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 anatomically 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 metric for clustering streamlines based on their position relative to cortical and subcortical brain regions. We incorporate this metric into a hierarchical clustering algorithm and compare it to a metric that relies on Euclidean distance,using data from the Human Connectome Project. We show that the anatomical similarity metric leads to a 20% improvement in the agreement of clustering results with manually labeled tracts,without introducing prior information from a tract atlas into the clustering.
CITATION STYLE
Siless, V., Chang, K., Fischl, B., & Yendiki, A. (2016). Hierarchical clustering of tractography streamlines based on anatomical similarity. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9900 LNCS, pp. 184–191). Springer Verlag. https://doi.org/10.1007/978-3-319-46720-7_22
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