Fusing feature and output space for unsupervised domain adaptation on medical image segmentation

Abstract

Image segmentation requires large amounts of annotated data. However, collecting massive datasets with annotations is difficult since they are expensive and labor-intensive. The unsupervised domain adaptation (UDA) for image segmentation is a promising approach to address the label-scare problem on the target domain, which enables the trained model on the source labeled domain to be adaptive to the target domain. The adversarial-based methods encourage extracting the domain-invariant features by training a domain discriminator to mitigate the domain gap. Existing UDA segmentation methods fail to obtain satisfied segmentation results as they only consider the global knowledge of output space while neglecting the local information of feature space. In this paper, a fusing feature and output (FFO) space method is proposed for UDA, which in the context of medical image segmentation. The proposed model is learned by training a more powerful domain discriminator, which considers features extracted from both feature space and output space. Extensive experiments carried out on several medical image datasets show the adaptation effectiveness of our approach in improving the segmentation performance.