Automated White Matter Hyperintensity Segmentation Using Bayesian Model Selection: Assessment and Correlations with Cognitive Change

Accurate, automated white matter hyperintensity (WMH) segmentations are needed for large-scale studies to understand contributions of WMH to neurological diseases. We evaluated Bayesian Model Selection (BaMoS), a hierarchical fully-unsupervised model selection framework for WMH segmentation. We comp...

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Dettagli Bibliografici
Pubblicato in:Neuroinformatics
Autori principali: Fiford, Cassidy M., Sudre, Carole H., Pemberton, Hugh, Walsh, Phoebe, Manning, Emily, Malone, Ian B., Nicholas, Jennifer, Bouvy, Willem H, Carmichael, Owen T., Biessels, Geert Jan, Cardoso, M. Jorge, Barnes, Josephine
Natura: Artigo
Lingua:Inglês
Pubblicazione: Springer US 2020
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Original Article
Accesso online:https://ncbi.nlm.nih.gov/pmc/articles/PMC7338814/
https://ncbi.nlm.nih.gov/pubmed/32062817
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1007/s12021-019-09439-6
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