  The state-of-the-art method for automatically segmenting white matter bundles
in diffusion-weighted MRI is tractography in conjunction with streamline
cluster selection. This process involves long chains of processing steps which
are not only computationally expensive but also complex to setup and tedious
with respect to quality control. Direct bundle segmentation methods treat the
task as a traditional image segmentation problem. While they so far did not
deliver competitive results, they can potentially mitigate many of the
mentioned issues. We present a novel supervised approach for direct tract
segmentation that shows major performance gains. It builds upon a stacked U-Net
architecture which is trained on manual bundle segmentations from Human
Connectome Project subjects. We evaluate our approach \textit{in vivo} as well
as \textit{in silico} using the ISMRM 2015 Tractography Challenge phantom
dataset. We achieve human segmentation performance and a major performance gain
over previous pipelines. We show how the learned spatial priors efficiently
guide the segmentation even at lower image qualities with little quality loss.
