CycleMorph: Cycle consistent unsupervised deformable image registration

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dc.contributor.authorKim, Boahko
dc.contributor.authorKim, Dong Hwanko
dc.contributor.authorPark, Seong Hoko
dc.contributor.authorKim, Jieunko
dc.contributor.authorLee, June-Gooko
dc.contributor.authorYe, Jong Chulko
dc.date.accessioned2021-07-13T05:10:14Z-
dc.date.available2021-07-13T05:10:14Z-
dc.date.created2021-07-13-
dc.date.created2021-07-13-
dc.date.issued2021-07-
dc.identifier.citationMEDICAL IMAGE ANALYSIS, v.71-
dc.identifier.issn1361-8415-
dc.identifier.urihttp://hdl.handle.net/10203/286555-
dc.description.abstractImage registration is a fundamental task in medical image analysis. Recently, many deep learning based image registration methods have been extensively investigated due to their comparable performance with the state-of-the-art classical approaches despite the ultra-fast computational time. However, the existing deep learning methods still have limitations in the preservation of original topology during the deformation with registration vector fields. To address this issues, here we present a cycle-consistent de formable image registration, dubbed CycleMorph. The cycle consistency enhances image registration performance by providing an implicit regularization to preserve topology during the deformation. The proposed method is so flexible that it can be applied for both 2D and 3D registration problems for various applications, and can be easily extended to multi-scale implementation to deal with the memory issues in large volume registration. Experimental results on various datasets from medical and non-medical applications demonstrate that the proposed method provides effective and accurate registration on diverse image pairs within a few seconds. Qualitative and quantitative evaluations on deformation fields also verify the effectiveness of the cycle consistency of the proposed method. (c) 2021 Elsevier B.V. All rights reserved.-
dc.languageEnglish-
dc.publisherELSEVIER-
dc.titleCycleMorph: Cycle consistent unsupervised deformable image registration-
dc.typeArticle-
dc.identifier.wosid000663615600009-
dc.identifier.scopusid2-s2.0-85103691775-
dc.type.rimsART-
dc.citation.volume71-
dc.citation.publicationnameMEDICAL IMAGE ANALYSIS-
dc.identifier.doi10.1016/j.media.2021.102036-
dc.contributor.localauthorYe, Jong Chul-
dc.contributor.nonIdAuthorKim, Dong Hwan-
dc.contributor.nonIdAuthorPark, Seong Ho-
dc.contributor.nonIdAuthorKim, Jieun-
dc.contributor.nonIdAuthorLee, June-Goo-
dc.description.isOpenAccessN-
dc.type.journalArticleArticle-
dc.subject.keywordAuthorCycle consistency-
dc.subject.keywordAuthorImage registration-
dc.subject.keywordAuthorDeep learning-
dc.subject.keywordAuthorUnsupervised learning-
dc.subject.keywordPlusLEARNING FRAMEWORK-
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