## 5 Results and Discussion
### RV Segmentation Results  
In this section, we compare the performance of our proposed method for RV segmentation on the OCTA-500 dataset with several state-of-the-art segmentation methods. Since most previous studies have focused on RV segmentation using complex models, our objective was to implement a lightweight model that would achieve good results while being computationally efficient. We performed our performance calculations on 60 OCTA-500 images, extracting the RV ground truth labels from the OCTA-500 ground truth. Using our ASDC approach, we segmented the input test images and extracted only the RV part, which we saved in a directory. To evaluate the performance of our segmentation results, we used the dice similarity coefficient (DSC) and Jaccard index (JAC) as evaluation indexes, which are defined in Equations (6) and (7) and are commonly used to measure the similarity between predicted values and labels:  
\[DSC=\frac{2\times TP}{FP+FN+2\times TP}, \tag{6}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Number** & **Target** & **Methods** & **Accuracy** \\ \hline \multirow{3}{*}{1 [2]} & & [PERSON] et al. & 0.84 \\  & & [PERSON] et al. & 0.70 \\ \cline{1-1}  & & [PERSON] et al. & 0.80 \\ \cline{1-1}  & & [PERSON] et al. & 0.93 \\ \cline{1-1}
2 [11] & FAZ segmentation & [PERSON] et al. & 0.89 \\ \cline{1-1}
3 & RV and FAZ & **Ours** & **0.96** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of different methods for retinal vasculature segmentation using OCTA images.  
Figure 8: Segmentation results of K-means and ASDC approach on OCTA_6M projection map (ILM_OPL).  
\[JAC=\frac{TP}{FP+FN+TP}, \tag{7}\]  
with, _TP_ (true positive), _FP_ (false positive), and _FN_ (false negative). _DSC_ measures the overlap between the predicted segmentation and the ground truth, with values ranging from 0 to 1. _JAC_ is similar to _DSC_ but places more weight on the true positives and less weight on the false positives and false negatives.  
Our results show that our proposed method achieved good performance while utilizing minimal resources. The results are shown in Figure 11 and Table 5. Despite differences in experimental setup, including the number of testing samples and hyperparameters, we were still able to make a fair comparison with other methods by using the same OCTA-500 dataset. Our visual and quantitative results were acceptable in comparison with other methods, suggesting that our proposed method may be a promising approach for RV segmentation in OCTA images. These results highlight the potential of our lightweight model to achieve good results while being computationally efficient, with potential applications in the diagnosis and monitoring of retinal diseases. In Table 5, the DICE and JAC values for RV segmentation and RV + FAZ segmentation are different because the RV + FAZ segmentation includes both the RV and the FAZ biomarkers, while the RV segmentation  
Figure 10: More results of our proposed methods on OCTA-500 data set.  
Figure 9: Comparison of RV segmentation results on OCTA-500 with OVS-net. Our proposed method achieves impressive segmentation outcomes for small and large vessels, as shown in row 2, column 4. The green circle represents the FAZ, an added benefit of our proposed method. The results obtained by our proposed method are compared with those reported by [PERSON] et al. [15].  
only includes the RV biomarker. The inclusion of the FAZ in the RV + FAZ segmentation makes the segmentation task more challenging because the FAZ is a small and complex structure that can be difficult to accurately segment.  
In general, the inclusion of more structures in a segmentation task can lead to lower dice and JAC values because the segmentation becomes more difficult. This is reflected in the lower dice and JAC values for the RV + FAZ segmentation compared to the RV segmentation. Figure 12 presents an analysis of the performance metrics for RV segmentation of OCTA-500 scans.