TopoVST: Toward Topology-fidelitous Vessel Skeleton Tracking

2026-03-16

AI Summary

This work addresses topological distortions in vascular skeleton extraction—particularly prevalent in fine vessel reconstruction due to fragmentation and artifacts—by proposing a high-fidelity automatic method. It constructs a geometry-aware representation via multi-scale spherical patch sampling and employs a graph neural network to jointly predict vessel tracing directions and radii. A multi-scale feature gating fusion mechanism and a geometry-aware directional loss weighting strategy are introduced to enhance accuracy. Furthermore, a wave propagation–based skeleton tracing algorithm, combined with spatial occupancy filtering, explicitly enforces topological consistency. Evaluated on two vascular datasets with distinct geometric structures, the proposed method achieves state-of-the-art performance, consistently outperforming existing approaches in both overlap and topological metrics.

Technology Category

• Computer Vision: Segmentation
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Machine Learning: Graph-based Machine Learning

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies

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.

Problem

• vessel skeleton
• topology fidelity
• skeleton tracking
• spurious segments
• discontinuities

Innovation

• topology-fidelitous
• graph neural networks
• multi-scale sphere graphs
• wave-propagation-based tracking
• geometry-aware weighting