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.

翻译：软脑膜侧支（LMCs）是急性缺血性卒中预后的重要预测因素。现有自动化方法依赖CT血管造影（CTA），但单个LMCs往往因体积过小难以在CTA上分辨，导致这些方法仅限于粗粒度的侧支评分。数字减影血管造影（DSA）能以更高分辨率可视化单个侧支血管，然而目前评估仍依赖主观判断，采用评分者间一致性较差的传统手动分级量表。本文提出一种框架，将侧支检测定义为基于DSA血管图结构的单支血管段分类问题。该混合图-像素架构将拓扑感知的图分支与密集像素分支相结合，在共享节点概率空间中进行融合。在五折交叉验证中，融合模型实现了0.434的PR-AUC值，优于仅基于图（0.403）和仅基于像素（0.362）的基线模型。据我们所知，这是首个实现DSA中LMCs个体化识别的方法，可对每支血管进行精确量化评估。该整合将DSA评估推向客观化方向，为未来单个LMCs的生物标志物发现与模式识别提供支持。