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Fiber tractography using machine learning

By Peter F. Neher, Marc-Alexandre Côté, Jean-Christophe Houde, Maxime Descoteaux, Klaus H. Maier-Hein

Posted 30 Jan 2017
bioRxiv DOI: 10.1101/104190 (published DOI: 10.1016/j.neuroimage.2017.07.028)

We present a fiber tractography approach based on a random forest classification and voting process, guiding each step of the streamline progression by directly processing raw diffusion-weighted signal intensities. For comparison to the state-of-the-art, i.e. tractography pipelines that rely on mathematical modeling, we performed a quantitative and qualitative evaluation with multiple phantom and in vivo experiments, including a comparison to the 96 submissions of the ISMRM tractography challenge 2015. The results demonstrate the vast potential of machine learning for fiber tractography.

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