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

Abstract

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.