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Semantic Segmentation of White Matter in FDG-PET Using Generative Adversarial Network

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dc.contributor.author유선국-
dc.contributor.author윤미진-
dc.date.accessioned2020-12-01T17:45:11Z-
dc.date.available2020-12-01T17:45:11Z-
dc.date.issued2020-08-
dc.identifier.issn0897-1889-
dc.identifier.urihttps://ir.ymlib.yonsei.ac.kr/handle/22282913/180429-
dc.description.abstractIn the diagnosis of neurodegenerative disorders, F-18 fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) is used for its ability to detect functional changes at early stages of disease process. However, anatomical information from another modality (CT or MRI) is still needed to properly interpret and localize the radiotracer uptake due to its low spatial resolution. Lack of structural information limits segmentation and accurate quantification of the 18F-FDG PET/CT. The correct segmentation of the brain compartment in 18F-FDG PET/CT will enable the quantitative analysis of the 18F-FDG PET/CT scan alone. In this paper, we propose a method to segment white matter in 18F-FDG PET/CT images using generative adversarial network (GAN). The segmentation result of GAN model was evaluated using evaluation parameters such as dice, AUC-PR, precision, and recall. It was also compared with other deep learning methods. As a result, the proposed method achieves superior segmentation accuracy and reliability compared with other deep learning methods.-
dc.description.statementOfResponsibilityrestriction-
dc.languageEnglish-
dc.publisherSpringer-
dc.relation.isPartOfJOURNAL OF DIGITAL IMAGING-
dc.rightsCC BY-NC-ND 2.0 KR-
dc.titleSemantic Segmentation of White Matter in FDG-PET Using Generative Adversarial Network-
dc.typeArticle-
dc.contributor.collegeCollege of Medicine (의과대학)-
dc.contributor.departmentDept. of Medical Engineering (의학공학교실)-
dc.contributor.googleauthorKyeong Taek Oh-
dc.contributor.googleauthorSangwon Lee-
dc.contributor.googleauthorHaeun Lee-
dc.contributor.googleauthorMijin Yun-
dc.contributor.googleauthorSun K Yoo-
dc.identifier.doi10.1007/s10278-020-00321-5-
dc.contributor.localIdA02471-
dc.contributor.localIdA02550-
dc.relation.journalcodeJ01379-
dc.identifier.eissn1618-727X-
dc.identifier.pmid32043177-
dc.identifier.urlhttps://link.springer.com/article/10.1007/s10278-020-00321-5-
dc.subject.keywordANDI-
dc.subject.keywordDeep learning-
dc.subject.keywordFDG-PET-
dc.subject.keywordGAN-
dc.subject.keywordWhite matter segmentation-
dc.contributor.alternativeNameYoo, Sun Kook-
dc.contributor.affiliatedAuthor유선국-
dc.contributor.affiliatedAuthor윤미진-
dc.citation.volume33-
dc.citation.number4-
dc.citation.startPage816-
dc.citation.endPage825-
dc.identifier.bibliographicCitationJOURNAL OF DIGITAL IMAGING, Vol.33(4) : 816-825, 2020-08-
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Medical Engineering (의학공학교실) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Nuclear Medicine (핵의학교실) > 1. Journal Papers

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