Unsupervised domain adaptation based COVID-19 CT infection segmentation network

Han Chen, Yifan Jiang, Murray Loew, Hanseok Ko

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)


Automatic segmentation of infection areas in computed tomography (CT) images has proven to be an effective diagnostic approach for COVID-19. However, due to the limited number of pixel-level annotated medical images, accurate segmentation remains a major challenge. In this paper, we propose an unsupervised domain adaptation based segmentation network to improve the segmentation performance of the infection areas in COVID-19 CT images. In particular, we propose to utilize the synthetic data and limited unlabeled real COVID-19 CT images to jointly train the segmentation network. Furthermore, we develop a novel domain adaptation module, which is used to align the two domains and effectively improve the segmentation network’s generalization capability to the real domain. Besides, we propose an unsupervised adversarial training scheme, which encourages the segmentation network to learn the domain-invariant feature, so that the robust feature can be used for segmentation. Experimental results demonstrate that our method can achieve state-of-the-art segmentation performance on COVID-19 CT images.

Original languageEnglish
Pages (from-to)6340-6353
Number of pages14
JournalApplied Intelligence
Issue number6
Publication statusPublished - 2022 Apr


  • Adversarial training
  • Automatic segmentation
  • COVID-19
  • Computed tomography
  • Domain adaptation

ASJC Scopus subject areas

  • Artificial Intelligence


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