Fiber tractography using machine learning

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.

Authors

• Peter F. Neher
• Marc-Alexandre Côté
• Jean-Christophe Houde
• Maxime Descoteaux
• Klaus H. Maier-Hein

Keywords

• Fiber tractography
• Machine learning
• Diffusion-weighted imaging
• Connectomics