Retinal vessel segmentation is an important step for automatic screening of diabetic retinopathy. Currently, most deep learning methods use a large number of labeled samples for network training, but it is difficult to obtain labeled samples in the medical field, and healthy samples and patient samples are imbalanced. In this paper, we have proposed a method of retinal vessel segmentation using few samples based on generating adversarial network. In the generator part, after preprocessing the image by inversing color and other methods, the dataset is expanded by rotation. The UNet structure is used in the network part and the discriminator uses CNN network. In the experimental stage, the training test was applied to DRIVE dataset and HRF dataset. Only 6 samples of the training set were used in the training step, and all the test samples were used in the test step. Finally, the area under the ROC curve of the two datasets reached 097 and 095, and the accuracy rate reached 095 and 094. Compared with UNet in condition of few samples, segmentation performance is improved greatly. It shows that this method is effective for the task of low sample segmentation in retina vessel.