A Machine Learning Based Approach to Fiber Tractography Using Classifier Voting

Current tractography pipelines incorporate several modelling assumptions about the nature of the diffusion-weighted signal. We present an approach that tracks fiber pathways based on a random forest classification and voting process, guiding each step of the streamline progression by directly processing raw signal intensities. We evaluated our approach quantitatively and qualitatively using phantom and in vivo data. The presented machine learning based approach to fiber tractography is the first of its kind and our experiments showed auspicious performance compared to 12 established state of the art tractography pipelines. Due to its distinctly increased sensitivity and specificity regarding tract connectivity and morphology, the presented approach is a valuable addition to the repertoire of currently available tractography methods and promises to be beneficial for all applications that build upon tractography results.