Automatic Segmentation of Drusen and Exudates on Color Fundus Images using Generative Adversarial Networks

J. Engelberts, C. González-Gonzalo, C. Sánchez and M. van Grinsven

Association for Research in Vision and Ophthalmology 2019.


   Methods: We used 4179 color fundus images that were acquired during clinical routine. The images were contrast enhanced to increase the contrast between bright lesions and the background. All bright lesions were manually annotated by marking the center point of the lesions. The GAN was trained to estimate the image without bright lesions. The final segmentation was obtained by taking the difference between the input image and the estimated output.

   Results: This method was applied to an independent test set of 52 color fundus images with non-advanced stages of AMD from the European Genetic Database, which were fully segmented for bright lesions by two trained human observers. The method achieved Dice scores of 0.4862 and 0.4849 when compared to the observers, whereas the inter-observer Dice score was 0.5043. The total segmented bright lesion area per image was evaluated using the intraclass correlation (ICC). The method scored 0.8537 and 0.8352 when compared to the observers, whereas the inter-observer ICC was 0.8893.

   Conclusions: The results show the performance is close to the agreement between trained observers. This automatic segmentation of bright lesions can help early diagnosis of visual threatening diseases and opens the way for large scale clinical trials.