Fig. 1. Improved U-Net retinal vessel segmentation algorithm model

Fig. 2. Classic Inception structure

Fig. 3. Inception module

Fig. 4. Schematic diagram of hole convolution under different expansion rates r. (a) r=1;(b) r=2;(c) r=4

Fig. 5. Schematic diagram of cascaded dilated convolution module

Fig. 6. Internal structure of attention mechanism

Fig. 7. DRIVE dataset (from left to right are the original color fundus image, two expert manual segmentation images, and binary mask image)

Fig. 8. Retina image preprocessing. (a) Original image of the DRIVE dataset; (b) pre-processed image

Fig. 9. Local blocky information map of retinal blood vessels. (a) Block information of the DRIVE dataset; (b) standard block information

Fig. 10. Segmentation of experimental results. (a) Original image preprocessing map; (b) image segmentation standard map; (c) experimental result segmentation map

Fig. 11. Partial blood vessel region segmentation diagram. (a) Original color fundus retinal images; (b) locally fundus retinal images; (c) local standard retinal segmentation images; (d) local retinal segmentation result images

Fig. 12. Comparison of evaluation indexes of different algorithms