  Cerebral X-ray digital subtraction angiography (DSA) is a widely used imaging
technique in patients with neurovascular disease, allowing for vessel and flow
visualization with high spatio-temporal resolution. Automatic artery-vein
segmentation in DSA plays a fundamental role in vascular analysis with
quantitative biomarker extraction, facilitating a wide range of clinical
applications. The widely adopted U-Net applied on static DSA frames often
struggles with disentangling vessels from subtraction artifacts. Further, it
falls short in effectively separating arteries and veins as it disregards the
temporal perspectives inherent in DSA. To address these limitations, we propose
to simultaneously leverage spatial vasculature and temporal cerebral flow
characteristics to segment arteries and veins in DSA. The proposed network,
coined CAVE, encodes a 2D+time DSA series using spatial modules, aggregates all
the features using temporal modules, and decodes it into 2D segmentation maps.
On a large multi-center clinical dataset, CAVE achieves a vessel segmentation
Dice of 0.84 ($\pm$0.04) and an artery-vein segmentation Dice of 0.79
($\pm$0.06). CAVE surpasses traditional Frangi-based K-means clustering
(P<0.001) and U-Net (P<0.001) by a significant margin, demonstrating the
advantages of harvesting spatio-temporal features. This study represents the
first investigation into automatic artery-vein segmentation in DSA using deep
learning. The code is publicly available at
https://github.com/RuishengSu/CAVE_DSA.
