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.

翻译：我们提出了一种完整的合成模型，能够模拟脑血管树的各种组成部分：脑动脉、分叉点和颅内动脉瘤。通过构建该模型，我们的目标是提供一个大量脑动脉数据集，可供三维卷积神经网络（CNN）用于分割或检测/识别各种血管疾病（如动脉夹层/血栓），甚至可识别脑血管系统的某些部分，如分叉点或动脉瘤。本研究将重点关注颅内动脉瘤（ICA）的检测与分割。脑动脉瘤最常发生在血管树中一个名为Willis环的特定结构上。已有多种研究对ICA进行检测与监测，其中基于深度学习（DL）的方法达到了最佳性能。具体而言，在本工作中，我们提出一个完整的合成三维模型，能够模拟通过磁共振血管成像（MRA）获取的脑部血管系统，尤其是飞行时间（TOF）原理的效果。在多种MRI模态中，MRA-TOF能够提供相对良好的血管成像，且无创（无需注射造影剂）。我们的模型设计可同时模拟动脉几何形状、ICA形态及背景噪声。血管树的几何形状通过三维样条函数插值建模，而背景MRI噪声的统计特性则从MRA采集数据中提取并复现于模型中。本工作详细描述了该合成血管模型，构建了专用于ICA分割与检测的神经网络，并最终深入评估了通过合成模型数据增强所获得的性能提升。