## 3 Experimental Results
### Experimental Settings and Results  
The experiment uses the open source deep learning toolbox Tensorflow1.14.0 as the framework. Firstly, we randomly selected 70% of the original dataset as the training set, 20% as the validation set, and 10% as the test set. Then, the three nonoverlapping datasets were individually expanded to 10 times by flipping, cropping, panning, and rotating operations. The training set was trained 200 times. The batch size of each training was 2, and output the Dice every two iterations. The final results were the average of five different training experiments, which repeated with different, random subsets of images to assess the robustness of the segmentation performance. The segmentation results of the proposed method were compared with expert 2 and two recent methods on the Duke dataset. The results are shown in Table 2 and Figures 8 and 9. The other two methods were FCN [11] and ReLayNet [15]. FCN is segmentation results by the full convolution neural network, and ReLayNet is segmentation results by U-Net. Table 2 summarizes the test results; the Dice score of the proposed method was 0.80, Iou: 0.78, recall: 0.84, precision: 0.80--all of which achieved the highest value. It is worth noting that there were slightly different annotations between the two experts, since expert 2 achieve the lowest score. Figure 8 illustrates the comparison diagram of confusion matrix, which reflects the segmentation effect of each category. In each cell, both the average number of pixels and the percentage corresponds to the first row and the second row, respectively, and the diagonal is the recall of each category. It can be seen that the recall of the proposed method is increased by 2% compared to ReLayNet, and the number of pixels correctly segmentation in the edema region is 74 more than ReLayNet in each test set. Figure 9 illustrates the comparison of receiver operating characteristic (ROC) curves. Since the dataset contains highly unbalanced classes, the coverage of macular edema is relatively small, and thus the ROC curve is used as the evaluation standard to visualize the model performance, and the area under the ROC curve (AUC) is used to evaluate the ROC curve. In Figure 9, it shows that the AUC of the proposed method is significantly better than that of the ReLayNet. The AUC of 0.993 of the proposed method was higher than the 0.981 of the RelayNet. Figure 10 shows the segmentation results of the proposed method on three 2D B-scan OCT images. 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 of the proposed method, which is similar to the annotations from two experts. The edemas' segmentation results around fovea edema regions of the OCT images can be significantly improved due to the feature extraction improvement of the network in this paper. In this paper, 30 images of large fovea edema and 50 images of multi-regions edema were selected from the test set and were compared with the ReLayNet method on four indicators. The fovea edema's segmentation results are shown in Table 3 and Figures 11 and 12. In this paper, compared with ReLayNet, Dice, Iou, Recall, and precision were improved by 0.01, 0.01, 0.02, and 0.01 in the proposed method. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Dice** & **Iou** & **Recall** & **Precision** \\ \hline Expert 2 & 0.58 & 0.61 & 0.62 & 0.57 \\ FCN [11] & 0.61 & 0.64 & 0.69 & 0.72 \\ ReLayNet [15] & 0.77 & 0.75 & 0.82 & 0.78 \\ The proposed method & 0.80 & 0.78 & 0.84 & 0.80 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Quantitative comparison of the segmentation results. Figure 8: Quantitative comparison of confusion matrices: (a) proposed method; (b) ReLayNet. Figure 9: Comparison of receiver operating characteristic (ROC) curves.