Abstract: Abstract： Due to some problems such as uneven distribution of blood vessel structure， inconsistent thickness， and poor contrast of blood vessel boundary， the image segmentation effect is not good， which cannot meet the needs of practical clinical assistance. To address the problem of breakage of small vessels and poor segmentation of low-contrast vessels， a CA module was integrated into the down-sampling process based on U-Net. Additiondly， to solve the problem of insufficient feature fusion in the original model， Res2NetBlock module was introduced into the model. Finally， a cascade void convolution module is added at the bottom of the model to enhance the receptive field， thereby improving the network’s spatial scale information and the contextual feature perception ability. So the segmentation task achieves better performance. Experiments on DRIVE， CHASEDB1 and self-made Dataset100 datasets show that the accuracy rates are 96.90%， 97.83% and 94.24%， respectively. The AUC is 98.84%， 98.98%， and 97.41%. Compared with U-Net and other mainstream methods， the sensitivity and accuracy are improved， indicating that the vessel segmentation method in this paper has the ability to capture complex features and has higher superiority.

Key words: Key words： fundus data augmentation, vascular segmentation, improved U-Net, attention mechanism, feature fusion, segmentation algorithm