The invention provides a medical
image synthesis method based on double generative adversarial networks. The method comprises the main content of performing
data management, generating the generativeadversarial networks, training a U-NET, establishing assessment indexes and obtaining processed pictures, wherein first, a DRIVE
database is used to manage a first-stage GAN, then the first-stage GANgenerates a partitioning
mask representing
variable geometry of a dataset, a second-stage GAN converts the
mask produced at the first stage into an image with a sense of reality, an generator minimizes a
loss function of the true data in classification through a descriminator, then the U-NET is trained to assess the reliability of
synthetic data, and finally the assessment indexes are establishedto measure a generated model. According to the method, by use of a pair of generative adversarial networks to create a new
image generation path, the problem that the synthetic image contains a fake shadow and
noise is solved, the stability and the sense of reality of the image are improved, and meanwhile image details are clearer.