reveal pathologic alterations associated with retinal disease, leading many researchers to use OCTA image biomarkers to support clinical assessment in the early stages of retinal diseases. Some researchers have used OCTA to uncover pathologic alterations connected with retinal vein occlusion (RVO), including neovascularization, retinal nonperfusion, and FAZ abnormalities [6]. OCTA imaging visualizes blood flow in the retinal vasculature, which can indicate retinal health. Variations in the retinal vasculature detected through OCTA may indicate specific retinal disease [7], while precise segmentation helps the ophthalmologist diagnose RV-related diseases. Another essential structure for retinal health is the FAZ, a zone at the center of the fovea, also termed the fovea centralis, with the maximal cone concentration in the photoreceptor layer. Smaller FAZ individuals have excessive myopia because of poor foveal vascular circulation. At the same time, an enlarged FAZ may signify visual insight loss due to an ischemia situation in the fovea [8]. Consequently, RV and FAZ segmentation are crucial tasks in retinal disease investigation. These familiar structures in every retina exhibit spontaneous changes when disease occurs. However, segmentation is challenging due to limitations in current methods. These methods consist of deep learning (such as U-Net and encoder-decoder-based architecture) based approaches and traditional methods. Deep learning methods primarily consist of a single biomarker, such as FAZ segmentation [9; 10; 11; 12; 13] and RV segmentation [14; 15]. However, the critical problem with RV segmentation is the thickness difference, particularly at the thin vessel ends as show in Figure 1, which have poor contrast, which remains the main barrier to RV segmentation. Another essential factor is similar structure interference, such as non-perfusion and FAZ, because their gray level values are similar and can cause difficulties in segmentation. Although the approach in [16] showed promising results, it suffered from non-uniform image quality, possible confusion between RV and FAZ regions, high computational complexity, and memory consumption. These challenges highlight the need for more efficient and accurate segmentation methods for OCTA images. Segmentation based on traditional image processing methods such as RV [2; 7] and FAZ segmentation [17; 18] uses filter-based models, random field models, morphological operations (opening and closing), and locally adaptive thresholding. The poor segmentation performance illustrates the drawbacks of traditional techniques in feature extraction. These algorithms create assumptions about blood vessel shapes and flow signal distribution that  
Figure 1: Variations in OCTA image quality: thickness difference, poor contrast, and inhomogeneity. 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. could not be accurate across all areas and disease states, which makes it difficult to segment multi-scale vessels and irregular FAZ precisely. The algorithm in [1] uses a frequency-based technique called a Gabor filter bank that only permits a specified frequency for vessel segmentation, which results in erroneous large vessel segmentation due to inhomogeneity in the OCTA image. Hence, simultaneous segmentation of RV and FAZ structures is a challenging task due to the intricate edges of the FAZ, non-uniform image quality, and the presence of multi-scale vessels and non-perfusion. While some studies have focused on extracting only one biomarker, multi-scale vessel segmentation is challenging when segmenting thin and thick vessels or terminal branches in noisy OCTA scans. The researchers' preference comes from the fact that OCTA image data are relatively hard to annotate, leading to a lack of standard available datasets with high-quality FAZ and RV annotations.