## 4 Discussion  
In terms of diverse edemas in multi-regions, Figure 16 shows the segmentation effect of the proposed method and ReLayNet on the one B-scan image. Figure 16a is the original OCT image, Figure 16b corresponds to the 13 annotated edema regions from expert 1, Figure 16c corresponds to the 11 annotated edema regions from expert 2, Figure 16d shows the only seven predicted edema regions by the ReLayNet, and Figure 16e shows the 11 predicted edema regions by the proposed method. Compared with ReLayNet, the proposed method had obvious advantages in the segmentation of multi-regions edema. It could better identify and segment diverse edema regions, and the merged situation of edema regions was reduced. Figure 16: Figures of edema segmentation results in multi-regions with the expert 1 annotations as the ground truth: (**a**) original image; (**b**) expert 1 annotations; (**c**) expert 2 annotations; (**d**) ReLayNet predictions; (**e**) proposed method predictions. Figure 15: Comparison of ROC curves. For diverse edemas in the multi-region, by the above 50 B-scan test images, Table 5 shows the average absolute error of the number for edema regions between expert 1 and the expert 2, expert 1 and ReLayNet, and expert 1 and the proposed method. In comparison, the results show that the proposed method achieved the least average absolute error of 2.87 for the number of edemas, and is 1.15 and 2.30 edemas less than the ReLayNet and the expert 2, respectively. There were some disagreements in the annotations between expert 1 and expert 2 due to their subjectivity, and thus we conducted an experiment that exchanged the two expert roles, namely, the expert 2 annotations were used as the ground truth to train and evaluate the proposed network model. The comparison of quantitative results is shown in Table 6. Compared with ReLayNet, Dice, Iou, Recall, and precision were improved by 0.02, 0.01, 0.02, and 0.01, respectively, in the proposed method. The experiment demonstrated that the proposed method in this paper has a good generalization ability. Having the expert 2 annotations as the ground truth, Figure 17 also shows the segmentation effect of the proposed method and ReLayNet on the one B-scan image. Figure 17a is the original OCT image, Figure 17b corresponds to the 14 annotated edema regions from expert 1, Figure 17c corresponds to the 23 annotated edema regions from expert 2, Figure 17d shows the only seven predicted edema regions by the ReLayNet, and Figure 17e shows the eight predicted edema regions by the proposed method. Compared with the ReLayNet, the proposed method can not only effectively predict the pixels of true edema as edema pixels, but also can better identify and segment diverse edema regions, being closer to the annotations from two experts. Figure 18 shows the comparison of ROC curves for the expert 2 annotations as a ground truth. The AUC value of 0.991 of the proposed method was 0.011 values higher than ReLayNet at 0.980. Having expert 2 annotations as the ground truth, Table 7 illustrates the comparison of quantitative results around fovea edema regions. Compared with ReLayNet, Dice, Iou, Recall, and precision were improved by 0.01, 0.01, 0.01, and 0.02 in the proposed method. Figure 19 also shows the comparison of ROC curves. The AUC value of 0.995 of the proposed method was 0.012 values higher than the ReLayNet at 0.983. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Network Name** & **Dice** & **Iou** & **Recall** & **Precision** \\ \hline Expert 1 & 0.58 & 0.61 & 0.62 & 0.57 \\ ReLayNet & 0.76 & 0.75 & 0.80 & 0.77 \\ The proposed method & 0.78 & 0.76 & 0.82 & 0.78 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Quantitative comparison of segmentation results with expert 2 annotations as the ground truth.