  This paper proposed a retinal image segmentation method based on conditional
Generative Adversarial Network (cGAN) to segment optic disc. The proposed model
consists of two successive networks: generator and discriminator. The generator
learns to map information from the observing input (i.e., retinal fundus color
image), to the output (i.e., binary mask). Then, the discriminator learns as a
loss function to train this mapping by comparing the ground-truth and the
predicted output with observing the input image as a condition.Experiments were
performed on two publicly available dataset; DRISHTI GS1 and RIM-ONE. The
proposed model outperformed state-of-the-art-methods by achieving around 0.96%
and 0.98% of Jaccard and Dice coefficients, respectively. Moreover, an image
segmentation is performed in less than a second on recent GPU.
