# PAM-UNet: Enhanced Retinal Vessel Segmentation Using a Novel Plenary Attention Mechanism
## 1 Introduction
proposed OCE-Net [25]. OCE-Net is able to capture both orientation information and contextual information of vessels and fuses these two types of information together to improve the accuracy of segmentation. Also in 2023, [PERSON] et al. proposed a Multi-resolution Contextual Network (MRC-Net) [26] for retinal vessel segmentation that addresses these issues by extracting multi-scale features to learn contextual dependencies between semantically different features and using bi-directional recurrent learning to model former-latter and latter-former dependencies. Another key idea is training in adversarial settings for foreground segmentation improvement through optimization of the region-based scores. This novel strategy boosts the performance of the segmentation network in terms of the Dice score and corresponding Jaccard index while keeping the number of trainable parameters comparatively low. In 2024, [PERSON] et al. proposed MU-Net [27]. MU-Net employs a multi-scale residual convolution module (MRCM) to extract image features with different granularities and improves the feature utilization efficiency and reduces information loss through residual learning. Selective kernel units (SKUs) are introduced in jump connections to obtain multi-scale features through soft attention. A Residual Attention Module (RAM) is constructed in the decoder stage to further extract vascular features and improve processing speed. In the same year, [PERSON] et al. proposed Anisotropic Perceptive Convolution (APC) and an Anisotropic Enhancement Module (AEM) to model visual cortex cells and their orientation selection mechanism, along with a novel network named W-shaped Deep Matched Filtering (WS-DMF) [28]. This network features a W-shaped framework, with DMF based on a multilayer aggregation of APC designed to enhance vascular features and inhibit pathological features. AEMs are embedded into DMF to improve the orientation and position information of high-dimensional features. Furthermore, to enhance APC's ability to perceive linear textures, such as blood vessels, an Orientation Anisotropic Loss (OAL) is introduced. In 2024, [PERSON] et al. proposed RCAR-UNet [29], a retinal vessel segmentation network algorithm that employs a novel rough channel attention mechanism. This method integrates deep neural networks to learn complex features and rough sets to handle uncertainty, designing rough neurons. A rough channel attention mechanism module is constructed based on these rough neurons and embedded in UNet skip connections to integrate high-level and low-level features. Additionally, residual connections are added to transmit low-level features to high-level, enhancing network feature extraction and aiding in gradient back-propagation during model training. Also in 2024, [PERSON] et al. proposed a multi-scale feature fusion segmentation network called IMFF-Net [30], which is based on a four-layer U-shaped architecture. It enhances the utilization of both high-level and low-level features through multi-scale feature fusion to improve segmentation performance. However, existing algorithms suffer from insufficient feature extraction capabilities, leading to difficulties in learning and focusing on different shapes and sizes of vessel structures. Meanwhile, the traditional Dropout [31] regularization method [32] is not suitable for convolutional operation. A U-shaped network (PAM-UNet) based on PAM is proposed to address the above problems. This network has the following features:  
(1) Proposes a plenary attention mechanism to enable the network to better learn and pay more attention to blood vessel structures of different shapes and sizes. (2) Proposes use of DropBlock_Diagonal based on DropBlock, which is more suitable for retinal vessel datasets. Furthermore, it adds DropBlock_Diagonal to the traditional convolutional block to prevent the network from overfitting. (3) Merges the feature maps containing vessel details at different scales obtained from each stage of the decoder to further improve the final vessel segmentation results.