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bioRxiv · 10.1101/104190

Fiber tractography using machine learning

Neher, P. F.·Cote, M.-A.·Houde, J.-C.·Descoteaux, M.·Maier-Hein, K. H.
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Abstract

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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Neher, P. F., Cote, M.-A., Houde, J.-C., Descoteaux, M., Maier-Hein, K. H.. 2017-01-30. Fiber tractography using machine learning. https://doi.org/10.1101/104190

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