Figure 1.

Illustration of the pipelines in model training and model application for the conversion from fundus images to OCT thickness maps. A, Model training: Color fundus images were inputted into the U-Net for generating OCT thickness maps. In addition, model parameter correction was performed using raw OCT images and the compound loss function (CLFun.) proposed in this study. Images similar to the original OCT thickness maps were generated. B, Model application: Only the color fundus images of the patients were required to generate output OCT thickness maps, which can be used for early glaucoma diagnosis. Conv = convolution layer; PSNR = peak signal-to-noise ratio; ReLU = rectified linear unit; SSIM = structural similarity index measure.