## 6 Conclusions  
This research introduced a new lightweight ASDC approach for the automatic segmentation of RV and FAZ biomarkers in OCTA-500 scans using 2D en face images of the ILM_OPL projection map. The proposed method outperforms existing techniques both visually and quantitatively, achieving comparable results with less computational complexity. Additionally, the proposed method can segment RV and FAZ simultaneously, whereas current approaches only focus on RV segmentation. To ensure fair comparison with other methods, we individually extracted the RV part from our results. These findings suggest that the proposed method offers a promising approach for retinal image analysis and clinical applications.  
Recent approaches used 3D OCTA volume data; ours uses 2D en face images. Currently, FAZ and RV extractions are typically done as individual tasks. However, it would be beneficial to understand the connections between the biomarkers in order to extract the necessary information to meet the clinical needs more effectively. The primary issue in segmenting large areas, such as FAZ, is in accurately determining the boundaries and in highlighting the complexities and relevance of accurate boundary segmentation.  
The fundamental cause is that the encoder-decoder network is generally inefficient; during the down-sampling process; the loss of image features, such as boundary information, steadily increases, and such data cannot be recovered during the up-sampling process in the decoding stage. After segmenting these important biomarkers, we compared supervised learning and K-means clustering techniques to show the effectiveness of each method. We compared the results with other methods and highlighted the benefit of simultaneous segmentation of retinal biomarkers. Hence, simultaneously segmenting the retinal biomarkers RV and FAZ can enhance the investigation of retinal vascular diseases associated with these biomarkers and further advance our understanding of the retinal vascular system.  
We need to point out that our study still has shortcomings, e.g., we acquire relatively smooth FAZ boundaries, which are rough and irregular in most cases, so our further study will be the improvement of the accuracy of FAZ boundaries. In the future, we will create a segmentation and classification system to examine retinal biomarkers used by ophthalmologists in DR early stages.  
Conceptualization, A.K., Z.D. and J.L.; Data curation, A.K., J.H. and J.L.; Formal analysis, A.K., J.H., Z.D. and J.L.; Funding acquisition, J.L.; Investigation, A.K. and J.L.; Methodology, A.K. and J.L.; Project administration, J.L.; Software, A.K.; Supervision, Z.D. and J.L.; Validation, A.K., J.H., Z.D. and J.L.; Visualization, A.K. and J.H.; Writing--original draft, A.K.;  
Figure 13: Comparison of computational complexity.  
Writing--review and editing, A.K., J.H., Z.D. and J.L. All authors have read and agreed to the published version of the manuscript.
* [1]**Funding:** This research was supported by the Department of Science & Technology of Shandong Province (2017 CXGC0810) and Shandong Natural Science Foundation (ZR2021 QF043).
* [2]**Data Availability Statement:** The ASDC experiment utilized the comprehensive public dataset OCTA-500 to facilitate the analysis and interpretation of the research findings.
* [3]**Acknowledgments:** We want to express our sincere gratitude to [PERSON], Biomedical Engineering (BME) student at Oregon Health & Science University USA, who provided invaluable guidance and support throughout this research project. He offered insightful feedback and suggestions that significantly improved the quality of our work, and his expertise and dedication were instrumental in the success of this project. We would also like to thank [PERSON] for his collaboration in managing the data and providing guidance.  
The authors declare no conflict of interest.