# Multi-Path Recurrent U-Net Segmentation of Retinal Fundus Image
## 4 Experiment
### Comparison of Model Improvement Results  
In order to verify the effectiveness of the multi-path U-Net recurrent block (RU) and attention recurrent block (ARU) proposed in this paper, experiments were performed on the Drishti-GS1 dataset and DRIVE dataset. The indicators in the following tables are averages and standard deviations. MPU stands for multi-path U-Net, MPR stands for multi-path recurrent U-Net, and MPAR stands for multi-path recurrent U-Net with an attention gate added to the recurrent block. In this paper, Drishti-GS1 compares the results of the segmentation of the optic discs of each model, as shown in Tables 1-3. It can be seen that the performance of the MPR model with the recurrent block is improved compared to the basic MPU; in particular, for the \(Cup_{Dice}\) indicator, the MPR model is 3.44% higher than the MPU model, which shows that combining the convolutional neural network and the recurrent neural network is significant for the OC segmentation. To further prove the improved performance of the model, we propose a hypothesis: the MPAR model performs better than the MPU model and MPA model. We conducted P value analysis on Dice, and the results are shown in Table 4. MPAR:MPU represents the comparison between the MPAR model and the MPU model, and MPAR:MPR represents the comparison between the MPAR model and the MPU model. In the table, MPAR:MPU has a \(P_{value}\) < 0.05 for OC and OD, indicating that the MPAR model and the MPU model have statistical significance; that is, the MPAR model performs better than the MPU model. In the table, MPAR:MPR has a \(P_{value}\) < 0.05 for OD, indicating that the MPAR model performs better in OD segmentation than the MPR model. Figure 5 shows the result of segmentation of the OC by different models, and the OC region is enlarged and displayed. The first line of the original image corresponds to the true label of the OD and the corresponding enlarged image, and the remaining lines are the segmentation results images of different models. It can be seen from the enlarged image that the contour of the MPAR model is more consistent when the OD is segmented, but the segmentation contour of the OC is insufficient, and the MPAR model is better at segmenting the OC. Table 5 shows the BLE errors of the segmentation results of each model. As can be seen from the table, the results of the MPAR model are better than the MPU model and the MPR model. Especially for the video cup, comparing MPAR with MPU, the BLE error is reduced by 6.242 pixels, which also shows that the model in this paper can segment the video cup more accurately. BLE error plays a crucial role in the evaluation of the model. This paper shows that the BLE was 360\({}^{\circ}\) (each 15\({}^{\circ}\)). Figure 6 shows the average error, median, maximum and minimum error, and quartile box of each test picture in the same orientation. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Accuracy** & **Specificity** & **Sensitivity** & **Dice** \\ \hline MPU & 0.9935/0.0037 & 0.9971/0.0024 & 0.7849/0.1558 & 0.8499/0.1194 \\ MPR & 0.9945/0.0030 & 0.9972/0.0019 & 0.8363/0.1507 & 0.8843/0.0837 \\ MPAR & **0.9950/0.0022** & **0.9981/0.0022** & **0.9365/0.0659** & **0.8921/0.0793** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of optic cup (OC) model changes based on the Drishti-GS1 dataset. MPU: multi-path U-Net; MPR: multi-path recurrent U-Net with attention gate. \begin{table}
\begin{tabular}{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 4: Comparison of \(P_{value}\) analysis results based on Dice.