Fiber tractography using machine learning

Publication date: September 2017
Source:NeuroImage, Volume 158
Author(s): Peter F. Neher, Marc-Alexandre Côté, Jean-Christophe Houde, Maxime Descoteaux, Klaus H. Maier-Hein
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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