针对深度学习算法在脑肿瘤分割中存在标记数据不足的问题,提出了一种基于条件生成对抗网络(CGAN)的低级别胶质瘤(LGG)磁共振(MR)图像自动分割方法。首先,使用原始数据集训练CGAN并生成LGG图像以扩充原始数据集;然后,利用生成图像预训练分割网络;最后,在预训练模型的基础上训练分割模型。实验结果表明,相比常规数据扩充方法,本方法的Dice系数提高了4.39%,Jaccard指数提高了4.42%,为基于MR图像的LGG分割计算机铺助诊断系统提供了参考。

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.