用两阶段生成模型精准控制脑动脉瘤形状，助力血流模拟研究
Two-Stage Generative Model for Intracranial Aneurysm Meshes with Morphological Marker Conditioning
- 分两阶段生成：先编码瘤体形状，再根据形状生成父血管
- 能生成符合临床测量指标的动脉瘤，支持可控形态设计
- 适用于血流动力学研究和个性化医疗模型训练
脑动脉瘤（IA）的网格几何生成对实时预测血流力至关重要，但缺乏大规模图像数据集。现有方法难以捕捉真实动脉瘤特征，忽略瘤体与父血管的关系，且生成不可控。本文提出AneuG，一种基于变分自编码器的两阶段生成模型。第一阶段利用图调和变形（GHD）令牌编码并重建瘤体形状，约束于形态能量统计特性，提升编码精度。第二阶段基于GHD令牌生成父血管中心线并传播横截面。生成过程可进一步控制为特定临床形态参数，有助于理解形状变异与血流动力学影响。代码与实现细节见https://github.com/anonymousaneug/AneuG。
原文摘要 · Abstract (English)
A generative model for the mesh geometry of intracranial aneurysms (IA) is crucial for training networks to predict blood flow forces in real time, which is a key factor affecting disease progression. This need is necessitated by the absence of a large IA image datasets. Existing shape generation methods struggle to capture realistic IA features and ignore the relationship between IA pouches and parent vessels, limiting physiological realism and their generation cannot be controlled to have specific morphological measurements. We propose AneuG, a two-stage Variational Autoencoder (VAE)-based IA mesh generator. In the first stage, AneuG generates low-dimensional Graph Harmonic Deformation (GHD) tokens to encode and reconstruct aneurysm pouch shapes, constrained to morphing energy statistics truths. GHD enables more accurate shape encoding than alternatives. In the second stage, AneuG generates parent vessels conditioned on GHD tokens, by generating vascular centreline and propagating the cross-section. AneuG's IA shape generation can further be conditioned to have specific clinically relevant morphological measurements. This is useful for studies to understand shape variations represented by clinical measurements, and for flow simulation studies to understand effects of specific clinical shape parameters on fluid dynamics. Source code and implementation details are available at https://github.com/anonymousaneug/AneuG.
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