print 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1016/j.nicl.2026.104007 · PMID 42224860 · PMCID PMC13251776 · OpenAlex W7162186771
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: structural MRI / diffusion (modality), human (organism), multiple sclerosis (population)
Methods: Connectivity, Statistics, Smoothing, state filtering, decompositions, Machine learning, fMRI & imaging
Keywords: Multiple sclerosis, Cortical lesions, Segmentation, Detection, Magnetic resonance imaging, Brain, Deep learning, Trustworthy AI
MeSH: Cerebral Cortex*, Deep Learning*, Image Interpretation, Computer-Assisted*, Image Processing, Computer-Assisted*, Magnetic Resonance Imaging*, Multiple Sclerosis*, Adult, Female, Humans, Male (* major topic)
Topic: Multiple Sclerosis Research Studies (Pathology and Forensic Medicine, Medicine), according to OpenAlex
Funding: Hasler Foundation Responsible AI program; Research Commission of the Faculty of Biology and Medicine (CRFBM); EUROSTAR E!113682 HORIZON2020; Intramural Research Program of NINDS, NIH; Intramural Research Program of the National Institutes of Health (NIH); Abata and Sanofi; Fonds de Recherche Clinique (FRC); Fédération Wallonie Bruxelles – FRIA du Fonds de la Recherche Scientifique – FNRS; Funds Claire Fauconnier, Ginette Kryksztein & José, and Marie Philippart-Hoffelt; Roche, Sanofi, and Brugmann Foundation; Fondation Charcot Stichting Research Fund 2023
Citations: not cited yet (Europe PMC); 54 references in the paper

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 present a multi-centric comparative study 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 promising 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. Furthermore, we designed and implemented a medical expert questionnaire for better assessment of clinical value of the model predictions. To reinforce the reproducibility, the implementation and models will be publicly accessible and ready to use at GitHub and Zenodo.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repositories

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Zenodo 3942042

License: Apache-2.0
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: the references
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 28 September 2026: the link answers (HTTP 200)
  • 28 September 2026: the link answers (HTTP 200)
2 files
At the source:

francescolr/ms-lesion-segmentation

License: Apache-2.0
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: 1ce239db9a7b0435efdadcbae5ba2fe0cbce0777, 26 April 2022
Size: 9 files, 0 scripts
Software Heritage: not archived
Found in: the Zenodo archive record
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
2 files
At the source: github.com/FrancescoLR/MS-lesion-segmentation/tree/v1.0.0

The paper's code and data availability statement is in the Data section.

Tracing map

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Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 20 authors, 8 keywords, 10 MeSH terms, 11 funders, 50 references.

Cite

This paper

Molchanova, N., Cagol, A., Ocampo–Pineda, M., Lu, P., Weigel, M., Chen, X., Beck, E. S., Tsagkas, C., Reich, D. S., Bulcke, C. V., Stölting, A., Borrelli, S., Maggi, P., Lugo, S. B., Lemay, D. R., Depeursinge, A., Granziera, C., Müller, H., Gordaliza, P. M., & Bach Cuadra, M. (2026). A comparative study of deep learning for cortical lesion MRI segmentation with explainability analysis in multiple sclerosis. NeuroImage. Clinical, 50, 104007. https://doi.org/10.1016/j.nicl.2026.104007

BibTeX

@article{molchanova2026comparative,
author = {Molchanova, Nataliia and Cagol, Alessandro and Ocampo–Pineda, Mario and Lu, Po–Jui and Weigel, Matthias and Chen, Xinjie and Beck, Erin S. and Tsagkas, Charidimos and Reich, Daniel S. and Bulcke, Colin Vanden and Stölting, Anna and Borrelli, Serena and Maggi, Pietro and Lugo, Sebastian Baez and Lemay, Delphine Ribes and Depeursinge, Adrien and Granziera, Cristina and Müller, Henning and Gordaliza, Pedro M. and Bach Cuadra, Meritxell},
title = {{A comparative study of deep learning for cortical lesion MRI segmentation with explainability analysis in multiple sclerosis}},
journal = {NeuroImage. Clinical},
year = {2026},
month = may,
volume = {50},
pages = {104007},
publisher = {Elsevier},
issn = {2213-1582},
doi = {10.1016/j.nicl.2026.104007},
url = {https://doi.org/10.1016/j.nicl.2026.104007},
pmid = {42224860},
pmcid = {PMC13251776}
}

RIS

TY - JOUR
AU - Molchanova, Nataliia
AU - Cagol, Alessandro
AU - Ocampo–Pineda, Mario
AU - Lu, Po–Jui
AU - Weigel, Matthias
AU - Chen, Xinjie
AU - Beck, Erin S.
AU - Tsagkas, Charidimos
AU - Reich, Daniel S.
AU - Bulcke, Colin Vanden
AU - Stölting, Anna
AU - Borrelli, Serena
AU - Maggi, Pietro
AU - Lugo, Sebastian Baez
AU - Lemay, Delphine Ribes
AU - Depeursinge, Adrien
AU - Granziera, Cristina
AU - Müller, Henning
AU - Gordaliza, Pedro M.
AU - Bach Cuadra, Meritxell
TI - A comparative study of deep learning for cortical lesion MRI segmentation with explainability analysis in multiple sclerosis
T2 - NeuroImage. Clinical
J2 - Neuroimage Clin
PY - 2026
DA - 2026/05/23
VL - 50
SP - 104007
SN - 2213-1582
PB - Elsevier
DO - 10.1016/j.nicl.2026.104007
UR - https://doi.org/10.1016/j.nicl.2026.104007
LA - en
ER -

CSL-JSON

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