## 5 Results and Discussion  
This section describes the results of the unsupervised and supervised learning methods and shows their comparison based on the results from both methods. Then we compare the  
Figure 7: OCTA_6M ILM_OPL projection images: (**a**) input image, (**b**) corresponding ground truth: The red arrows represent RV and the yellow represents FAZ. results of our proposed encoder-decoder model with other state-of-the-art methods [15]. All the method's performance was evaluated using three well-known metrics, namely, precision, recall, and accuracy, as shown in Equations (3)-(5). \[Accuracy=\frac{TP+TN}{TP+FP+TP+TN}, \tag{3}\]  
\[Precision=\frac{TP}{TP+FP}, \tag{4}\]  
\[Recall=\frac{TP}{TP+FN}, \tag{5}\]  
where \(TP\) represents true positives (correctly predicted RV and FAZ pixels), \(FN\) are false negatives, \(FP\) is false positives, and \(TN\) is true negatives. The proposed methods were evaluated using the performance metrics namely accuracy, precision, and recall. Our deep learning method performs best over unsupervised learning, as shown in Table 3, on RV and FAZ segmentation using OCTA-500 ILM_OPL 2D en face images. We used the trained model on the test set to evaluate ASDC performance. In the context of image segmentation, accuracy is a measure of the overall correctness of the segmentation result, which is typically defined as the ratio of the number of correctly classified pixels to the total number of pixels in the image. In other words, accuracy represents the percentage of pixels that are correctly classified by the segmentation algorithm. The function we used consists of two arguments: the flattened ground truth image and the flattened segmented image. It returns a scalar value between 0 and 1, where 1 indicates perfect accuracy, and 0 indicates complete disagreement between the ground truth and segmented images. In Figure 8, the results from the two proposed methods are shown. The importance of large superficial retinal blood vessels is of great significance in quantifying retinal disease analysis, and the best projection map is the ILM_OPL map which clearly shows the morphology of the large vessel. The irregular white lines represent the large vessel, and the center point represents the FAZ region. The area highlighted in the red circle shows the ability of our proposed method to segment the final RV and FAZ accurately, even with inhomogeneous images and using one projection map. The result from the deep encoder-decoder + K-means clustering outperforms and gains the best outcomes, which are close to the ground truth of the OCTA-500 data set. Figure 8 compares our proposed ASDC approach with the K-means algorithm. Our proposed simple architecture achieves the same comparable results mentioned in the related work. One advantage over other approaches is that our proposed method simultaneously segments RV and FAZ and achieves comparable results. In Figure 8, the input OCTA images are shown in the first column, the K-means clustering algorithm result is shown in the second column, the proposed deep encoder-decoder + K-means clustering method result is shown in the third column, and the ground truth images from the OCTA-500 data set are shown in the last column for all projection maps. In Table 4, we compared the segmentation results of RV and FAZ using various approaches as listed. The table shows that most studies only focused on a single biomarker, whereas our approach simultaneously segmented both biomarkers, resulting in better accuracy metrics.