  Subtle changes in white matter (WM) microstructure have been associated with
normal aging and neurodegeneration. To study these associations in more detail,
it is highly important that the WM tracts can be accurately and reproducibly
characterized from brain diffusion MRI. In addition, to enable analysis of WM
tracts in large datasets and in clinical practice it is essential to have
methodology that is fast and easy to apply. This work therefore presents a new
approach for WM tract segmentation: Neuro4Neuro, that is capable of direct
extraction of WM tracts from diffusion tensor images using convolutional neural
network (CNN). This 3D end-to-end method is trained to segment 25 WM tracts in
aging individuals from a large population-based study (N=9752, 1.5T MRI). The
proposed method showed good segmentation performance and high reproducibility,
i.e., a high spatial agreement (Cohen's kappa, k = 0.72 ~ 0.83) and a low
scan-rescan error in tract-specific diffusion measures (e.g., fractional
anisotropy: error = 1% ~ 5%). The reproducibility of the proposed method was
higher than that of a tractography-based segmentation algorithm, while being
orders of magnitude faster (0.5s to segment one tract). In addition, we showed
that the method successfully generalizes to diffusion scans from an external
dementia dataset (N=58, 3T MRI). In two proof-of-principle experiments, we
associated WM microstructure obtained using the proposed method with age in a
normal elderly population, and with disease subtypes in a dementia cohort. In
concordance with the literature, results showed a widespread reduction of
microstructural organization with aging and substantial group-wise
microstructure differences between dementia subtypes. In conclusion, we
presented a highly reproducible and fast method for WM tract segmentation that
has the potential of being used in large-scale studies and clinical practice.
