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

Spatial graphs provide a lightweight and elegant represen-tation of curvilinear anatomical structures such as blood vessels, lungairways, and neuronal networks. Accurately modeling these graphs iscrucial in clinical and (bio-)medical research. However, the high spatialresolution of large networks drastically increases their complexity, result-ing in significant computational challenges. In this work, we aim to tacklethese challenges by proposing VesselTok, a framework that approachesspatially dense graphs from a parametric shape perspective to learn la-tent representations (tokens). VesselTok leverages centerline points witha pseudo radius to effectively encode tubular geometry. Specifically, welearn a novel latent representation conditioned on centerline points to en-code neural implicit representations of vessel-like, tubular structures. Wedemonstrate VesselTok’s performance across diverse anatomies, includ-ing lung airways, lung vessels, and brain vessels, highlighting its ability torobustly encode complex topologies. To prove the effectiveness of Vessel-Tok’s learned latent representations, we show that they (i) generalize tounseen anatomies, (ii) support generative modeling of plausible anatom-ical graphs, and (iii) transfer effectively to downstream inverse problems,such as link prediction.