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

2026-03-19

AI Summary

High-resolution 3D modeling of vascular structures faces dual challenges of geometric complexity and computational efficiency. This work proposes the first graph tokenization framework tailored for tubular anatomical structures, leveraging centerline points and pseudo-radii to construct a neural implicit representation that encodes both geometric and topological information into compact, generalizable latent tokens. The approach enables efficient solutions to inverse problems such as reconstruction, generation, and link prediction. Extensive experiments on pulmonary airways, pulmonary vasculature, and cerebral vasculature demonstrate the method’s strong cross-anatomical generalization capability and anatomically plausible generation performance.

Technology Category

• Machine Learning: Deep Generative Models & Autoencoders
• Computer Vision: 3D Computer Vision
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications

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.

Problem

• spatial graphs
• vessel-like structures
• 3D biomedical representation
• computational complexity
• anatomical modeling

Innovation

• VesselTok
• spatial graph representation
• neural implicit representation
• centerline-based modeling
• generative biomedical modeling