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Subtle changes in white matter (WM) microstructure have been associated withnormal aging and neurodegeneration. To study these associations in more detail,it is highly important that the WM tracts can be accurately and reproduciblycharacterized from brain diffusion MRI. In addition, to enable analysis of WMtracts in large datasets and in clinical practice it is essential to havemethodology that is fast and easy to apply. This work therefore presents a newapproach for WM tract segmentation: Neuro4Neuro, that is capable of directextraction of WM tracts from diffusion tensor images using convolutional neuralnetwork (CNN). This 3D end-to-end method is trained to segment 25 WM tracts inaging individuals from a large population-based study (N=9752, 1.5T MRI). Theproposed method showed good segmentation performance and high reproducibility,i.e., a high spatial agreement (Cohen's kappa, k = 0.72 ~ 0.83) and a lowscan-rescan error in tract-specific diffusion measures (e.g., fractionalanisotropy: error = 1% ~ 5%). The reproducibility of the proposed method washigher than that of a tractography-based segmentation algorithm, while beingorders of magnitude faster (0.5s to segment one tract). In addition, we showedthat the method successfully generalizes to diffusion scans from an externaldementia dataset (N=58, 3T MRI). In two proof-of-principle experiments, weassociated WM microstructure obtained using the proposed method with age in anormal elderly population, and with disease subtypes in a dementia cohort. Inconcordance with the literature, results showed a widespread reduction ofmicrostructural organization with aging and substantial group-wisemicrostructure differences between dementia subtypes. In conclusion, wepresented a highly reproducible and fast method for WM tract segmentation thathas the potential of being used in large-scale studies and clinical practice.

Neuro4Neuro: A neural network approach for neural tract segmentation using large-scale population-based diffusion imaging