A teacher-student framework with Fourier Transform augmentation for COVID-19 infection segmentation in CT images
DC Field | Value | Language |
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dc.contributor.author | Chen, Han | - |
dc.contributor.author | Jiang, Yifan | - |
dc.contributor.author | Ko, Hanseok | - |
dc.contributor.author | Loew, Murray | - |
dc.date.accessioned | 2022-12-08T09:42:09Z | - |
dc.date.available | 2022-12-08T09:42:09Z | - |
dc.date.created | 2022-12-08 | - |
dc.date.issued | 2023-01 | - |
dc.identifier.issn | 1746-8094 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/146464 | - |
dc.description.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. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | ELSEVIER SCI LTD | - |
dc.title | A teacher-student framework with Fourier Transform augmentation for COVID-19 infection segmentation in CT images | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Ko, Hanseok | - |
dc.identifier.doi | 10.1016/j.bspc.2022.104250 | - |
dc.identifier.scopusid | 2-s2.0-85139030607 | - |
dc.identifier.wosid | 000876862500003 | - |
dc.identifier.bibliographicCitation | BIOMEDICAL SIGNAL PROCESSING AND CONTROL, v.79 | - |
dc.relation.isPartOf | BIOMEDICAL SIGNAL PROCESSING AND CONTROL | - |
dc.citation.title | BIOMEDICAL SIGNAL PROCESSING AND CONTROL | - |
dc.citation.volume | 79 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | Y | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalWebOfScienceCategory | Engineering, Biomedical | - |
dc.subject.keywordAuthor | COVID-19 | - |
dc.subject.keywordAuthor | Infection segmentation | - |
dc.subject.keywordAuthor | Computed tomography | - |
dc.subject.keywordAuthor | Fourier Transform | - |
dc.subject.keywordAuthor | Teacher-student network | - |
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