首次实现DSA影像中脑膜侧支血管的精准识别与量化
Leptomeningeal Collateral Detection on DSA via Vessel-Graph Neural Networks
- 将DSA血管图谱化，用图神经网络分类每段血管
- 融合图结构与像素信息，检测准确率提升至PR-AUC 0.434
- 适合需要精准评估脑侧支循环的卒中研究与临床决策
脑膜侧支循环（LMCs）是急性缺血性卒中的重要预后因素。现有自动化方法依赖CT血管造影（CTA），但单个侧支血管常因过小而无法分辨，仅能进行粗略评分。数字减影血管造影（DSA）可实现更高分辨率的个体化侧支显影，但当前评估仍依赖主观的人工分级，存在严重评价者间差异。本文提出一种新框架，将侧支检测建模为基于DSA生成的血管图谱中对单个血管段的分类问题。采用混合图-像素架构，结合拓扑感知图分支与密集像素分支，在共享节点概率空间中融合特征。在五折交叉验证下，融合模型取得0.434的PR-AUC，优于仅图分支（0.403）和仅像素分支（0.362）的基线。据我们所知，这是首个实现DSA中个体化脑膜侧支识别的方法，支持按血管段进行精确定量分析。该集成方法推动了DSA评估向客观化转变，有助于未来个体侧支的生物标志物与模式发现。
原文摘要 · Abstract (English)
Leptomeningeal collaterals (LMCs) are an important prognostic factor in acute ischemic stroke. Existing automated methods rely on CT angiography (CTA), but individual LMCs are often too small to be resolved on CTA, limiting these methods to coarse collateral scoring. Digital subtraction angiography (DSA) visualizes individual collaterals at superior resolution, yet current assessment remains subjective, relying on manual grading scales that suffer from poor inter-rater agreement. We present a framework that formulates collateral detection as the classification of individual vessel segments on a graph derived from DSA. A hybrid graph-pixel architecture combines a topology-aware graph branch with a dense pixel branch, fused in a shared node-probability space. In a five-fold cross-validation setting, the fused model achieves a PR-AUC of 0.434, outperforming the graph-only (0.403) and pixel-only (0.362) baselines. To our knowledge, this is the first method to enable the individualization of LMCs in DSA, allowing for precise per-vessel quantitative assessment. This integration shifts DSA assessment toward objective evaluation, supporting future biomarker and pattern discovery for individual LMCs.
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