The earlier studies on brain vasculature semantic segmentation used classical image analysis methods to extract the vascular tree from images. Nowadays, deep learning methods are widely exploited for various image analysis tasks. One of the strong restrictions when dealing with neural networks in the framework of semantic segmentation is the need to dispose of a ground truth segmentation dataset, on which the task will be learned. It may be cumbersome to manually segment the arteries in a 3D volumes (MRA-TOF typically). In this work, we aim to tackle the vascular tree segmentation from a new perspective. Our objective is to build an image dataset from mouse vasculatures acquired using CT-Scans, and enhance these vasculatures in such a way to precisely mimic the statistical properties of the human brain. The segmentation of mouse images is easily automatized thanks to their specific acquisition modality. Thus, such a framework allows to generate the data necessary for the training of a Convolutional Neural Network - i.e. the enhanced mouse images and there corresponding ground truth segmentation - without requiring any manual segmentation procedure. However, in order to generate an image dataset having consistent properties (strong resemblance with MRA images), we have to ensure that the statistical properties of the enhanced mouse images do match correctly the human MRA acquisitions. In this work, we evaluate at length the similarities between the human arteries as acquired on MRA-TOF and the "humanized" mouse arteries produced by our model. Finally, once the model duly validated, we experiment its applicability with a Convolutional Neural Network.