  We hereby present a full synthetic model, able to mimic the various
constituents of the cerebral vascular tree: the cerebral arteries, the
bifurcations and the intracranial aneurysms. By building this model, our goal
was to provide a substantial dataset of brain arteries which could be used by a
3D Convolutional Neural Network (CNN) to either segment or detect/recognize
various vascular diseases (such as artery dissection/thrombosis) or even some
portions of the cerebral vasculature, such as the bifurcations or aneurysms. In
this study, we will particularly focus on Intra-Cranial Aneurysm (ICA)
detection and segmentation. The cerebral aneurysms most often occur on a
particular structure of the vascular tree named the Circle of Willis. Various
studies have been conducted to detect and monitor the ICAs and those based on
Deep Learning (DL) achieve the best performances. Specifically, in this work,
we propose a full synthetic 3D model able to mimic the brain vasculature as
acquired by Magnetic Resonance Angiography (MRA), and more particularly the
Time Of Flight (TOF) principle. Among the various MRI modalities, the MRA-TOF
allows to have a relatively good rendering of the blood vessels and is
non-invasive (no contrast liquid injection). Our model has been designed to
simultaneously mimic the arteries geometry, the ICA shape and the background
noise. The geometry of the vascular tree is modeled thanks to an interpolation
with 3D Spline functions, and the statistical properties of the background MRI
noise is collected from MRA acquisitions and reproduced within the model. In
this work, we thoroughly describe the synthetic vasculature model, we build up
a neural network designed for ICA segmentation and detection, and finally, we
carry out an in-depth evaluation of the performance gap gained thanks to the
synthetic model data augmentation.
