Fiber tractography using machine learning

- First machine learning-driven approach to fiber tractography.
- Processing of the raw signal. No mathematical modeling and inverse problem solving.
- Extensive evaluation using publicly available in vivo and in silico data.
- Highly promising results compared to over 100 tractography pipelines.

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.