VFA-Net3D: A zero-shot vascular flow-guided 3D network for brain vessel segmentation in acute ischemic stroke

Timely intervention in acute ischemic stroke (AIS) is critical, with approximately 1.9 million neurons lost per minute. Clinical time-of-flight magnetic resonance angiography (TOF-MRA) protocols, designed for rapid acquisition within a few minutes, introduce substantial domain shifts compared to high-resolution research datasets. These include reduced resolution, a lower signal-to-noise ratio, partial-volume effects, and motion artifacts, which are compounded by stroke-specific vascular abnormalities. Conventional segmentation models often fail under such conditions due to limited robustness to domain variability. We propose Vascular Flow-Attention Network (VFA-Net), a fully 3D deep neural network designed for zero-shot vessel segmentation in AIS, enabling knowledge transfer from annotated healthy TOF-MRA scans without retraining on pathological or low-quality clinical data. The architecture integrates five novel modules: (1) Flow-Pattern Attention for vascular continuity; (2) Multi-Scale Context Aggregation using dilated attention; (3) Vascular Flow Feature Refinement for adaptive attention enhancement; (4) Boundary-Guided Skip for precise boundary delineation; and (5) Flow-Refined Up-sampling to recover fine vessel details. The proposed model, trained exclusively on healthy TOF-MRA scans from four vendors (1.5 T and 3 T), was evaluated across three experimental configurations and significantly outperformed state-of-the-art models. By explicitly encoding vascular domain knowledge (continuity, boundaries, and flow topology) into its architecture, VFA-Net3D functions as a knowledge-guided system, enabling robust zero-shot generalization across clinical domains. VFA-Net3D presents a robust and clinically deployable solution for AIS vessel segmentation, supporting faster and more accurate diagnosis and treatment planning.