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Background <p>While ultra-widefield fluorescein angiography (UWF-FA) is essential for evaluating retinal vascular pathology in diabetic retinopathy (DR), its invasive nature limits its clinical application. This study aimed to develop and evaluate UWFDR-GAN, a generative adversarial network (GAN) framework for translating ultra-widefield color fundus photography (UWF-CFP) into UWF-FA specifically for DR patients.</p> Methods <p>A total of 270 paired UWF-CFP and UWF-FA images were collected from patients with DR, comprising 73 pairs of mild non-proliferative diabetic retinopathy (NPDR), 47 pairs of moderate NPDR, 82 pairs of severe NPDR, and 68 pairs of proliferative diabetic retinopathy (PDR). We first employed a self-supervised keypoint detection framework for precise cross-modal image registration. The generation network incorporated discrete wavelet transform/inverse transform (DWT/IDWT) to preserve high-frequency details and a Swin Transformer-based multi-scale discriminator to enhance structural realism. We quantitatively compared the performance of our model against several state-of-the-art methods, including pix2pix, pix2pixHD, and UWAFA-GAN, using objective evaluation metrics: the Multi-Scale Structural Similarity Index Measure (MS-SSIM), Peak Signal-to-Noise Ratio (PSNR), Fréchet Inception Distance (FID), and Inception Score (IS).</p> Results <p>UWFDR-GAN achieved the best quantitative performance (MS-SSIM: 0.7214; PSNR: 20.00; FID: 77.48; IS: 1.0123), outperforming all comparison models. Qualitatively, it preserved global vascular architecture and demonstrated superior reconstruction of DR-specific lesions, particularly neovascularization and non-perfusion areas.</p> Conclusions <p>UWFDR-GAN provided a non-invasive ultra-widefield vascular assessment solution for clinical DR management, demonstrating potential to reduce reliance on invasive fluorescein imaging.</p>

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Cross-modality synthesis of ultra-widefield fluorescein angiography from ultra-widefield color fundus photography for diabetic retinopathy via UWFDR-GAN

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月16日

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  • Zhicong Xu,
  • Tao Wang,
  • Dawei Yang,
  • Huilin Liang,
  • Guoye Lin,
  • Anyi Liang,
  • Zikang Xu,
  • Taozheng Li,
  • Danling Huang,
  • Liang Zhang,
  • Qianjin Feng,
  • Dan Cao

摘要

Background

While ultra-widefield fluorescein angiography (UWF-FA) is essential for evaluating retinal vascular pathology in diabetic retinopathy (DR), its invasive nature limits its clinical application. This study aimed to develop and evaluate UWFDR-GAN, a generative adversarial network (GAN) framework for translating ultra-widefield color fundus photography (UWF-CFP) into UWF-FA specifically for DR patients.

Methods

A total of 270 paired UWF-CFP and UWF-FA images were collected from patients with DR, comprising 73 pairs of mild non-proliferative diabetic retinopathy (NPDR), 47 pairs of moderate NPDR, 82 pairs of severe NPDR, and 68 pairs of proliferative diabetic retinopathy (PDR). We first employed a self-supervised keypoint detection framework for precise cross-modal image registration. The generation network incorporated discrete wavelet transform/inverse transform (DWT/IDWT) to preserve high-frequency details and a Swin Transformer-based multi-scale discriminator to enhance structural realism. We quantitatively compared the performance of our model against several state-of-the-art methods, including pix2pix, pix2pixHD, and UWAFA-GAN, using objective evaluation metrics: the Multi-Scale Structural Similarity Index Measure (MS-SSIM), Peak Signal-to-Noise Ratio (PSNR), Fréchet Inception Distance (FID), and Inception Score (IS).

Results

UWFDR-GAN achieved the best quantitative performance (MS-SSIM: 0.7214; PSNR: 20.00; FID: 77.48; IS: 1.0123), outperforming all comparison models. Qualitatively, it preserved global vascular architecture and demonstrated superior reconstruction of DR-specific lesions, particularly neovascularization and non-perfusion areas.

Conclusions

UWFDR-GAN provided a non-invasive ultra-widefield vascular assessment solution for clinical DR management, demonstrating potential to reduce reliance on invasive fluorescein imaging.