# Multi-Path Recurrent U-Net Segmentation of Retinal Fundus Image
## 4 Experiment
### Comparison of Model Improvement Results
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Accuracy** & **Specificity** & **Sensitivity** & **Dice** \\ \hline MPU & 0.9980/0.0012 & 0.9985/0.0009 & **0.9838/0.0220** & 0.9690/0.0168 \\ MPR & 0.9983/0.0011 & 0.9989/0.0008 & 0.9810/0.0222 & 0.9726/0.0138 \\ MPAR & **0.9985/0.0011** & **0.9990/0.0009** & 0.9811/0.0215 & **0.9740/0.0145** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of optic model changes based on the Drishti-GS1 dataset. Figure 5: Comparison of segmented images of each model in the Drishti-GS1 dataset. (**a**) original image; (**b**) is a partial cut of the original image; (**c**) Disc; (**d**) is a partial cut of the Disc; (**e**) Cup; (**f**) is a partial cut of the Cup; (**g**) Optic; (**h**) is a partial cut of the Optic. Figure 6: Box plot of BLE statistics for each model of the Drishti-GS1 dataset. This paper compares the receiver operating characteristic curve (ROC) curves of different models in the optic cup and joint optic cup segmentation, as shown in Figure 7. The MPR model has an increase in the area under the curve (AUC) of the optic disc by 0.0441 compared with the MPU model. The MUC model increased the AUC for the optic cup by 0.01 compared to the MPR model. The AUC of the MPAR model for joint optic disc segmentation was 0.9957. In order to further illustrate the effectiveness of this method, this paper conducts a generalization verification on the DRIVE dataset. Table 6 shows the segmentation results of different models, which can show the effectiveness of the multipath U-Net in retinal vessel segmentation. It can also be seen that it is reasonable to combine convolutional neural networks and recurrent neural networks. At the same time, the results also show that the addition of an attention gate to the recurrent block in this paper also improves the performance of blood vessel segmentation. Figure 8 is an effect diagram of the blood vessel segmentation by each model. As shown in the figure, the MPAR model can effectively segment the small peripheral blood vessels effectively. and at the same time, it can more completely segment the blood vessels, reducing the problem of blood vessel rupture during segmentation. Figure 9 shows the ROC curve of each model on the DRIVE data set. Comparing the MPA model and the MPU model, the AUC area increased by 0.0034, and the MPAR model AUC area was 0.9754. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Accuracy** & **Specificity** & **Sensitivity** & **F1** \\ \hline MPU & 0.9564 & **0.9835** & 0.8022 & 0.8196 \\ MPR & 0.9599 & 0.9823 & 0.8145 & 0.8207 \\ MPAR & **0.9642** & 0.9820 & **0.8173** & **0.8275** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of model changes based on the DRIVE dataset. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Disc** & **Cup** \\ \hline MPU & 5.832/3.301 & 20.584/16.697 \\ MPR & 5.206/2.876 & 15.087/12.238 \\ MPAR & **4.765/2.933** & **14.342/10.169** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Boundary distance localization error (BLE) comparison of model changes based on the Drishti-GS1 dataset. Figure 7: Comparison of receiver operating characteristic (ROC) curves of each model in the Drishti-GS1 dataset. (**a**) Cup; (**b**) Disc; (**c**) Optic.