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Abstract #0673

A Novel Paradigm For Automated Segmentation of Very Large Whole-Brain Probabilistic Tractography Data Sets

Robert Elton Smith1,2, Jacques-Donald Tournier1,2, Fernando Calamante1,2, Alan Connelly1,2

1Brain Research Institute, Florey Neuroscience Institutes, Heidelberg West, Victoria, Australia; 2Department of Medicine, The University of Melbourne, Melbourne, Victoria, Australia

Conventional clustering based upon pair wise similarities has proven inadequate for the task of meaningful segmentation of whole brain probabilistic tractography. A new fully-automated algorithm has been developed based upon the identification of bound coherent bundles of tracks; fibers are segmented based upon their traversal through a common structure, rather than similarity along their entire lengths. It identifies anatomically-meaningful structures at a wide range of physical scales, and intrinsically captures the structural connectivity of each region. We demonstrate the technique on a 10,000,000 probabilistic streamlines data set.