发表机构
University of Liverpool（利物浦大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本论文提出结构感知联邦学习框架，通过形状敏感损失、对抗优化及扩散合成数据，提升X射线导管导丝分割精度，在保护隐私下实现多中心协作训练。
AI 中文摘要
血管内手术依赖于在X射线透视引导下对细长器械、导管和导丝的实时操作，其中准确的视觉分析对于手术安全至关重要。基于学习的方法受到结构复杂性、数据稀缺性以及禁止跨机构集中训练的隐私法规的制约。本论文提出了一种结构感知的联邦学习框架，用于导管和导丝分析，并在真实动物和体模数据上评估了四项贡献。引入了一个用于导管插入术分析的基准数据集CathAction，包含超过60万帧带标注的帧和4万个分割掩码。一种形状敏感损失将掩码转换为符号距离图，并在结构特征空间中进行比较，在五个骨干网络上将Dice系数提高了最多2.9个百分点。该方法扩展到带有形状敏感损失的联邦学习中，在异构客户端数据下保持了几何一致性，并在客户端数量从四个扩展到八个时，平均交并比优于联邦平均法最多三个百分点。带有投影梯度下降的联邦学习增加了对抗性优化，在真实动物数据上将平均交并比提高了超过十个百分点。最后，一个结构感知的扩散框架合成了导管和导丝视频序列，将结构监督与域自适应重建目标相结合，在保持视觉保真度的同时，将Frechet视频距离相对于强基线有所降低。将合成序列纳入联邦训练，在数据稀缺情况下将Dice分数从44%提高到51%，并在四个保留站点上均有提升。这些贡献共同推进了隐私保护的导管和导丝分析，支持在不集中患者数据或大量手动标注的情况下进行协作训练。
英文摘要
Endovascular procedures rely on real-time manipulation of thin instruments, catheters and guidewires, under X-ray fluoroscopy guidance, where accurate visual analysis is essential for procedural safety. Learning-based methods are constrained by structural complexity, data scarcity, and privacy regulations precluding centralised training across institutions. This thesis presents a structure-aware federated learning framework for catheter and guidewire analysis, with four contributions evaluated on real-animal and phantom data. A benchmark dataset, CathAction, is introduced for catheterisation analysis, with over 600,000 annotated frames and 40,000 segmentation masks. A shape-sensitive loss transforms masks into signed distance maps compared in a structural feature space, improving Dice coefficient by up to 2.9 points across five backbones. This is extended to federated learning with shape-sensitive loss, preserving geometric consistency under heterogeneous client data and outperforming federated averaging by up to three points in mean intersection-over-union as clients scale from four to eight. Federated learning with projected gradient descent adds adversarial optimisation, raising mean intersection-over-union by over ten points on real-animal data. Finally, a structure-aware diffusion framework synthesises catheter and guidewire video sequences, combining structural supervision with a domain-adaptive reconstruction objective, reducing Frechet video distance over a strong baseline while maintaining visual fidelity. Incorporating synthetic sequences into federated training raises the Dice score from 44 to 51 percent under data scarcity, with gains across four held-out sites. Together, these contributions advance privacy-preserving catheter and guidewire analysis, supporting collaborative training without centralising patient data or large amounts of manual annotation.
CommentsPhD Thesis, University of Liverpool. 163 pages
