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.

翻译：血管骨架的自动提取对于众多临床应用至关重要。然而，实现细血管骨架的拓扑保真性描绘仍然极具挑战性，这主要源于频繁的间断和虚假骨架片段的存在。为解决这些难题，我们提出了TopoVST，一种拓扑保真性血管骨架追踪器。TopoVST构建多尺度球图以对输入图像进行采样，并利用图神经网络联合估计追踪方向和血管半径。通过基于门控的特征融合机制增强了多尺度表征的利用，同时通过将几何感知加权方案嵌入方向损失中，缓解了训练期间的类别不平衡问题。此外，我们设计了一种基于波传播的骨架追踪算法，该算法通过空间占用滤波显式地抑制虚假骨架的生成。我们在两个具有不同几何形状的血管数据集上评估了TopoVST。与最先进基线的广泛比较表明，TopoVST在重叠度和拓扑度量上均取得了具有竞争力的性能。我们的源代码位于：https://github.com/EndoluminalSurgicalVision-IMR/TopoVST。