  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 due to the
domain shift. In this paper, we propose 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. To
overcome the intensity shift, our method first transforms annotated lung cancer
data into the style of unlabeled COVID-19 data using an effective augmentation
approach via a Fourier transform. Furthermore, to reduce the distribution
shift, we design a teacher-student network to learn rotation-invariant features
for segmentation. Experiments demonstrate that even without getting access to
the annotations of COVID-19 CT during training, the proposed network can
achieve a state-of-the-art segmentation performance on COVID-19 images.
