ROP-GAN: an image synthesis method for retinopathy of prematurity based on generative adversarial network

Objective. Training data with annotations are scarce in the intelligent diagnosis of retinopathy of prematurity (ROP), and existing typical data augmentation methods cannot generate data with a high degree of diversity. In order to increase the sample size and the generalization ability of the classification model, we propose a method called ROP-GAN for image synthesis of ROP based on a generative adversarial network. Approach. To generate a binary vascular network from color fundus images, we first design an image segmentation model based on U2-Net that can extract multi-scale features without reducing the resolution of the feature map. The vascular network is then fed into an adversarial autoencoder for reconstruction, which increases the diversity of the vascular network diagram. Then, we design an ROP image synthesis algorithm based on a generative adversarial network, in which paired color fundus images and binarized vascular networks are input into the image generation model to train the generator and discriminator, and attention mechanism modules are added to the generator to improve its detail synthesis ability. Main results. Qualitative and quantitative evaluation indicators are applied to evaluate the proposed method, and experiments demonstrate that the proposed method is superior to the existing ROP image synthesis methods, as it can synthesize realistic ROP fundus images. Significance. Our method effectively alleviates the problem of data imbalance in ROP intelligent diagnosis, contributes to the implementation of ROP staging tasks, and lays the foundation for further research. In addition to classification tasks, our synthesized images can facilitate tasks that require large amounts of medical data, such as detecting lesions and segmenting medical images.