TopoVST: Toward Topology-fidelitous Vessel Skeleton Tracking

TL;DR

TopoVST introduces a novel topology-aware vessel skeleton tracking method using multi-scale graph neural networks and wave-propagation filtering, significantly improving topological fidelity in vessel delineation.

Contribution

The paper presents a new vessel skeleton tracker that combines multi-scale graph neural networks with a wave-propagation algorithm to enhance topological accuracy and reduce spurious segments.

Findings

Achieves state-of-the-art topological metrics on vessel datasets.

Effectively reduces spurious skeleton segments.

Demonstrates robustness across different vessel geometries.

Abstract

Automatic extraction of vessel skeletons is crucial for many clinical applications. However, achieving topologically faithful delineation of thin vessel skeletons remains highly challenging, primarily due to frequent discontinuities and the presence of spurious skeleton segments. To address these difficulties, we propose TopoVST, a topology-fidelitious vessel skeleton tracker. TopoVST constructs multi-scale sphere graphs to sample the input image and employs graph neural networks to jointly estimate tracking directions and vessel radii. The utilization of multi-scale representations is enhanced through a gating-based feature fusion mechanism, while the issue of class imbalance during training is mitigated by embedding a geometry-aware weighting scheme into the directional loss. In addition, we design a wave-propagation-based skeleton tracking algorithm that explicitly mitigates the generation of spurious skeletons through space-occupancy filtering. We evaluate TopoVST on two vessel datasets with different geometries. Extensive comparisons with state-of-the-art baselines demonstrate that TopoVST achieves competitive performance in both overlapping and topological metrics. Our source code is available at: https://github.com/EndoluminalSurgicalVision-IMR/TopoVST.