Speckle noise in optical coherence tomography (OCT) images obscures important morphological details, and hinders the clinical observation and diagnosis of retinal lesions. A structural similarity constrained generative adversarial network ( SSGAN) is proposed for retinal OCT image denoising. The proposed SSGAN utilizes the residual strategy to improve the structure of original generative adversarial network, and incorporates the structural similarity index measure loss into the objective function to achieve more structural constrains while suppressing speckle noise. The experiments on the SD-OCT public dataset published by Duke University show that the peak signal-to-noise ratio and edge preserve index of the proposed method are 28. 08 and 0. 960 respectively, outperforming the other denoising comparison methods. Further experiments demonstrate that the proposed method can be easily applied to other public datasets from the A2A SD-OCT study.