Abstract: Aiming at the problem of missing and disconnected capillary segmentation in the retinal vascular segmentation task, from the perspective of maximizing the use of retinal vascular feature information, by adding the global structure information and retinal blood vessels boundary information, based on the U-shaped network, a dynamic graph convolution for retinal vascular segmentation model assisted by boundary attention is proposed. The dynamic graph convolution is first embedded into the U-shaped network to form a multi-scale structure, which improves the ability of the model to obtain the global structural information, and thus improving the segmentation quality. Then, the boundary attention network is utilized to assist the model to increase the attention to the boundary information, and further improve the segmentation performance. The proposed algorithm is tested on three retinal image datasets, DRIVE, CHASEDB1, and STARE, and good segmentation results are obtained. The experimental results show that the model can better distinguish the noise and capillary, and segment retinal blood vessels with more complete structure, which has generalization and robustness.