MAG-Net : Multi-fusion network with grouped attention for retinal vessel segmentation

Yun Jiang; Jie Chen; Wei Yan; Zequn Zhang; Hao Qiao; Meiqi Wang; Yun Jiang; Jie Chen; Wei Yan; Zequn Zhang; Hao Qiao; Meiqi Wang

doi:10.3934/mbe.2024086

Mathematical Biosciences and Engineering

2024, Volume 21, Issue 2: 1938-1958. doi: 10.3934/mbe.2024086

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Research article

MAG-Net : Multi-fusion network with grouped attention for retinal vessel segmentation

College of Computer Science and Engineering, Northwest Normal University, Lanzhou 730070, China

Academic Editor: Yang Kuang

Received: 14 September 2023 Revised: 15 November 2023 Accepted: 27 November 2023 Published: 05 January 2024

Retinal vessel segmentation plays a vital role in the clinical diagnosis of ophthalmic diseases. Despite convolutional neural networks (CNNs) excelling in this task, challenges persist, such as restricted receptive fields and information loss from downsampling. To address these issues, we propose a new multi-fusion network with grouped attention (MAG-Net). First, we introduce a hybrid convolutional fusion module instead of the original encoding block to learn more feature information by expanding the receptive field. Additionally, the grouped attention enhancement module uses high-level features to guide low-level features and facilitates detailed information transmission through skip connections. Finally, the multi-scale feature fusion module aggregates features at different scales, effectively reducing information loss during decoder upsampling. To evaluate the performance of the MAG-Net, we conducted experiments on three widely used retinal datasets: DRIVE, CHASE and STARE. The results demonstrate remarkable segmentation accuracy, specificity and Dice coefficients. Specifically, the MAG-Net achieved segmentation accuracy values of 0.9708, 0.9773 and 0.9743, specificity values of 0.9836, 0.9875 and 0.9906 and Dice coefficients of 0.8576, 0.8069 and 0.8228, respectively. The experimental results demonstrate that our method outperforms existing segmentation methods exhibiting superior performance and segmentation outcomes.
- retinal images,
- vessel segmentation,
- convolutional neural network,
- multi-scale technique,
- attention mechanism
Citation: Yun Jiang, Jie Chen, Wei Yan, Zequn Zhang, Hao Qiao, Meiqi Wang. MAG-Net : Multi-fusion network with grouped attention for retinal vessel segmentation[J]. Mathematical Biosciences and Engineering, 2024, 21(2): 1938-1958. doi: 10.3934/mbe.2024086

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Abstract

Retinal vessel segmentation plays a vital role in the clinical diagnosis of ophthalmic diseases. Despite convolutional neural networks (CNNs) excelling in this task, challenges persist, such as restricted receptive fields and information loss from downsampling. To address these issues, we propose a new multi-fusion network with grouped attention (MAG-Net). First, we introduce a hybrid convolutional fusion module instead of the original encoding block to learn more feature information by expanding the receptive field. Additionally, the grouped attention enhancement module uses high-level features to guide low-level features and facilitates detailed information transmission through skip connections. Finally, the multi-scale feature fusion module aggregates features at different scales, effectively reducing information loss during decoder upsampling. To evaluate the performance of the MAG-Net, we conducted experiments on three widely used retinal datasets: DRIVE, CHASE and STARE. The results demonstrate remarkable segmentation accuracy, specificity and Dice coefficients. Specifically, the MAG-Net achieved segmentation accuracy values of 0.9708, 0.9773 and 0.9743, specificity values of 0.9836, 0.9875 and 0.9906 and Dice coefficients of 0.8576, 0.8069 and 0.8228, respectively. The experimental results demonstrate that our method outperforms existing segmentation methods exhibiting superior performance and segmentation outcomes.

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