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WLKA-RVS: a retinal vessel segmentation method using weighted large kernel attention

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Abstract

Retinal vessel segmentation is an important task in medical image analysis and has a wide range of applications in the diagnosis and treatment of retinal diseases. However, existing segmentation methods still have some shortcomings in accurately segmenting thin vessels. Based on this observation, we propose a Retinal Vessel Segmentation method based on Weighted Large Kernel Attention (WLKA-RVS), which aims to improve the accuracy of retinal vessel segmentation to better assist physicians in clinical diagnosis and treatment. Our method consists of an encoder and a decoder. In the encoder, a convolution stem first reduces the dimension of the input image. Then, feature extraction is performed by four stages of Swin Transformer modules, each stage with a downsampling layer. In the decoder, there are four different stages of Weighted Large Kernel Attention Block (WLKAB) corresponding to the Swin Transformer modules in the encoder. Then WLKA-RVS applies the Patch Expanding module to achieve upsampling. Finally, a linear layer outputs the final results. We have performed extensive experiments comparing several recent advanced models on three public datasets. WLKA-RVS led by 0.32%, 1.24%, and 0.71% in the mAcc metric, respectively. At the same time, the inference speed of WLKA-RVS met the real-time requirements for medical diagnosis. A series of experiments demonstrated the efficiency, robustness, and applicability of WLKA-RVS.

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Data Availability

The data that support the findings of this study are openly available at https://cecas.clemson.edu/ahoover/stare/, https://www5.cs.fau.de/research/data/fundus-images/, and https://blogs.kingston.ac.uk/retinal/chasedb1/.

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Acknowledgements

The authors wish to thank the editors and anonymous reviewers for their valuable comments and suggestions.

Funding

This work was supported by the Guangdong University of Science and Technology Research Natural Science Project with Grant No. GKY-2023KYYBK-17.

Author information

Authors and Affiliations

  1. School of Computer Science and Engineering, Faculty of Innovation Engineering, Macau University of Science and Technology, Taipa, Macau, 999078, China

    Jiayao Li & Qianxiang Cheng

  2. College of Computer Science, Guangdong University of Science and Technology, Dongguan, 523083, PR China

    Min Zeng

  3. University International College, Macau University of Science and Technology, Taipa, Macau, 999078, China

    Chenxi Wu

  4. The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, PR China

    Qiuyan Guo

  5. Wangjiahe Street Community Health Service Center, Yueyang, 414000, PR China

    Song Li

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  1. Jiayao Li
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  4. Qianxiang Cheng
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Contributions

Jiayao Li: Conceptualization, Methodology, Software, Validation, Writing - Original Draft, Visualization. Min Zeng: Formal analysis, Writing - review & editing, Supervision. Chenxi Wu: Investigation, Writing - review & editing, Supervision. Qianxiang Cheng: Resources, Data Curation, Writing - review & editing. Qiuyan Guo: Investigation, Writing - review & editing, Supervision. Song Li: Conceptualization, Supervision, Project administration, Funding acquisition.

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Correspondence to Song Li.

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Li, J., Zeng, M., Wu, C. et al. WLKA-RVS: a retinal vessel segmentation method using weighted large kernel attention. Appl Intell 55, 403 (2025). https://doi.org/10.1007/s10489-025-06309-4

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