Synthesizing images of the eye fundus is a challenging task that has been previously approached by formulating complex models of the anatomy of the eye. New images can then be generated by sampling a suitable parameter space. In this work, we propose a method that learns to synthesize eye fundus images directly from data. For that, we pair true eye fundus images with their respective vessel trees, by means of a vessel segmentation technique. These pairs are then used to learn a mapping from a binary vessel tree to a new retinal image. For this purpose, we use a recent image-to-image translation technique, based on the idea of adversarial learning. Experimental results show that the original and the generated images are visually different in terms of their global appearance, in spite of sharing the same vessel tree. Additionally, a quantitative quality analysis of the synthetic retinal images confirms that the produced images retain a high proportion of the true image set quality.

本文提出了一种通过血管分割技术直接从数据中学习综合眼底图像的方法，并使用基于对抗学习的图像转换技术将二进制血管树映射到新的视网膜图像，其实验结果以定量方法证实生成的图像保留了真实图像集合的大部分质量。

针对对抗性视网膜图像合成的研究