TY - JOUR T1 - Fiber tractography using machine learning JF - bioRxiv DO - 10.1101/104190 SP - 104190 AU - Peter F. Neher AU - Marc-Alexandre Côté AU - Jean-Christophe Houde AU - Maxime Descoteaux AU - Klaus H. Maier-Hein Y1 - 2017/01/01 UR - http://biorxiv.org/content/early/2017/01/30/104190.abstract N2 - 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. ER -