标题： TopoTTA：用于管状结构分割的拓扑增强型测试时自适应 显示英文标题

标题： TopoTTA: Topology-Enhanced Test-Time Adaptation for Tubular Structure Segmentation

摘要： 管状结构分割（TSS）对于各种应用非常重要，例如血流动力学分析和路径导航。 尽管在TSS方面取得了显著进展，领域偏移仍然是一个主要挑战，导致在未见过的目标领域中性能下降。 与其他分割任务不同，TSS对领域偏移更加敏感，因为拓扑结构的变化可能会损害分割完整性，而区分前景和背景的局部特征（例如纹理和对比度）的变化可能会进一步破坏拓扑连续性。 为了解决这些挑战，我们提出了 拓扑增强的测试时适应（TopoTTA），这是第一个专门为TSS设计的测试时适应框架。 TopoTTA包括两个阶段：第一阶段使用提出的拓扑元差异卷积（TopoMDCs）来适应跨领域的拓扑差异，这可以在不改变预训练参数的情况下增强拓扑表示；第二阶段通过一种新颖的拓扑困难样本生成（TopoHG）策略，并在生成的伪断裂区域中的困难样本上进行预测对齐来提高拓扑连续性。 在四个场景和十个数据集上的广泛实验表明，TopoTTA在处理拓扑分布偏移方面是有效的，在clDice上平均提高了31.81%。 TopoTTA还可作为适用于基于CNN的TSS模型的即插即用TTA解决方案。 显示英文摘要

摘要： Tubular structure segmentation (TSS) is important for various applications, such as hemodynamic analysis and route navigation. Despite significant progress in TSS, domain shifts remain a major challenge, leading to performance degradation in unseen target domains. Unlike other segmentation tasks, TSS is more sensitive to domain shifts, as changes in topological structures can compromise segmentation integrity, and variations in local features distinguishing foreground from background (e.g., texture and contrast) may further disrupt topological continuity. To address these challenges, we propose Topology-enhanced Test-Time Adaptation (TopoTTA), the first test-time adaptation framework designed specifically for TSS. TopoTTA consists of two stages: Stage 1 adapts models to cross-domain topological discrepancies using the proposed Topological Meta Difference Convolutions (TopoMDCs), which enhance topological representation without altering pre-trained parameters; Stage 2 improves topological continuity by a novel Topology Hard sample Generation (TopoHG) strategy and prediction alignment on hard samples with pseudo-labels in the generated pseudo-break regions. Extensive experiments across four scenarios and ten datasets demonstrate TopoTTA's effectiveness in handling topological distribution shifts, achieving an average improvement of 31.81% in clDice. TopoTTA also serves as a plug-and-play TTA solution for CNN-based TSS models.