Temporally Consistent Graph Extraction and Matching for Longitudinal Angiographic Images

2026-09-15

AI Summary

本文针对血管图像中图匹配不一致的问题，提出了一种先匹配后联合精炼的策略，以提高纵向血管图像的匹配精度和一致性。

Abstract

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.

Problem

• graph extraction
• vessel matching
• longitudinal angiographic images

Innovation

• graph matching
• joint refinement
• longitudinal angiographic images
• temporal consistency