Retinal Optic Disc Segmentation using Conditional Generative Adversarial Network

Vivek Kumar Singh; Hatem Rashwan; Farhan Akram; Nidhi Pandey; Md. Mostaf Kamal Sarker; Adel Saleh; Saddam Abdulwahab; Najlaa Maaroof; Santiago Romani; Domenec Puig

arXiv · 1806.03905

Retinal Optic Disc Segmentation using Conditional Generative Adversarial Network

Vivek Kumar Singh·Hatem Rashwan·Farhan Akram·Nidhi Pandey·Md. Mostaf Kamal Sarker·Adel Saleh·Saddam Abdulwahab·Najlaa Maaroof·Santiago Romani·Domenec Puig

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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.

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Vivek Kumar Singh, Hatem Rashwan, Farhan Akram, Nidhi Pandey, Md. Mostaf Kamal Sarker, Adel Saleh, Saddam Abdulwahab, Najlaa Maaroof, Santiago Romani, Domenec Puig. 2018-06-11. Retinal Optic Disc Segmentation using Conditional Generative Adversarial Network. https://arxiv.org/abs/1806.03905

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