JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE) ›› 2025, Vol. 60 ›› Issue (7): 22-31.doi: 10.6040/j.issn.1671-9352.0.2024.062

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A dual branch retinal vessel segmentation network based on guided filtering and Transformer

YAN Bencong1, WANG Yingmei1,2*   

  1. 1. School of Mathematics and Statistics, Shandong University of Technology, Zibo 255049, Shandong, China;
    2. School of Mathematics, Shandong University, Jinan 250100, Shandong, China
  • Published:2025-07-01

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Abstract: Based on the Transformer structure and the UNet++ network,a new dual branch retinal vessel segmentation network is proposed. The dual branch encoder in the network can better correlate global information in the image, so that the proposed network performs well on small datasets. Furthermore, in order to solve the problem of retinal vessel information loss caused by downsampling operations in UNet, guided filtering is introduced in the feature maps of the output layer and the second layer, which effectively improves the accuracy of small vessel segmentation. The effectiveness of the network is validated by experiments on the DRIVE(digital retinal images for vessel extraction)dataset and the CHASEDB1(combined healthy and diabetic retinopathy database 1)dataset, which shows significant improvements in accuracy, sensitivity, and other parameters. In addition, more small blood vessels are visually segmented with better accuracy. All results demonstrate that the proposed method has better performance.

Key words: UNet++, Transformer, guided filtering, retinal vascular segmentation

CLC Number: 

  • O29
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