VesselTok: Tokenizing Vessel-like 3D Biomedical Graph Representations for Reconstruction and Generation

Abstract

Spatial graphs provide a lightweight and elegant representation of curvilinear anatomical structures such as blood vessels, lung airways, and neuronal networks. Accurately modeling these graphs is crucial in clinical and (bio-)medical research. However, the high spatial resolution of large networks drastically increases their complexity, resulting in significant computational challenges. In this work, we aim to tackle these challenges by proposing VesselTok, a framework that approaches spatially dense graphs from a parametric shape perspective to learn latent representations (tokens). VesselTok leverages centerline points with a pseudo radius to effectively encode tubular geometry. Specifically, we learn a novel latent representation conditioned on centerline points to encode neural implicit representations of vessel-like, tubular structures. We demonstrate VesselTok's performance across diverse anatomies, including lung airways, lung vessels, and brain vessels, highlighting its ability to robustly encode complex topologies. To prove the effectiveness of VesselTok's learnt latent representations, we show that they (i) generalize to unseen anatomies, (ii) support generative modeling of plausible anatomical graphs, and (iii) transfer effectively to downstream inverse problems, such as link prediction.

Figures (14)

• Figure 1: VesselTok provides expressive latent representations $\mathbf{Z}$ which can be effectively leveraged to several downstream tasks, including generalization to unseen anatomies, generative modeling (, sample additional graphs), and inverse problems (, repair of incomplete structures).
• Figure 2: Architectural overview of VesselTok. VesselTok consists of an encoder $\mathcal{T}$, which extracts features from a pre-processed centerline point cloud $\mathbf{P}$ to generate a continuous, expressive, and compressed latent token $\mathbf{Z}$. A decoder $\mathcal{D}$ subsequently reconstructs the graph occupancy field $\Tilde{\phi}_\theta$ from $\mathbf{Z}$ via cross-attention to output query points $\mathbf{Q}_{\text{out}}$. The final graph is subsequently reconstructed from $\Tilde{\phi}_\theta$.
• Figure 3: Qualitative results for the graph reconstruction task. We find that VesselTok demonstrates superior reconstruction capabilities.
• Figure 4: Qualitative results for the graph reconstruction task of previously unseen domains. This demonstrates VesselTok's strong prior, resulting in robust reconstructions.
• Figure 5: Qualitative results for conditional generation. VesselTok consistently generates more realistic vessels than previous methods.