用递归神经网络生成逼真多样的3D血管模型。
Recursive Variational Autoencoders for 3D Blood Vessel Generative Modeling
- 基于递归变分自编码器建模血管层级结构与几何特征
- 生成的血管在半径、长度和扭曲度上接近真实数据
- 适合医学训练与血流模拟，首次应用于血管生成
解剖学树状结构在临床诊断与治疗规划中至关重要，但其复杂的拓扑与几何特性使得精确建模极具挑战。现有血管合成方法多为规则驱动，虽具一定可控性与变异性，却难以捕捉真实解剖数据的多样性与复杂性。本文提出一种递归变分神经网络（RvNN），充分挖掘血管的层次组织特性，学习编码分支连接关系及目标表面几何特征的低维流形。训练完成后，可从RvNN隐空间采样生成新血管几何体。借助生成式神经网络能力，所生成的3D血管模型兼具高准确度与多样性，适用于医学与手术培训、血流动力学模拟等场景。实验表明，生成结果在多种数据集（包括动脉瘤数据）上与真实数据在血管半径、长度与扭曲度方面高度相似。据我们所知，这是首个将该技术用于血管生成的研究。
原文摘要 · Abstract (English)
Anatomical trees play an important role in clinical diagnosis and treatment planning. Yet, accurately representing these structures poses significant challenges owing to their intricate and varied topology and geometry. Most existing methods to synthesize vasculature are rule based, and despite providing some degree of control and variation in the structures produced, they fail to capture the diversity and complexity of actual anatomical data. We developed a Recursive variational Neural Network (RvNN) that fully exploits the hierarchical organization of the vessel and learns a low-dimensional manifold encoding branch connectivity along with geometry features describing the target surface. After training, the RvNN latent space can be sampled to generate new vessel geometries. By leveraging the power of generative neural networks, we generate 3D models of blood vessels that are both accurate and diverse, which is crucial for medical and surgical training, hemodynamic simulations, and many other purposes. These results closely resemble real data, achieving high similarity in vessel radii, length, and tortuosity across various datasets, including those with aneurysms. To the best of our knowledge, this work is the first to utilize this technique for synthesizing blood vessels.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。