Leptomeningeal Collateral Detection on DSA via Vessel-Graph Neural Networks
For clinicians assessing acute ischemic stroke, this method provides objective, individualized LMC detection in DSA, replacing subjective manual grading with poor inter-rater agreement.
The paper presents a vessel-graph neural network framework for detecting leptomeningeal collaterals (LMCs) in digital subtraction angiography (DSA), achieving a PR-AUC of 0.434, outperforming graph-only (0.403) and pixel-only (0.362) baselines. This is the first method to enable per-vessel LMC detection in DSA.
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.