AnatomiCuts: Hierarchical clustering of tractography streamlines based on anatomical similarity.
Neuroimage
; 166: 32-45, 2018 02 01.
Article
em En
| MEDLINE
| ID: mdl-29100937
ABSTRACT
Diffusion MRI tractography produces massive sets of streamlines that contain a wealth of information on brain connections. The size of these datasets creates a need for automated clustering methods to group the streamlines into meaningful bundles. Conventional clustering techniques group streamlines based on their spatial coordinates. Neuroanatomists, however, define white-matter bundles based on the anatomical structures that they go through or next to, rather than their spatial coordinates. Thus we propose a similarity measure for clustering streamlines based on their position relative to cortical and subcortical brain regions. We incorporate this measure into a hierarchical clustering algorithm and compare it to a measure that relies on Euclidean distance, using data from the Human Connectome Project. We show that the anatomical similarity measure leads to a 20% improvement in the overlap of clusters with manually labeled tracts. Importantly, this is achieved without introducing any prior information from a tract atlas into the clustering algorithm, therefore without imposing the existence of any named tracts.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Imagem de Tensor de Difusão
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Conectoma
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Substância Branca
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Modelos Teóricos
Limite:
Adult
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Humans
Idioma:
En
Revista:
Neuroimage
Assunto da revista:
DIAGNOSTICO POR IMAGEM
Ano de publicação:
2018
Tipo de documento:
Article