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Automatic segmentation of infection areas in computed tomography (CT) imageshas proven to be an effective diagnosis approach for COVID-19. However, due tothe limited number of pixel-level annotated medical images, accuratesegmentation remains a major challenge. In this paper, we propose anunsupervised domain adaptation based segmentation network to improve thesegmentation performance of the infection areas in COVID-19 CT images. Inparticular, we propose to utilize the synthetic data and limited unlabeled realCOVID-19 CT images to jointly train the segmentation network. Furthermore, wedevelop a novel domain adaptation module, which is used to align the twodomains and effectively improve segmentation network's generalizationcapability to the real domain. Besides, we propose an unsupervised adversarialtraining scheme, which encourages the segmentation network to learn thedomain-invariant feature, so that the robust feature can be used forsegmentation. Experimental results demonstrate that our method can achievestate-of-the-art segmentation performance on COVID-19 CT images.

Unsupervised domain adaptation based COVID-19 CT infection segmentation network