SEGMA: An Automatic SEGMentation Approach for Human Brain MRI Using Sliding Window and Random Forests

Quantitative volumes from brain magnetic resonance imaging (MRI) acquired across the life course may be useful for investigating long term effects of risk and resilience factors for brain development and healthy aging, and for understanding early life determinants of adult brain structure. Therefore...

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Detalhes bibliográficos
Publicado no:Front Neuroinform
Main Authors: Serag, Ahmed, Wilkinson, Alastair G., Telford, Emma J., Pataky, Rozalia, Sparrow, Sarah A., Anblagan, Devasuda, Macnaught, Gillian, Semple, Scott I., Boardman, James P.
Formato: Artigo
Idioma:Inglês
Publicado em: Frontiers Media S.A. 2017
Assuntos:
Neuroscience
Acesso em linha:https://ncbi.nlm.nih.gov/pmc/articles/PMC5247463/
https://ncbi.nlm.nih.gov/pubmed/28163680
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.3389/fninf.2017.00002
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