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.