## 2 Literature Review  
Automatic retinal vascular segmentation techniques were first established with the introduction of AI [32]. These techniques were initially unsupervised but eventually developed into supervised techniques by including labeled data. The literature surrounding retinal vessel segmentation underscores its critical role in facilitating accurate visualization, diagnosis, early treatment, and surgical planning for ocular diseases. Retinal vascular segmentation techniques are now performing at the cutting edge thanks to recent developments in deep learning. However, there are still issues because of the wide variations in vessel morphology versus noisy backgrounds. These issues include low discriminative ability in the optic disk area and trouble correctly identifying small, thin vessels. [PERSON] et al., in their study, present an improved U-Net-like model with a skip connection technique and a weighted attention strategy to address these issues [33]. Experiments carried out on two benchmark datasets confirm that the suggested method is effective in enhancing the accuracy of retinal vascular segmentation and resolving the previously described issues. Pyramid U-Net offers a solution by leveraging Pyramid-Scale Aggregation Blocks (PSABs) to aggregate features across multiple scales, yet challenges in segmenting capillaries persist despite these advancements [34]. The ARP-Net represents a significant advancement in retinal vessel segmentation by integrating the Adaptive Gated Axial Transformer (AGAT) and Residual and Point repair modules, enabling more accurate delineation of retinal vascular structures [35]. The PCAT-UNet [36] represents a significant advancement in retinal vessel segmentation by combining the global computing features of Transformer with the local feature extraction capabilities of CNNs, therefore addressing the limitations of existing methods in capturing both global dependence relationships and local feature details. The limitation of PCAT-UNet could be its susceptibility to overfitting, especially when trained on datasets with limited variability or insufficient sample diversity. This could result in the model's reduced ability to generalize to unseen data or variations in imaging conditions, impacting its performance in real-world applications where such variability is common. Recent advancements in deep learning have significantly enhanced blood vessel segmentation techniques. Furthermore, the exploration of the usage of attention-based models and multi-scale features has allowed the neural network models to give better performance [37; 38; 39]. [PERSON] et al. (2023) introduced the Multi-scale Context Dense Aggregation Network (MCDAU-Net), which tackles issues like inadequate sample sizes and neglect of contextual information by utilizing concentric patches and incorporating a Cascaded Dilated Spatial Pyramid Pooling (CDSPP) module to enhance the receptive field. The InceptionConv (IConv) module explores deeper semantic features, and the Multi-scale Adaptive Feature Aggregation (MAFA) module fuses multi-scale features through adaptive weight coefficients, achieving superior segmentation performance [40]. [PERSON] et al. (2022) proposed ResDO-UNet, integrating residual depth-wise over-parameterized convolutional layers (ResDO-conv), a pooling fusion block (PFB) to combine max and average pooling layers, and an attention fusion block (AFB) for effective multi-scale feature expression, enhancing the extraction capabilities and reducing information loss caused by multiple pooling operations [41]. The MTPA_Unet architecture segments retinal vessels by leveraging both the local feature extraction capabilities of CNN and the long-distance dependency modeling strengths of transformers [42]. However, the scalability of MTPA_Unet may be limited when dealing with images of varying resolutions or sizes, as the network architecture may not readily adapt to such changes, potentially leading to decreased segmentation accuracy. [PERSON] et al.