# PAM-UNet: Enhanced Retinal Vessel Segmentation Using a Novel Plenary Attention Mechanism
## 5 Conclusions  
PAM-UNet is optimized in three main ways. By incorporating the Plenary Attention Mechanism (PAM) in the network's BottleNeck block, our method is able to find key channels and embed positional information, enabling full-dimensional attention across the X-, Y-, and C-directions of the feature maps. This results in more precise and detailed feature extraction, which is particularly important for complex structures like retinal vessels. Our proposed DropBlock_Diagonal method, which is specifically tailored for retinal vessel datasets, is added to the traditional convolutional blocks. This helps to prevent overfitting by discarding contiguous regions of activation units, forcing the network to learn more robust features. This is especially beneficial in medical image segmentation, where datasets are often limited in size. The integration of feature maps at each decoder stage in the final output ensures that vessel details are preserved, leading to improved segmentation accuracy. Our experiments demonstrate that PAM-UNet achieves better segmentation results on the DRIVE and CHASE_DB1 datasets compared to most other algorithms, indicating its effectiveness and robustness. Through extensive testing, we observed that PAM-UNet performs well even in challenging scenarios with low contrast and severe lesions. This highlights the model's ability to handle complex cases effectively, making it a valuable tool in medical image analysis. While our current focus has been on retinal vessel segmentation, the architecture and mechanisms we propose have potential applications in other medical fields. We plan to explore these possibilities in our future work.  
Conceptualization, Y.W.; methodology, S.W.; software, S.W.; validation, Y.W., S.W. and J.J.; formal analysis, Y.W.; investigation, Y.W. and S.W.; resources, Y.W.; data curation, S.W.; writing--original draft preparation, S.W.; writing--review and editing, Y.W. and S.W.; visualization, S.W.; supervision, Y.W.; project administration, Y.W.; funding acquisition, Y.W. All authors have read and agreed to the published version of the manuscript.  
Key Science and Technology Program of Henan Province (222102210131); Doctoral Fund Support Project of Henan Polytechnic University (B2014-043).  
Not applicable.  
We use two publicly available retinal image datasets to evaluate the segmentation network proposed in this paper, namely, the DRIVE dataset and the CHASE_DB1 dataset. They can be downloaded from the URL: [[https://www.kaggle.com/datasets/andrewmvd/drive-digital-retinal-images-for-vessel-extraction](https://www.kaggle.com/datasets/andrewmvd/drive-digital-retinal-images-for-vessel-extraction)]([https://www.kaggle.com/datasets/andrewmvd/drive-digital-retinal-images-for-vessel-extraction](https://www.kaggle.com/datasets/andrewmvd/drive-digital-retinal-images-for-vessel-extraction)) (accessed on 15 June 2024) and [[https://www.kaggle.com/datasets/rashasarhanalharthi/chase-db1](https://www.kaggle.com/datasets/rashasarhanalharthi/chase-db1)]([https://www.kaggle.com/datasets/rashasarhanalharthi/chase-db1](https://www.kaggle.com/datasets/rashasarhanalharthi/chase-db1)) (accessed on 15 June 2024).  
The authors declare no conflict of interest.