Hierarchical Clustering of Tractography Streamlines Based on Anatomical Similarity

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 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.