A teacher-student framework with Fourier Transform augmentation for COVID-19 infection segmentation in CT imagesopen access
- Authors
- Chen, Han; Jiang, Yifan; Ko, Hanseok; Loew, Murray
- Issue Date
- 1월-2023
- Publisher
- ELSEVIER SCI LTD
- Keywords
- COVID-19; Infection segmentation; Computed tomography; Fourier Transform; Teacher-student network
- Citation
- BIOMEDICAL SIGNAL PROCESSING AND CONTROL, v.79
- Indexed
- SCOPUS
- Journal Title
- BIOMEDICAL SIGNAL PROCESSING AND CONTROL
- Volume
- 79
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/146464
- DOI
- 10.1016/j.bspc.2022.104250
- ISSN
- 1746-8094
- Abstract
- 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.
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Collections - College of Engineering > School of Electrical Engineering > 1. Journal Articles
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