发表机构
Technical University of Munich (TUM); TUM University Hospital; Munich Center for Machine Learning (MCML); Imperial College London（慕尼黑工业大学; 慕尼黑工业大学附属医院; 慕尼黑机器学习中心; 伦敦帝国理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对血管造影纵向图像中图提取与匹配对分割差异敏感的问题，提出先匹配后联合细化的策略，在视网膜血管图实验中实现更高匹配面积且避免碎片化。
AI 中文摘要
血管造影成像的最新进展使得微血管的纵向可视化成为可能。基于血管图的图像处理流程能够解析单个血管水平上的细微时间变化。然而，当前的图提取、细化和匹配策略高度敏感，底层分割图中的微小差异就可能导致血管图显著不同。这些伪影严重阻碍了随时间准确匹配同一受试者的连续血管图的能力。为解决此问题，我们提出了一种先匹配图再联合细化的策略。具体而言，我们在基本图提取后、利用联合信息去除伪凸起并合并连接点之前，进行早期匹配。在复杂的视网膜血管图实验中，我们证明与分别细化或不细化相比，该策略在无图碎片化的情况下实现了更高的匹配面积。
英文摘要
Recent advances in angiographic imaging have enabled longitudinal visualization of the microvasculature. Image processing pipelines based on vessel graphs are able to resolve subtle temporal changes at the level of individual blood vessels. However, current strategies for graph extraction, refinement, and matching are highly sensitive, with even minuscule differences in the underlying segmentation map resulting in substantially different vessel graphs. These artifacts severely inhibit the ability to accurately match sequential vessel graphs of the same subject over time. To address this problem, we propose a strategy that matches graphs before jointly refining them. Specifically, we perform an early matching after basic graph extraction before removing spurious bulges and merging junctions in both graphs using joint information. In experiments with complex retinal vessel graphs, we demonstrate that this strategy results in a higher matched area without graph fragmentation compared to separate or no refinement, respectively.
CommentsAccepted at MICCAI 2026 GRAIL workshop
