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Abstract #0878

BundleAtlasing: unbiased population-specific atlasing of bundles in streamline space

David Romero-Bascones1, Bramsh Qamar Chandio2, Shreyas Fadnavis2, Jong Sung Park2, Serge Koudoro2, Unai Ayala1, Maitane Barrenechea1, and Eleftherios Garyfallidis2
1Biomedical Engineering Department, Mondragon Unibertsitatea, Mondragón, Spain, 2Department of Intelligent Systems Engineering, Indiana University Bloomington, Bloomington, IN, United States


White matter bundle atlases play a crucial role in the segmentation of bundles and the understanding of brain connectomes. However, the construction of streamline atlases that accurately represent the underlying population anatomy is challenging. In this work, we present BundleAtlasing, a new method to compute population-specific bundle atlases in the space of streamlines. The proposed approach is based on two key aspects: an iterative groupwise unbiased bundle registration, and a pairwise bundle combination strategy. We show that our method is able to correctly generate unbiased atlases that represent the average group anatomy of a population.

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