Abstract
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.
Abstract (translated)
解剖树在临床诊断和治疗计划中扮演着重要角色。然而，由于其复杂的拓扑结构和几何形状，准确地表示这些结构面临巨大挑战。目前大多数合成血管的方法都是基于规则的，在提供一定程度的控制和变化的同时，却无法捕捉到实际解剖数据的多样性和复杂性。 我们开发了一种递归变分神经网络（Recursive variational Neural Network, RvNN），它充分利用了血管层次组织的特点，并学习低维流形编码分支连接以及描述目标表面的几何特征。经过训练后，RvNN的潜在空间可以被采样以生成新的血管几何形状。 通过利用生成性神经网络的力量，我们能够产生准确且多样化的3D血管模型，这对于医学和外科培训、血流动力学模拟以及其他许多用途来说至关重要。我们的结果非常接近实际数据，在各种数据集（包括动脉瘤）中实现了高度相似的血管半径、长度和曲折度。 据我们所知，这是首次利用该技术来合成血管的工作。
URL
https://arxiv.org/abs/2506.14914
