A teacher–student framework with Fourier Transform augmentation for COVID-19 infection segmentation in CT images

Han Chen, Yifan Jiang, Hanseok Ko, Murray Loew

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)


Automatic segmentation of infected regions in computed tomography (CT) images is necessary for the initial diagnosis of COVID-19. Deep-learning-based methods have the potential to automate this task but require a large amount of data with pixel-level annotations. Training a deep network with annotated lung cancer CT images, which are easier to obtain, can alleviate this problem to some extent. However, this approach may suffer from a reduction in performance when applied to unseen COVID-19 images during the testing phase, caused by the difference in the image intensity and object region distribution between the training set and test set. In this paper, we proposed a novel unsupervised method for COVID-19 infection segmentation that aims to learn the domain-invariant features from lung cancer and COVID-19 images to improve the generalization ability of the segmentation network for use with COVID-19 CT images. First, to address the intensity difference, we proposed a novel data augmentation module based on Fourier Transform, which transfers the annotated lung cancer data into the style of COVID-19 image. Secondly, to reduce the distribution difference, we designed a teacher–student network to learn rotation-invariant features for segmentation. The experiments demonstrated that even without getting access to the annotations of the COVID-19 CT images during the training phase, the proposed network can achieve a state-of-the-art segmentation performance on COVID-19 infection.

Original languageEnglish
Article number104250
JournalBiomedical Signal Processing and Control
Publication statusPublished - 2023 Jan


  • COVID-19
  • Computed tomography
  • Fourier Transform
  • Infection segmentation
  • Teacher–student network

ASJC Scopus subject areas

  • Signal Processing
  • Biomedical Engineering
  • Health Informatics


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