Tract Dictionary Learning for Fast and Robust Recognition of Fiber Bundles

Fiber bundle parcellation is key to bundle-specific analysis of white matter pathways.In this abstract, we propose an efficient framework for parcellation of white matter tractograms using discriminative dictionary learning. The key to our framework is to learn a compact dictionary for each fiber bundle so that the streamlines within the bundle can be succinctly represented. Experiments on a bundle-labeled HCP dataset and an infant dataset highlight the ability of our framework in grouping streamlines into anatomically plausible bundles.