Accuracy and reproducibility of automated white matter hyperintensities segmentation with lesion segmentation tool: A European multi-site 3T study
Contributeurs/tricesRibaldi, Federica; Altomare, Daniele; Jovicich, Jorge; Ferrari, Clarissa; Picco, Agnese; Pizzini, Francesca Benedetta; Soricelli, Andrea; Mega, Anna; Ferretti, Antonio; Drevelegas, Antonios; Bosch, Beatriz; Müller, Bernhard W; Marra, Camillo; Cavaliere, Carlo; Bartrés-Faz, David; Nobili, Flavio; Alessandrini, Franco; Barkhof, Frederik; Gros-Dagnac, Helene; Ranjeva, Jean-Philippe; Wiltfang, Jens; Kuijer, Joost; Sein, Julien; Hoffmann, Karl-Titus; Roccatagliata, Luca; Parnetti, Lucilla; Tsolaki, Magda; Constantinidis, Manos; Aiello, Marco; Salvatore, Marco; Montalti, Martina; Caulo, Massimo; Didic, Mira; Bargallo, Núria; Blin, Olivier; Rossini, Paolo M; Schonknecht, Peter; Floridi, Piero; Payoux, Pierre; Visser, Pieter Jelle; Bordet, Régis; Lopes, Renaud; Tarducci, Roberto; Bombois, Stephanie; Hensch, Tilman; Fiedler, Ute; Richardson, Jill C; Frisoni, Giovanni; Marizzoni, Moira
Publié dansMagnetic resonance imaging, vol. 76, p. 108-115
Date de publication2021-02
Date de mise en ligne2020-11-19
Résumé
Mots-clés
- Accuracy
- Automated segmentation algorithms
- Lesion segmentation toolbox
- Reproducibility
- White matter hyperintensities
- Adult
- Aging
- Algorithms
- Automation
- Cross-Sectional Studies
- Female
- Humans
- Image Processing, Computer-Assisted / methods
- Magnetic Resonance Imaging
- Male
- Reproducibility of Results
- White Matter / diagnostic imaging
- White Matter / pathology
Structure d'affiliation
Groupe de recherche
Citation (format ISO)
RIBALDI, Federica et al. Accuracy and reproducibility of automated white matter hyperintensities segmentation with lesion segmentation tool: A European multi-site 3T study. In: Magnetic resonance imaging, 2021, vol. 76, p. 108–115. doi: 10.1016/j.mri.2020.11.008
Fichiers principaux (1)
Article (Published version)
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Fichiers secondaires (1)
Identifiants
- PID : unige:169174
- DOI : 10.1016/j.mri.2020.11.008
- PMID : 33220450
ISSN du journal0730-725X