Accurate and robust segmentation of cerebral distal small arteries by DVNet with dual contextual path and vascular attention enhancement.

Background: The segmentation of cerebral vasculature on magnetic resonance angiography (MRA) images is crucial for both clinical applications and research. However, existing cerebrovascular segmentation techniques fail to account for the complex geometric and topological features of cerebral vasculature, resulting in suboptimal segmentation of distal small arteries. This study aimed to develop a novel deep vascular network (DVNet) with dual contextual path (DCP) and vascular attention enhancement module (VAEM) to accurately segment the cerebrovasculature. Methods: First, we developed DVNet with DCP and VAEM to accurately segment the cerebral vasculature from the publicly available dataset MR Brain Images of Healthy Volunteers (MIDAS)-I. Second, the segmentation performance of the proposed DVNet was evaluated using the MIDAS-II dataset, followed by further validation using our hospital data. Finally, ablation experiments were performed to evaluate the segmentation performance. Results: Experiments show that the proposed segmentation approach outperforms the previously proposed approaches (U-Net, V-Net, endoplasmic reticulum Net, etc.), with 0.900 Dice and 0.860 mean Intersection over Union (IoU) in the MIDAS-I dataset and 0.715 Dice and 0.713 IoU in the MIDAS-II dataset. External validation revealed good segmentation performance (Dice coefficient: 0.733; IoU: 0.730). Conclusions: Our approach demonstrates that accurate and robust cerebrovascular segmentation is achievable on MRA using DVNet with DCP and VAEM, especially in small distal vessels.