# PAM-UNet: Enhanced Retinal Vessel Segmentation Using a Novel Plenary Attention Mechanism
## 1 Introduction  
Retinal vessels contain much biological information and are the only clear blood vessels in the body that can be visualized by non-invasive means. Retinal vessel segmentation can be used to characterize the morphology of retinal vessels, such as length, width, branches pattern and angle. Current medical research suggests that retinal vasculopathy may precede cardiovascular diseases, such as hypertension, coronary artery disease, and diabetes, and that retinal vessel segmentation can be used as a basis for diagnosing related diseases [1; 2; 3; 4; 5]. However, the complex distribution and trend of blood vessels in the retina, the large variation in size and the interference of lesions, as well as the low illumination and imaging resolution of fundus cameras, make it difficult to completely segment retinal vessels [6]. Therefore, retinal vessel image segmentation has been a hot and difficult issue in the field of retinal image analysis. In recent years, deep learning has proved to be powerful in the field of image segmentation [7; 8; 9; 10]. Researchers have proposed many neural networks for vessel segmentation and achieved good segmentation results. In 2015, [PERSON] et al. proposed semantic segmentation using full convolutional neural networks [11] for the first time, based on which a variety of different semantic segmentation networks have emerged [12]. Among them, the UNet [13] (U-shaped Convolutional Network) network model has gradually become the focus of the medical image segmentation field because of its good segmentation performance, after which many improved network models based on UNet have appeared. In 2018, [PERSON] et al. utilized the power of UNet, residual networks, and RCNN [14] (recurrent convolutional neural network) to propose RU-Net and R2U-Net [15]. In 2019, [PERSON] et al. proposed the CE-net [16] model by replacing the traditional UNet encoding and decoding blocks withpre-trained ResNet-34 [17] blocks, while applying dense atrous convolution and residual multi-kernel pooling to the bottleneck of the network, which achieved good results in retinal vessel segmentation. In 2020, [PERSON] et al. [18] extracted the global context information through ResNet blocks in the encoding phase, and the feature maps obtained by guiding the attention modules (spatial self-attention and channel self-attention) in the decoding phase were used as the final segmentation results. In the same year, [PERSON] et al. proposed IterNet [19], which makes retinal vessel segmentation more coherent through UNet and several mini-UNets, and is connected by dense jumps [20] to prevent overfitting. In 2021, [PERSON] et al. proposed SCS-net [21], which replaces the traditional four-fold downsampling with three-fold downsampling. SCS-net employs scale-aware feature fusion through \(3\times 3\) convolutions with different strides at the bottleneck, incorporates attention mechanisms during skip connections, and applies semantic supervision by decoding feature maps at different stages during the final output. SCS-net has achieved outstanding results on common retinal vessel datasets. In 2022, a novel full-resolution network (FR-UNet) [22] was proposed, which horizontally and vertically expands through a multi-resolution convolution interaction mechanism to address the issue of spatial information loss in traditional U-shaped segmentation networks. The feature aggregation module in FR-UNet integrates multi-scale feature maps from adjacent stages to supplement high-level contextual information. Modified residual blocks continuously learn multi-resolution representations to obtain pixel-level accuracy prediction maps. In 2022, [PERSON] et al. proposed ResDO-UNet [23]. ResDO-UNet introduces the residual DO-conv [24] (ResDO-conv) network as a backbone network. ResDO-UNet proposes a Fusion Pooling Block (PFB) for non-linear fusion pooling during pooling operations. Additionally, it introduces an Attention Fusion Block (AFB) for multi-scale feature representation during skip connections. In 2023, [PERSON] et al.