Fluid and White Matter Suppression contrasts MRI improves Deep Learning detection of Multiple Sclerosis Cortical Lesions
Integration of FLAWS-derived contrasts and annotations significantly improves DL-based CL detection and segmentation. The models demonstrate capability in identifying lesions missed by individual raters and maintain robust performance when applied to standard clinical sequences at external sites. This cross-sequence generalization facilitates immediate clinical translation, supported by publicly available inference models on DockerHub.
Authors
- Francesco La Rosa (ORCID: https://orcid.org/0000-0002-9224-4664)
- Nataliia Molchanova (ORCID: https://orcid.org/0000-0002-7211-8863)
- Charidimos Tsagkas
- Alessandro Cagol (ORCID: https://orcid.org/0000-0002-7703-0553)
- Pedro M. Gordaliza (ORCID: https://orcid.org/0000-0001-5305-3104)
- Jannis Müller (ORCID: https://orcid.org/0000-0003-1394-8231)
- Meritxell Bach Cuadra (ORCID: https://orcid.org/0000-0003-2730-4285)
- Cristina Granziera (ORCID: https://orcid.org/0000-0002-4917-8761)
Institutions
- HES-SO University of Applied Sciences and Arts Western Switzerland (CH)
- University Hospital of Basel (CH)
- National Institute of Neurological Disorders and Stroke (US)
- Centre d'Imagerie BioMedicale (CH)
- University of Lausanne (CH)
- Icahn School of Medicine at Mount Sinai (US)
Publication Details
- Journal
- NeuroImage Clinical
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1016/j.nicl.2025.103818
- Citations
- 1
- Primary Topic
- Advanced MRI Techniques and Applications
- Type
- article
- Field-Weighted Citation Impact
- 1.36