FunSyn-Net: enhanced residual variational auto-encoder and image-to-image translation network for Fundus Image Synthesis

Abstract

Medical imaging datasets typically do not contain many training images and are usually not sufficient for training deep learning networks. We propose a deep residual variational auto-encoder and a generative adversarial network based approach that can generate a synthetic retinal fundus image dataset with corresponding blood vessel annotations. In terms of structural statistics comparison of real and artificial our model performed better than existing methods. The generated blood vessel structures achieved a structural similarity value of 0.74 and the artificial dataset achieved a sensitivity of 0.84 and specific city of 0.97 for the blood vessel segmentation task. The successful application of generative models for the generation of synthetic medical data will not only help to mitigate the small dataset problem but will also address the privacy concerns associated with such medical datasets.

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