## 3 Experimental Results
### Experimental Settings and Results
Figure 11: Quantitative comparison of confusion matrices: (**a**) the proposed method; (**b**) ReLayNet. Figure 10: The segmentation results: (**a**) original image; (**b**) expert 1 annotations; (**c**) expert 2 annotations; (**d**) ReLayNet predictions; (**e**) proposed method predictions. Figure 11 illustrates the quantitative comparison diagram of confusion matrix. It can be seen that the recall score of the proposed method was increased by 2% compared to ReLayNet, and the number of pixels correctly segmented in the edema region was 73 more than ReLayNet. Figure 12 shows the comparison of ROC curves. The AUC value of 0.997 of the proposed method was 0.011 values higher than ReLayNet at 0.986. Figure 13 shows the segmentation effects of the proposed method and ReLayNet in two B-scan images with the fovea edema. The first row is the original OCT image, the second row corresponds to the annotations from expert 1, the third row corresponds to the annotations from expert 2, the fourth row is the predicted segmentation by the ReLayNet, and the final row is the predicted segmentation by the proposed method. In comparison, the proposed method can not only effectively predict the pixels of true edema as edema pixels, but also reduce the prediction of non-edema pixels as edema pixels, which is closer to the annotations from expert 1. On the boundary of the edema regions, the effect of the proposed method was slightly better than ReLayNet. Since this paper is inspired by ResNeSt, the loss of features in the transmission process were reduced, which made the proposed method significantly improve the segmentation effect of diverse edemas in multi-regions. In this paper, 50 images were selected as the test dataset, with diverse edemas in multi-regions, and were compared with the ReLayNet method and expert 2 on four indicators. The comparison results are shown in Table 4 and Figures 14 and 15. The proposed method effectively upgraded to 0.79, 0.78, 0.83, and 0.82 in the four indicators and was much better than the ReLayNet method and expert 2 for diverse edemas in multi-regions. Figure 14 illustrates the quantitative comparison diagram of confusion matrix. It can be seen that the recall of the proposed method was increased by 4% compared to ReLayNet, and the number of pixels correctly segmented in the edema region was 157 more than ReLayNet in the test set. Figure 15 shows the comparison of ROC curves. The AUC value of 0.990 of the proposed method was 0.019 values higher than that of ReLayNet at 0.971. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Network Name** & **Dice** & **Iou** & **Recall** & **Precision** \\ \hline Expert 2 & 0.61 & 0.58 & 0.60 & 0.61 \\ ReLayNet & 0.75 & 0.73 & 0.79 & 0.78 \\ The proposed method & 0.79 & 0.78 & 0.83 & 0.82 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Quantitative comparison of segmentation results for diverse edemas in multi-regions. Figure 12: Comparison of ROC curves. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Dice** & **Iou** & **Recall** & **Precision** \\ \hline Expert2 & 0.56 & 0.57 & 0.61 & 0.58 \\ ReLayNet & 0.81 & 0.79 & 0.85 & 0.84 \\ The proposed method & 0.82 & 0.80 & 0.87 & 0.85 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Quantitative comparison of segmentation results around fovea edema regions. Figure 14: Comparison of confusion matrices. (**a**) the proposed method; (**b**) ReLayNet. Figure 13: Comparison of segmentation results around fovea edema: (**a**) original image; (**b**) expert 1 annotations; (**c**) expert 2 annotations; (**d**) ReLayNet predictions; (**e**) proposed method predictions.