Segmentation of the arteries and veins of the cerebral vasculature is important for improved visualization and for the detection of vascular related pathologies including arterio-venous malformations. We propose a three dimensional fully convolutational neural network (CNN), with Time-to-Signal images as input, extended with the distance to the center of gravity of the brain as spatial feature integrated at the abstract level of the CNN. The method is trained and validated on 6 and tested on 4 4D CT patient imaging data. The reference standard was acquired by manual annotations by an experienced observer. Quantitative evaluation shows a mean Dice similarity coefficient of 0.936 +- 0.027 and 0.973 +- 0.012, a mean absolute volume di
erence of 4.36 +- 5.47 % and 1.79 +- 2.26 % for artery and vein respectively and an overall accuracy of 0.962 +- 0.017. Average calculation time per volume on the test set was approximately one minute. Our method shows promising results and enables fast and accurate segmentation of arteries and veins in full 4D CT imaging data.