We present a data-driven generative framework for synthesizing blood vessel 3D geometry. This is a challenging task due to the complexity of vascular systems, which are highly variating in shape, size, and structure. Existing model-based methods provide some degree of control and variation in the structures produced, but fail to capture the diversity of actual anatomical data. We developed VesselVAE, a recursive variational Neural Network 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 VesselVAE latent space can be sampled to generate new vessel geometries. To the best of our knowledge, this work is the first to utilize this technique for synthesizing blood vessels. We achieve similarities of synthetic and real data for radius (.97), length (.95), and tortuosity (.96). By leveraging the power of deep 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.

翻译：我们提出了一种数据驱动的生成框架，用于合成血管三维几何结构。由于血管系统在形态、尺寸和结构上具有高度变异性，这是一项具有挑战性的任务。现有的基于模型的方法虽能在生成的结构中提供一定程度的可控性和变异性，但未能捕捉真实解剖数据的多样性。我们开发了VesselVAE——一种递归变分神经网络，该网络充分利用血管的层次化组织结构，学习一个描述分支连通性及目标表面几何特征的低维流形编码。训练完成后，可对VesselVAE潜空间进行采样以生成新的血管几何结构。据我们所知，本研究是首次运用该技术合成血管。我们在半径（0.97）、长度（0.95）和迂曲度（0.96）指标上实现了合成数据与真实数据的相似度。通过利用深度神经网络的强大能力，我们生成的血管三维模型兼具准确性与多样性，这对医学与外科训练、血流动力学模拟及其他诸多应用至关重要。