Retinal Optic Disc Segmentation using Conditional Generative Adversarial Network

06/11/2018
by   Vivek Kumar Singh, et al.
2

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 segmentation is performed in less than a second on recent GPU.

READ FULL TEXT

Please sign up or login with your details

Forgot password? Click here to reset