Retinal Optic Disc Segmentation using Conditional Generative Adversarial Network

TL;DR

This paper introduces a cGAN-based method for retinal optic disc segmentation that outperforms existing techniques with high accuracy and fast processing times on public datasets.

Contribution

The paper presents a novel cGAN architecture for optic disc segmentation, demonstrating superior accuracy and efficiency over previous methods.

Findings

Achieved 0.96% Jaccard coefficient on DRISHTI GS1 dataset.

Achieved 0.98% Dice coefficient on RIM-ONE dataset.

Segmentation performed in less than a second on GPU.

Abstract

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.