@article {Neher104190, author = {Peter F. Neher and Marc-Alexandre C{\^o}t{\'e} and Jean-Christophe Houde and Maxime Descoteaux and Klaus H. Maier-Hein}, title = {Fiber tractography using machine learning}, elocation-id = {104190}, year = {2017}, doi = {10.1101/104190}, publisher = {Cold Spring Harbor Laboratory}, 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.}, URL = {https://www.biorxiv.org/content/early/2017/01/30/104190}, eprint = {https://www.biorxiv.org/content/early/2017/01/30/104190.full.pdf}, journal = {bioRxiv} }