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Abstract #0041

Bundle-Wise Deep Tracker: Learning to track bundle-specific streamline paths

Philippe Poulin1, Francois Rheault1, Etienne St-Onge1, Pierre-Marc Jodoin1, and Maxime Descoteaux1

1University of Sherbrooke, Sherbrooke, QC, Canada

We propose a novel bundle-wise tracking algorithm based on deep learning and recurrent neural networks. This allows bundle-specific features to be learned directly from the diffusion signal without the need to reconstruct a fiber orientation distribution. With a high amount of examples, the proposed method improves classic algorithms for several quantitative measures such as tracking efficiency, number of valid streamlines, and volume coverage.

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