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Retinal fundus image super-resolution based on generative adversarial network guided with vascular structure prior.

Yanfei Jia1, Guangda Chen2, Haotian Chi3

  • 1School of Electrical and Information Engineering, Beihua University, Jilin, 132013, China. jia_yanfei@163.com.

Scientific Reports
|October 1, 2024
PubMed
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This study enhances retinal fundus image super-resolution by incorporating structural priors from U-Net segmentation maps. The improved method effectively suppresses structural distortions, leading to better visual performance in diagnostic imaging.

Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Retinal fundus image analysis is crucial for diagnosing ophthalmic and systemic diseases.
  • Image clarity and resolution are vital for accurate clinical diagnosis.
  • Generative adversarial networks (GANs) excel at image super-resolution, but Real-ESRGAN struggles with structural distortions in retinal images.

Purpose of the Study:

  • To improve the super-resolution of retinal fundus images by addressing structural distortions.
  • To enhance the accuracy and visual quality of retinal images for better disease screening.

Main Methods:

  • Utilized a pre-trained U-Net model to generate structural segmentation maps of retinal vessels.
  • Integrated spatial feature transform layers to incorporate structural priors into the GAN generator.

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  • Introduced channel and spatial attention modules into the discriminator's skip connections.
  • Incorporated L1 loss on segmentation maps to further constrain the super-resolution process.

    • Main Results:

    • The improved algorithm significantly suppressed structural distortions in super-resolved retinal images.
    • Enhanced visual performance and preservation of critical vascular structures were observed.
    • Simulation results demonstrated superior quality compared to existing methods.

    Conclusions:

    • The proposed method effectively overcomes the limitations of existing super-resolution techniques for retinal fundus images.
    • This approach offers a promising tool for improving diagnostic accuracy in ophthalmology.
    • The integration of structural priors enhances the reliability of deep learning-based image enhancement for medical applications.