Retinal Optic Disc Segmentation using Conditional Generative Adversarial Network
This work addresses the problem of accurate and fast optic disc segmentation in retinal images for medical diagnosis, representing an incremental improvement over existing methods.
The paper tackled retinal optic disc segmentation by proposing a conditional Generative Adversarial Network (cGAN) method, achieving Jaccard and Dice coefficients of around 0.96% and 0.98% respectively on two datasets, with segmentation performed in less than a second on a GPU.
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.