PT  - JOURNAL ARTICLE
AU  - Peter F. Neher
AU  - Marc-Alexandre Côté
AU  - Jean-Christophe Houde
AU  - Maxime Descoteaux
AU  - Klaus H. Maier-Hein
TI  - Fiber tractography using machine learning
AID  - 10.1101/104190
DP  - 2017 Jan 01
TA  - bioRxiv
PG  - 104190
4099  - http://biorxiv.org/content/early/2017/01/30/104190.short
4100  - http://biorxiv.org/content/early/2017/01/30/104190.full
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.