## 2 Related Work  
OCTA is a non-invasive novel modality; thus, a limited number of RV and FAZ segmentation studies are available. Most researchers have worked on color fundus images [20] for retinal blood vessel segmentation [21]. The retinal vasculature precise segmentation is a challenging task because of the low contrast of the image data, interference of homogenous structure in the retinal vasculature, and variation in vessel size; thus, automatic RV and FAZ segmentation is a crucial issue in retinal vasculature segmentation. This paper discusses mostly RV segmentation-related work based on color fundus images because of the few studies available on OCTA. Most RV segmentation was conducted on color fundus images, and several studies have been reported [22; 23; 24; 25; 26; 27; 28; 29; 30] etc. This is because OCTA is a relatively rare, available technology, and some RV segmentation techniques have been reported, such as [PERSON] et al. [2] who used a noise reduction filter using the generalized Gauss-Markov random field (GGMRF) model. Then they used a joint Markov-Gibbs random field (MGRF) model for RV segmentation. [PERSON] et al. [1], proposed a new frequency-based method using Gabor filter banks for RV and FAZ segmentation in OCTA en face images and compared the result within a built device called Cirrus HD-OCT with a difference of 1.58 \(\pm\) 1.08% compared to the device for FAZ where a mean dice similarity coefficient of 0.89 was obtained. [PERSON] et al. [31], proposed an angiogenesis tracking algorithm using top hat filtering and optimally oriented flux (OOF) algorithms to segment the retinal capillary plexus. [PERSON] et al. [14] presented the retinal OCTA segmentation (ROSE) data set. They proposed a new split-based coarse-to-fine RV segmentation network able to detect thick and thin vessels, and this study mainly focused on RV segmentation without segmenting the FAZ part. [PERSON] et al. [17] proposed an OCTA FAZ segmentation method based on a generalized gradient vector flow (GGVF) snake model to identify the FAZ area. [PERSON] et al. [18] proposed an image processing method using morphological operators to enhance the input image and edge detection technique to extract the FAZ. [PERSON] et al. [11] proposed a deep learning model which is called MEDnet, a multi-scaled encoder-decoder neural network to segment the retinal capillary non-perfusion area which also includes the FAZ region in the retinal superficial vascular complex while the highest accuracy of the proposed method was 0.89 \(\pm\) 0.04. [PERSON] et al. [16] proposed a 3D-to-2D image projection network (IPN), IPN-V2, and IPN-V2+, which gave promising results in RV and FAZ segmentation. The author also developed the OCTA-500 dataset. Despite their advancements, these methods remain limited and need vast training data and model parameters. Such as is seen in the case of [14; 16]; extensive parameter training and a required 3D volumetric OCTA scan for training, suffering from non-uniform image quality resulting in confusion between RV and FAZ. Another essential factor is similar structure interference, such as non-perfusion and FAZ, because their greyscale values are similar and can cause difficulties in segmentation. Traditional image processing methods such as RV and FAZ segmentation [17; 18] use filter-based models, random field models, morphological operation (opening and closing), and locally adaptive thresholding methods, which produce rough vessel boundaries caused by the non-uniform image quality and excessive background noise caused by threshold-based and filter-based segmentation algorithms. Some of the approaches mentioned above [7] are limited to segmenting a single biomarker, due to issues such as ambiguous FAZ with intrusive structures, unavoidable outliers caused by erroneous layer projection, and the inability to segment low-contrast FAZ from its surrounding region. In summary, only a limited number of studies are currently available for the simultaneous segmentation of RV and FAZ.