In order to solve the problem that deep learning algorithm has insufficient labeled data in brain tumor segmentation, in this paper, an automatic segmentation method of low-grade gliomas (LGG) magnetic resonance (MR) images based on conditional generative adversarial networks (CGAN) is proposed. First, the original dataset is used to train the CGAN and generate LGG images to expand the original dataset. Then, the generated images are used to pre-train a segment network. Finally, the segmentation model is trained on the basis of the pre-training model. Experimental results show that compared with traditional data augmentation methods, the proposed method improves the Dice coefficient by 4.39% and Jaccard index by 4.42%. The method provides a reference for the development of a computer assisted diagnosis system for LGG segmentation based on MR images.