Dual coordinate attention (DCA) network for accurate cerebral vascular endothelium segmentation in OCT images

Accurate cerebral vascular endothelium segmentation in Optical Coherence Tomography (OCT) images is crucial for cerebrovascular disease assessment, yet remains challenging due to the extreme thinness of endothelial structures and the scarcity of high-quality annotations. In this work, we make two key contributions. First, we construct a high-quality cerebral vascular OCT dataset with meticulous manual annotations provided by experienced experts, offering a reliable foundation for supervised learning and quantitative evaluation. Second, we propose a novel segmentation framework based on a Dual Coordinate Attention (DCA) mechanism, which explicitly integrates Cartesian and polar coordinate representations to capture complementary structural cues of vascular endothelium. Extensive experiments demonstrate that the proposed DCA-based network consistently outperforms representative baseline models in terms of Dice and HD95. Ablation studies further validate the effectiveness of the DCA module and identify its optimal deployment strategy. Overall, this work provides a robust automated solution for cerebral vascular endothelium segmentation in OCT images, with potential value for cerebrovascular research and clinical assessment.