Contrast-Enhancing Lesion Segmentation in Multiple Sclerosis: A Deep Learning Approach Validated in a Multicentric Cohort

The detection of contrast-enhancing lesions (CELs) is fundamental for the diagnosis and monitoring of patients with multiple sclerosis (MS). This task is time-consuming and suffers from high intra- and inter-rater variability in clinical practice. However, only a few studies proposed automatic appro...

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Autores principales:	Martina Greselin, Po-Jui Lu, Lester Melie-Garcia, Mario Ocampo-Pineda, Riccardo Galbusera, Alessandro Cagol, Matthias Weigel, Nina de Oliveira Siebenborn, Esther Ruberte, Pascal Benkert, Stefanie Müller, Sebastian Finkener, Jochen Vehoff, Giulio Disanto, Oliver Findling, Andrew Chan, Anke Salmen, Caroline Pot, Claire Bridel, Chiara Zecca, Tobias Derfuss, Johanna M. Lieb, Michael Diepers, Franca Wagner, Maria I. Vargas, Renaud Du Pasquier, Patrice H. Lalive, Emanuele Pravatà, Johannes Weber, Claudio Gobbi, David Leppert, Olaf Chan-Hi Kim, Philippe C. Cattin, Robert Hoepner, Patrick Roth, Ludwig Kappos, Jens Kuhle, Cristina Granziera
Formato:	Artigo
Lenguaje:	Inglês
Publicado:	MDPI AG 2024-08-01
Colección:	Bioengineering
Materias:	deep learning multiple sclerosis automatic segmentation gadolinium contrast-enhancing lesions
Acceso en línea:	https://www.mdpi.com/2306-5354/11/8/858
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