In spite of the compelling achievements that deep neural networks (DNNs) have made in medical image computing, these deep models often suffer from degraded performance when being applied to new test datasets with domain shift. In this paper, we present a novel unsupervised domain adaptation approach for segmentation tasks by designing semantic-aware generative adversarial networks (GANs). Specifically, we transform the test image into the appearance of source domain, with the semantic structural information being well preserved, which is achieved by imposing a nested adversarial learning in semantic label space. In this way, the segmentation DNN learned from the source domain is able to be directly generalized to the transformed test image, eliminating the need of training a new model for every new target dataset. Our domain adaptation procedure is unsupervised, without using any target domain labels. The adversarial learning of our network is guided by a GAN loss for mapping data distributions, a cycle-consistency loss for retaining pixel-level content, and a semantic-aware loss for enhancing structural information. We validated our method on two different chest X-ray public datasets for left/right lung segmentation. Experimental results show that the segmentation performance of our unsupervised approach is highly competitive with the upper bound of supervised transfer learning.

通过设计语义感知的生成对抗网络，将测试图像转换成源域的外观，保留语义结构信息，并实现语义标签空间内的嵌套对抗学习。通过生成对抗网络映射数据分布、循环一致性保留像素级内容以及语义感知增强结构信息，实现对两个不同的胸部X光公共数据集进行左/右肺分割的无监督领域自适应，创造性地解决了深度学习在 domain shift 上性能下降的问题，并与有监督的迁移学习性能相媲美。

基于语义感知的生成对抗网络在胸部X光分割的无监督领域自适应中的应用