# Research on Retinal Vessel Segmentation Algorithm Based on a Modified U-Shaped Network
## 3 Experiment and Results
### Comparisons  
#### 3.5.1 Quantitative Analysis  
Based on the experimental data in Table 3, the following observations can be made:  
1. When comparing U-Net on the DRIVE dataset with the introduction of MRCM and SKU, there are improvements in _Acc_, _Sen_, _Spe_, and _AUC_ by 0.0137/0.0149, 0.0423/0.0488, 0.0020/\(-\)0.002, and 0.0014/0.0062 respectively. Similar performance improvements were observed in other datasets. These results indicate that the strategy of utilizing multi-scale residual modules and the selective kernel unit efficiently learns and locates multi-scale features of blood vessels. 2. The introduction of the residual attention module resulted in increased _Acc_, _Sen_, _Spe_, and _AUC_ on the STARE dataset by 0.0122, 0.0443, \(-\)0.0010, and 0.0002 respectively. These findings suggest that the residual attention module is particularly effective in capturing microvessels. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multirow{2}{*}{**Year**} & \multicolumn{8}{c}{**DRIVE**} & \multicolumn{8}{c}{**STARE**} \\ \cline{2-13}  & & _Acc_ & _Sen_ & _Spe_ & _AUC_ & _Mcc_ & _Acc_ & _Sen_ & _Spe_ & _AUC_ & _Mcc_ \\ \hline U-Net [13] & 2015 & 0.9546 & 0.7554 & 0.9836 & 0.9766 & 0.7857 & 0.9622 & 0.7469 & 0.9876 & 0.9810 & 0.7889 \\ Zhou et al. [49] & 2017 & 0.9469 & 0.8078 & 0.9674 & - & 0.7656 & 0.9585 & 0.8065 & 0.9761 & - & 0.7830 \\ DeepLabv3-[14] & 2018 & 0.9526 & 0.7411 & 0.9694 & 0.9702 & 0.6729 & 0.9562 & 0.7402 & 0.9706 & 0.9764 & 0.6580 \\ R2U-Net [50] & 2018 & 0.9553 & 0.7735 & 0.9818 & 0.9784 & - & 0.9632 & 0.7944 & 0.9832 & 0.9819 & - \\ HCF [51] & 2019 & 0.9753 & 0.8176 & 0.9709 & - & 0.7659 & **0.9751** & 0.8239 & 0.9749 & - & 0.7818 \\ SA-UNet [52] & 2021 & 0.9583 & 0.7962 & 0.9781 & 0.9643 & - & 0.9642 & 0.8212 & 0.9722 & 0.9769 & - \\ EEA Unet [53] & 2021 & 0.9577 & 0.7918 & 0.9708 & - & 0.7115 & 0.9445 & 0.8021 & 0.9561 & - & 0.7115 \\ Att U-Net [27] & 2022 & 0.9531 & 0.7635 & **0.9840** & 0.9791 & 0.7963 & 0.9698 & 0.7695 & 0.9887 & 0.9815 & **0.8021** \\ [PERSON] et al. [54] & 2022 & **0.9701** & 0.7719 & 0.9799 & 0.8895 & 0.7399 & 0.9691 & 0.6912 & **0.9911** & 0. Our MU-Net showed improvements in _Acc, Sen, Spe_, and _AUC_ of 0.0163, 0.0908, 0.0039, and 0.0099 respectively when compared to the U-Net baseline on the CHASE_DB1 dataset. In the quantitative analysis, U-Net demonstrated good accuracy in retinal blood vessel segmentation. However, it had low sensitivity, indicating limitations in accurately extracting blood vessels. This is mainly attributed to the weak feature extraction ability of traditional convolution and U-Net susceptibility to image contrast and noise, which affects its precise blood vessel extraction, leading to vessel rupture and inadequate segmentation. To address these issues, we proposed a solution that enhances the extraction of multi-scale feature information, locates the receptive field matching the blood vessel scale at the skip connection, and utilizes channel attention to extract key information between channels. This improved U-Net encoder-decoder structure resulted in better prediction results compared to the U-Net baseline. In addition, this study compared the performance of MU Net with other CNN methods, including SA-UNet, EEA Unet, and several other techniques, in retinal vessel segmentation. On the DRIVE dataset, MU-Net achieved the highest _Sen, AUC_, and _Mcc_ values of 0.8197, 0.9853, and 0.8051, respectively. Similarly, on the CHASE_DB1 dataset, MU-Net obtained the highest _Acc, AUC_, and _Mcc_ values of 0.9752, 0.9860, and 0.7960, respectively. Although the STARE dataset did not yield the highest performance metric, MU-Net still demonstrated competitive results with the second highest _Acc, Sen_, and _Mcc_ values compared to the best performing methods (HCF, Liang, and EEA Unet, respectively). Overall, MU-Net outperforms these CNN methods in terms of quantitative performance. Our proposed method also has some limitations.