Generating future fundus images for early age-related macular degeneration based on generative adversarial networks

• Published in: Computer methods and programs in biomedicine 2022-04, Vol.216, p.106648-106648, Article 106648
• Main Authors: Pham, Quang T.M., Ahn, Sangil, Shin, Jitae, Song, Su Jeong
• Format: Article
• Language: English
• Subjects: Age related macular degeneration; Fundus image; Fundus Oculi; Generative adversarial network (GAN); Humans; Macular Degeneration - diagnostic imaging; Retina
• ISSN: 0169-2607; 1872-7565
• DOI: 10.1016/j.cmpb.2022.106648

•The first attempt to generate future fundus images based on current fundus images for early age-related macular degeneration (AMD) patients based on deep learning model.•Exploit the drusen segmentation mask for improving the performance.•Introduce a GAN-based model with two discriminators for preserving the identity and utilizing drusen masks.•Develop a fundus dataset for our task. Background and objective: Age-related macular degeneration (AMD) is one of the most common diseases that can lead to blindness worldwide. Recently, various fundus image analyzing studies are done using deep learning methods to classify fundus images to aid diagnosis and monitor AMD disease progression. But until now, to the best of our knowledge, no attempt was made to generate future synthesized fundus images that can predict AMD progression. In this paper, we developed a deep learning model using fundus images for AMD patients with different time elapses to generate synthetic future fundus images. Method: We exploit generative adversarial networks (GANs) with additional drusen masks to maintain the pathological information. The dataset included 8196 fundus images from 1263 AMD patients. A proposed GAN-based model, called Multi-Modal GAN (MuMo-GAN), was trained to generate synthetic predicted-future fundus images. Results: The proposed deep learning model indicates that the additional drusen masks can help to learn the AMD progression. Our model can generate future fundus images with appropriate pathological features. The drusen development over time is depicted well. Both qualitative and quantitative experiments show that our model is more efficient to monitor the AMD disease as compared to other studies. Conclusion: This study could help individualized risk prediction for AMD patients. Compared to existing methods, the experimental results show a significant improvement in terms of tracking the AMD stage in both image-level and pixel-level.