  The individual course of white matter fiber tracts is an important key for
analysis of white matter characteristics in healthy and diseased brains.
Uniquely, diffusion-weighted MRI tractography in combination with region-based
or clustering-based selection of streamlines allows for the in-vivo delineation
and analysis of anatomically well known tracts. This, however, currently
requires complex, computationally intensive and tedious-to-set-up processing
pipelines. TractSeg is a novel convolutional neural network-based approach that
directly segments tracts in the field of fiber orientation distribution
function (fODF) peaks without requiring tractography, image registration or
parcellation. We demonstrate in 105 subjects from the Human Connectome Project
that the proposed approach is much faster than existing methods while providing
unprecedented accuracy. The code and data are openly available at
https://github.com/MIC-DKFZ/TractSeg/ and
https://doi.org/10.5281/zenodo.1088277, respectively.
