  The brain white matter consists of a set of tracts that connect distinct
regions of the brain. Segmentation of these tracts is often needed for clinical
and research studies. Diffusion-weighted MRI offers unique contrast to
delineate these tracts. However, existing segmentation methods rely on
intermediate computations such as tractography or estimation of fiber
orientation density. These intermediate computations, in turn, entail complex
computations that can result in unnecessary errors. Moreover, these
intermediate computations often require dense multi-shell measurements that are
unavailable in many clinical and research applications. As a result, current
methods suffer from low accuracy and poor generalizability. Here, we propose a
new deep learning method that segments these tracts directly from the diffusion
MRI data, thereby sidestepping the intermediate computation errors. Our
experiments show that this method can achieve segmentation accuracy that is on
par with the state of the art methods (mean Dice Similarity Coefficient of
0.826). Compared with the state of the art, our method offers far superior
generalizability to undersampled data that are typical of clinical studies and
to data obtained with different acquisition protocols. Moreover, we propose a
new method for detecting inaccurate segmentations and show that it is more
accurate than standard methods that are based on estimation uncertainty
quantification. The new methods can serve many critically important clinical
and scientific applications that require accurate and reliable non-invasive
segmentation of white matter tracts.
