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Noise Reduction for SD-OCT Using a Structure-Preserving Domain Transfer Approach

DC Field Value Language
dc.contributor.authorMenglin Wu-
dc.contributor.authorWei Chen-
dc.contributor.authorQiang Chen-
dc.contributor.authorHyunjin Park-
dc.date.accessioned2022-07-29T08:11:11Z-
dc.date.available2022-07-29T08:11:11Z-
dc.date.created2021-06-11-
dc.date.issued2021-09-
dc.identifier.issn2168-2194-
dc.identifier.urihttps://pr.ibs.re.kr/handle/8788114/12090-
dc.description.abstractSpectral-domain optical coherence tomography (SD-OCT) images inevitably suffer from multiplicative speckle noise caused by random interference. This study proposes an unsupervised domain adaptation approach for noise reduction by translating the SD-OCT to the corresponding high-quality enhanced depth imaging (EDI)-OCT. We propose a structure-persevered cycle-consistent generative adversarial network for unpaired image-to-image translation, which can be applied to imbalanced unpaired data, and can effectively preserve retinal details based on a structure-specific cross-domain description. It also imposes smoothness by penalizing the intensity variation of the low reflective region between consecutive slices. Our approach was tested on a local data set that consisted of 268 SD-OCT volumes and two public independent validation datasets including 20 SD-OCT volumes and 17 B-scans, respectively. Experimental results show that our method can effectively suppress noise and maintain the retinal structure, compared with other traditional approaches and deep learning methods in terms of qualitative and quantitative assessments. Our proposed method shows good performance for speckle noise reduction and can assist downstream tasks of OCT analysis.-
dc.language영어-
dc.publisherInstitute of Electrical and Electronics Engineers Inc.-
dc.titleNoise Reduction for SD-OCT Using a Structure-Preserving Domain Transfer Approach-
dc.typeArticle-
dc.type.rimsART-
dc.identifier.wosid000692596400026-
dc.identifier.scopusid2-s2.0-85103918097-
dc.identifier.rimsid75811-
dc.contributor.affiliatedAuthorHyunjin Park-
dc.identifier.doi10.1109/JBHI.2021.3071421-
dc.identifier.bibliographicCitationIEEE Journal of Biomedical and Health Informatics, v.25, no.9, pp.3460 - 3472-
dc.relation.isPartOfIEEE Journal of Biomedical and Health Informatics-
dc.citation.titleIEEE Journal of Biomedical and Health Informatics-
dc.citation.volume25-
dc.citation.number9-
dc.citation.startPage3460-
dc.citation.endPage3472-
dc.description.journalClass1-
dc.description.journalClass1-
dc.description.isOpenAccessN-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.subject.keywordAuthorimage domain transfer-
dc.subject.keywordAuthorgenerative adversarial network, structure preservation-
dc.subject.keywordAuthoroptical coherence tomography-
dc.subject.keywordAuthorspeckle noise reduction-
Appears in Collections:
Center for Neuroscience Imaging Research (뇌과학 이미징 연구단) > 1. Journal Papers (저널논문)
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