Reduced accuracy of MRI deep grey matter segmentation in multiple sclerosis: an evaluation of four automated methods against manual reference segmentations in a multi-center cohort

BACKGROUND: Deep grey matter (DGM) atrophy in multiple sclerosis (MS) and its relation to cognitive and clinical decline requires accurate measurements. MS pathology may deteriorate the performance of automated segmentation methods. Accuracy of DGM segmentation methods is compared between MS and con...

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Detalles Bibliográficos
Autores principales: de Sitter, Alexandra, Verhoeven, Tom, Burggraaff, Jessica, Liu, Yaou, Simoes, Jorge, Ruggieri, Serena, Palotai, Miklos, Brouwer, Iman, Versteeg, Adriaan, Wottschel, Viktor, Ropele, Stefan, Rocca, Mara A., Gasperini, Claudio, Gallo, Antonio, Yiannakas, Marios C., Rovira, Alex, Enzinger, Christian, Filippi, Massimo, De Stefano, Nicola, Kappos, Ludwig, Frederiksen, Jette L., Uitdehaag, Bernard M. J., Barkhof, Frederik, Guttmann, Charles R. G., Vrenken, Hugo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2020
Materias:
Original Communication
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7674567/
https://www.ncbi.nlm.nih.gov/pubmed/32621103
http://dx.doi.org/10.1007/s00415-020-10023-1

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7674567/
https://www.ncbi.nlm.nih.gov/pubmed/32621103
http://dx.doi.org/10.1007/s00415-020-10023-1

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