RT Journal Article
SR Electronic
T1 Fiber tractography using machine learning
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 104190
DO 10.1101/104190
A1 Peter F. Neher
A1 Marc-Alexandre Côté
A1 Jean-Christophe Houde
A1 Maxime Descoteaux
A1 Klaus H. Maier-Hein
YR 2017
UL http://biorxiv.org/content/early/2017/01/30/104190.abstract
AB 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.