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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Detalles Bibliográficos
Autores principales: Serag, Ahmed, Wilkinson, Alastair G., Telford, Emma J., Pataky, Rozalia, Sparrow, Sarah A., Anblagan, Devasuda, Macnaught, Gillian, Semple, Scott I., Boardman, James P.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2017
Materias:
Neuroscience
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5247463/
https://www.ncbi.nlm.nih.gov/pubmed/28163680
http://dx.doi.org/10.3389/fninf.2017.00002

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5247463/
https://www.ncbi.nlm.nih.gov/pubmed/28163680
http://dx.doi.org/10.3389/fninf.2017.00002

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