Multi-domain CT translation by a routable translation network

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dc.contributor.authorKim, Hyunjongko
dc.contributor.authorOh, Gyutaekko
dc.contributor.authorSeo, Joon Beomko
dc.contributor.authorHwang, Hye Jeonko
dc.contributor.authorLee, Sang Minko
dc.contributor.authorYun, Jihyeko
dc.contributor.authorYe, Jong Chulko
dc.date.accessioned2022-11-02T07:00:28Z-
dc.date.available2022-11-02T07:00:28Z-
dc.date.created2022-11-01-
dc.date.created2022-11-01-
dc.date.created2022-11-01-
dc.date.issued2022-11-
dc.identifier.citationPHYSICS IN MEDICINE AND BIOLOGY, v.67, no.21-
dc.identifier.issn0031-9155-
dc.identifier.urihttp://hdl.handle.net/10203/299267-
dc.description.abstractObjective. To unify the style of computed tomography (CT) images from multiple sources, we propose a novel multi-domain image translation network to convert CT images from different scan parameters and manufacturers by simply changing a routing vector. Approach. Unlike the existing multi-domain translation techniques, our method is based on a shared encoder and a routable decoder architecture to maximize the expressivity and conditioning power of the network. Main results. Experimental results show that the proposed CT image conversion can minimize the variation of image characteristics caused by imaging parameters, reconstruction algorithms, and hardware designs. Quantitative results and clinical evaluation from radiologists also show that our method can provide accurate translation results. Significance. Quantitative evaluation of CT images from multi-site or longitudinal studies has been a difficult problem due to the image variation depending on CT scan parameters and manufacturers. The proposed method can be utilized to address this for the quantitative analysis of multi-domain CT images.-
dc.languageEnglish-
dc.publisherIOP Publishing Ltd-
dc.titleMulti-domain CT translation by a routable translation network-
dc.typeArticle-
dc.identifier.wosid000869315500001-
dc.identifier.scopusid2-s2.0-85140307646-
dc.type.rimsART-
dc.citation.volume67-
dc.citation.issue21-
dc.citation.publicationnamePHYSICS IN MEDICINE AND BIOLOGY-
dc.identifier.doi10.1088/1361-6560/ac950e-
dc.contributor.localauthorYe, Jong Chul-
dc.contributor.nonIdAuthorKim, Hyunjong-
dc.contributor.nonIdAuthorSeo, Joon Beom-
dc.contributor.nonIdAuthorHwang, Hye Jeon-
dc.contributor.nonIdAuthorLee, Sang Min-
dc.contributor.nonIdAuthorYun, Jihye-
dc.description.isOpenAccessN-
dc.type.journalArticleArticle-
dc.subject.keywordAuthorx-ray CT-
dc.subject.keywordAuthordeep learning-
dc.subject.keywordAuthormulti-domain image-to-image translation-
dc.subject.keywordPlusINTERSTITIAL LUNG-DISEASE-
dc.subject.keywordPlusRADIOMICS-
dc.subject.keywordPlusCYCLEGAN-
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