  Deep learning approaches for diffusion MRI have so far focused primarily on
voxel-based segmentation of lesions or white-matter fiber tracts. A drawback of
representing tracts as volumetric labels, rather than sets of streamlines, is
that it precludes point-wise analyses of microstructural or geometric features
along a tract. Traditional tractography pipelines, which do allow such
analyses, can benefit from detailed whole-brain segmentations to guide tract
reconstruction. Here, we introduce fast, deep learning-based segmentation of
170 anatomical regions directly on diffusion-weighted MR images, removing the
dependency of conventional segmentation methods on T 1-weighted images and slow
pre-processing pipelines. Working natively in diffusion space avoids non-linear
distortions and registration errors across modalities, as well as interpolation
artifacts. We demonstrate consistent segmentation results between 0 .70 and 0
.87 Dice depending on the tissue type. We investigate various combinations of
diffusion-derived inputs and show generalization across different numbers of
gradient directions. Finally, integrating our approach to provide anatomical
priors for tractography pipelines, such as TRACULA, removes hours of
pre-processing time and permits processing even in the absence of high-quality
T 1-weighted scans, without degrading the quality of the resulting tract
estimates.
