Subsequently, various large OCTA image datasets were published, and the authors began investigating the extraction strategy of two biomarkers using the same method. Determining boundaries is a significant challenge in segmenting large areas such as the FAZ. Improved boundary segmentation methods are needed to meet clinical requirements and address the complexities involved in this task. While encoder-decoder models can effectively capture unique features, they may produce unclear or inaccurate outputs, mainly when dealing with complex and variable features. The fundamental cause is that the encoder-decoder network is generally inefficient; that is, during the down-sampling process, the loss of image features such as boundary information steadily increases [19], and such data cannot be recovered during the up-sampling process in the decoding stage. The extraction of biomarker features using traditional approaches is another issue. This study addresses this challenge by using separate encoding and decoding for each task instead of combining them into a single task. Combining the encoder-decoder models with K-means clustering can further refine and optimize the segmentation mask. By combining the RV and FAZ segmentation tasks into a single approach, we can achieve improved accuracy, fine-grained control over the training process, and scalability to other OCTA biomarkers, facilitating early retinal disease diagnosis by ophthalmologists. To address the challenges associated with OCTA image data analysis, such as the extensive data annotation required for accurate segmentation, the loss of information in the down-sampling process for RV and FAZ, the difficulties in feature extraction using traditional methods, and the extensive training and inference times associated with complex network structures, we propose a new ASDC approach that reduces features and boosts clustering performance by combining a deep encoder-decoder with K-means clustering to segment the two essential structures (RV and FAZ) in OCTA-500 scans. This approach allows for the simultaneous segmentation of two important biomarkers, RV and FAZ, using parallel processing to reduce the time required for analysis. Combining the RV and FAZ segmentation tasks into a single approach aims to improve accuracy and scalability while reducing computational cost and time in OCTA image analysis. Our method has the potential to help ophthalmologists diagnose diseases early and manage retinal diseases more efficiently. To improve K-means clustering performance, we use an encoder-decoder network to reduce the number of features and prepare the data for clustering. K-means struggles to evaluate high-dimensional spaces, which is common in OCTA images with large numbers of pixels and intensity values. By reducing the dimensionality of the data, we can enhance the clustering effectiveness and accurately identify meaningful groupings. The importance of K-means clustering preserves the quality of small and large vessels. As a result, in non-uniform OCTA scans, we attempt to acquire simultaneous RV and FAZ segmentation and precise multi-scaled vessel extraction while avoiding interference from similar structures such as non-perfusion and FAZ. Our dual segmentation network based on an encoder-decoder network with K-means treats the segmentation problem independently instead of treating it as a single class. The framework treats each problem as a single task to segment RV and FAZ and learn their relationship. Feature extraction through the encoder and feature expansion using a decoder followed by the K-means algorithm obtains the candidate for segmentation. Then, it converges to the desired target segmentation based on cluster pixel similarity, which results in multi-scaled RV segmentation with a smooth gradient by removing noisy dots. A similar model is used for the FAZ candidate, and the final segmented output is obtained by merging the dual model results.