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.

我们提出了一种完整的合成模型，能够模拟脑血管树的各个组成部分，以及使用三维卷积神经网络（CNN）对各种血管疾病进行分割或检测 / 识别提供了实质性的大脑动脉数据集。在这项研究中，我们特别关注颅内动脉瘤（ICA）的检测和分割，并通过合成血管模型进行了细致的描述、ICA 分割和检测的神经网络构建，并最终对合成模型数据增强所获得的性能差距进行了深入评估。

基于深度神经网络的改进动脉瘤分割和检测的血管合成模型

A vascular synthetic model for improved aneurysm segmentation and  detection via Deep Neural Networks

Magnetic resonance angiography (MRA) is an imaging modality for visualising
blood vessels. It is useful for several diagnostic applications and for
assessing the risk of adverse events such as haemorrhagic stroke (resulting
from the rupture of aneurysms in blood vessels). However, MRAs are not acquired
routinely, hence, an approach to synthesise blood vessel segmentations from
more routinely acquired MR contrasts such as T1 and T2, would be useful. We
present an encoder-decoder model for synthesising segmentations of the main
cerebral arteries in the circle of Willis (CoW) from only T2 MRI. We propose a
two-phase multi-objective learning approach, which captures both global and
local features. It uses learned local attention maps generated by dilating the
segmentation labels, which forces the network to only extract information from
the T2 MRI relevant to synthesising the CoW. Our synthetic vessel segmentations
generated from only T2 MRI achieved a mean Dice score of $0.79 \pm 0.03$ in
testing, compared to state-of-the-art segmentation networks such as transformer
U-Net ($0.71 \pm 0.04$) and nnU-net($0.68 \pm 0.05$), while using only a
fraction of the parameters. The main qualitative difference between our
synthetic vessel segmentations and the comparative models was in the sharper
resolution of the CoW vessel segments, especially in the posterior circulation.

用 T2 磁共振成像合成大脑 Willis 环的主要脑动脉分割的编码器 - 解码器模型，其根据 T2 磁共振成像提取相关信息，与现有模型相比，产生具有更高分辨率的 Willis 环血管分割。