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Title |
Enhancing Retinal Fundus Image Segmentation Using GAN |
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Author |
Manal AlGhamdi |
| Citation |
Vol. 24 No. 10 pp. 213-220 |
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Abstract |
Retinal vessel analysis plays a vital role in the detection of some diseases. For example, diabetic retinopathy which may lead to blindness is one of the most common diseases that cause retinal blood vessel structure to change. However, doctors usually take a lot of time and money to collect and label training sets. Thus, automated vessel segmentation as the first step toward computer-aided analysis of fundus remains an active research avenue. We propose an automated Retinal vessel segmentation method based on the GAN network. Traditional image segmentation networks are unsupervised, and GAN is a new semi-supervised network due to adding a Discriminator. By training the discriminator network, we can capture the quality of the generator’s output and drive it closer to the true image features. In our experiment, we use DRIVE dataset for training and testing. The final segmentation effect is represented by the Dice coefficient. Experimental results show that the GAN network can effectively improve the edge effect of image segmentation. Compared with the traditional U-net network, GAN shows about 1.55% higher segmentation accuracy. |
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Keywords |
Retinal image, generative adversarial networks, medical image segmentation. |
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