基于多尺度双时融合提取和全局分组注意力的视网膜分割方法

doi:10.3969/j.issn.0258-8021.2026.01.002

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Abstract Retinal vessel segmentation plays a crucial role in diagnosing retinal vascular diseases. Recent studies based on UNet have demonstrated promising performance in this task. However, segmenting thin retinal vessels with low contrast often leads to the loss of spatial information at certain stages. To address this issue, this paper proposed the novel network architecture, BFM-GGCA-UNet. The model employed convolutional kernels of different sizes at various stages to process feature maps, effectively capturing multi-scale features and forming a comprehensive feature representation. Furthermore, a multi-resolution convolutional interaction mechanism was introduced to expand the receptive field in both horizontal and vertical directions while maintaining the full image resolution. Additionally, to enhance this directional expansion, a global grouped attention mechanism was proposed. This mechanism leverages shared convolutional layers and an attention module to generate attention maps for the height and width dimensions, which weight the input feature maps to accentuate crucial features for a more precise prediction map. The proposed model was evaluated on five public datasets: DRIVE, STARE, CHASE_DB1, HRF and ARIA. The results showed that our model achieved AUCs of 98.89%, 99.50%, 99.13%,98.87%, and98.72%, and accuracies of97.25%, 98.06%,97.49%, 97.96% and 96.81% on these datasets, respectively, outperforming most existing methods. In conclusion, the designed BFM-GGCA-UNet effectively improved segmentation accuracy and delivered superior performance on these five fundamental retinal vessel segmentation datasets.

Key words： retinal vessel segmentation UNet multi-scale bitemporal fusion multi-resolution convolutional interaction mechanism global grouped coordinate attention

Received: 28 March 2025

PACS:

R318

Corresponding Authors: ^*E-mail: xuhe@njupt.edu.cn

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	Liu Xuepeng
	Xu He
	Ji Yimu
	Li Peng

Cite this article:

Liu Xuepeng,Xu He,Ji Yimu, et al. Retinal Segmentation Method Based on Multi-Scale Bitemporal Fusion Module and GlobalGrouped Coordinate Attention[J]. Chinese Journal of Biomedical Engineering, 2026, 45(1): 11-24.

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http://cjbme.csbme.org/EN/10.3969/j.issn.0258-8021.2026.01.002 OR http://cjbme.csbme.org/EN/Y2026/V45/I1/11

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