Our approach provides parallel segmentation of RV and FAZ, reducing computational complexity and producing accurate, interpretable results for distinct parts. Additionally, the additivity can be applied to a variety of OCTA biomarkers. Figure 1, demonstrate the OCTA-500 ILM_OPL scans, highlighting the variation in vessel thickness across different vessels. The yellow square indicates thin vessels, while the red rectangle shows thick vessels. The images also demonstrate differences in contrast across varying scales of vessels, with thick vessels having good contrast and thin vessels exhibiting poor contrast. Moreover, the image quality varies across the scans, making it challenging to accurately segment the vessels and FAZ. Inhomogeneity in image quality further adds to the complexity of the biomarker segmentation. In summary, the encoder-decoder models extract relevant features from the input image and produce a compressed feature representation of the object of interest. K-means clustering refines and optimizes the segmentation mask to produce a more precise representation of the biomarkers. The proposed framework comprises two main phases: (1) In phase 1, the first step is to divide the target image (ground truth image) into two parts, FAZ and RV. Then, we convert the input image into grayscale format. These three images will be fed to phase 2 for training the encoder-decoder model. (2) In phase 2, we train two models independently which is Model 1 and Model 2, for RV and FAZ segmentation; each model is comprised of an encoder-decoder network followed by a K-means clustering algorithm applied to each part to obtain the desired segmented output. Finally, the two segmented parts, predicated image 1 corresponding to the FAZ part and predicated image 2 corresponding to the RV part, are concatenated to obtain the final segmented output. Our primary contributions are listed below:  
1. Introducing the ASDC approach for the simultaneous segmentation of RV and FAZ biomarkers in an automatic and scalable manner, thus reducing the computational cost and time required for OCTA image analysis. This approach can potentially aid in early retinal disease investigation by providing more accurate and efficient segmentation. 2. Conducting a comparative analysis of both unsupervised and supervised learning methods for OCTA image segmentation, providing insights into the strengths and weaknesses of different approaches, and helping to inform future research in this area. 3. Demonstrating a thorough understanding of the current state-of-the-art in OCTA image segmentation research and introducing a scalable approach for processing OCTA data. This research contributes to developing more efficient and accurate methods for analyzing OCTA images, which can aid in diagnosing and managing retinal diseases. The visual and calculated segmentation results demonstrate that our framework can obtain the desired segmentation results. Compared to current methods, the ASDC approach improves accuracy while reducing the computing time for OCTA image analysis. Our study examines both supervised and unsupervised learning approaches for OCTA image segmentation and demonstrates improved segmentation accuracy for RV biomarker segmentation compared to previous methodologies. We validated our model on the OCTA-500 dataset using OCTA scans with a maximal projection between the internal limiting membrane (ILM) and the outer plexiform layer (OPL). This projection is derived from the inner retina's highest projection and clearly shows the retina's blood vessels and the structure of the FAZ. The ILM and OPL are commonly used as reference points when segmenting the FAZ and RV biomarkers. The remainder of the paper is organized as follows: Section 2 explains the related work. Section 3 explains the method. Section 4 explains the training process. Section 5 explains the experimental results and discussion. Section 6 presents the conclusion.