用双阶段Transformer学习血管树的向量表示，实现高效精准建模。
Vector Representations of Vessel Trees
- 分两阶段训练：先学血管段几何，再学整体拓扑结构
- 相比3D卷积模型，显存降低显著，支持大规模训练
- 适合医学影像中血管树结构的建模与生成任务
我们提出一种新框架，用于学习树状几何数据（特别是3D血管网络）的向量表示。该方法采用两个顺序训练的基于Transformer的自编码器：第一阶段，血管自编码器通过从每条曲线采样点学习嵌入，捕捉单个血管段的连续几何细节；第二阶段，血管树自编码器利用第一阶段获得的段级嵌入，将血管网络拓扑编码为单一向量表示，并通过递归解码确保重建拓扑为有效树结构。与3D卷积模型相比，该方法显著降低GPU内存占用，支持大规模训练。在2D合成树数据集和3D冠状动脉数据集上的实验表明，该方法在重建保真度、拓扑准确性和潜在空间插值方面表现优异。所提出的可扩展框架VeTTA，能够对医学影像中的解剖树结构实现精确、灵活且拓扑一致的建模。
原文摘要 · Abstract (English)
We introduce a novel framework for learning vector representations of tree-structured geometric data focusing on 3D vascular networks. Our approach employs two sequentially trained Transformer-based autoencoders. In the first stage, the Vessel Autoencoder captures continuous geometric details of individual vessel segments by learning embeddings from sampled points along each curve. In the second stage, the Vessel Tree Autoencoder encodes the topology of the vascular network as a single vector representation, leveraging the segment-level embeddings from the first model. A recursive decoding process ensures that the reconstructed topology is a valid tree structure. Compared to 3D convolutional models, this proposed approach substantially lowers GPU memory requirements, facilitating large-scale training. Experimental results on a 2D synthetic tree dataset and a 3D coronary artery dataset demonstrate superior reconstruction fidelity, accurate topology preservation, and realistic interpolations in latent space. Our scalable framework, named VeTTA, offers precise, flexible, and topologically consistent modeling of anatomical tree structures in medical imaging.
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