Improving retinal images segmentation using styleGAN image augmentation

Retinal vessels segmentation algorithms have a great significance for diagnosis of various blood-related diseases such as diabetes, high blood pressure, etc. In addition, every one of us has a different retinal vascular tree so it can be also used as a bio-metric identification. This paper describes our work to take up a challenge∗ to build the best segmentation model of blood vessels out of retinal images. One of the issues in deep learning in the medical domain is the lack of sufficient labeled data, and the DRIVE dataset given in the challenge is no exception. Therefore, in this work we propose a method to improve the performance of the state-of-the-art retinal images segmentation model by synthesizing new retinal images using StyleGAN and their corresponding segmentation maps created by a segmentation network. We show that training with additional generated images improves the segmentation performance.