VesselTok: Tokenizing Vessel-like 3D Biomedical Graph Representations for Reconstruction and Generation
This work addresses computational bottlenecks in representing anatomical structures for clinical and biomedical research, though it appears incremental as it builds on existing graph and implicit representation methods.
The authors tackled the computational challenges of modeling high-resolution 3D biomedical graphs like blood vessels by proposing VesselTok, a framework that learns latent tokens from parametric shapes, and demonstrated its ability to encode complex topologies, generalize to unseen anatomies, support generative modeling, and transfer to downstream tasks.
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.