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Two-step Deep Neural Network for Segmentation of Deep White Matter Hyperintensities in Migraineurs

DC FieldValueLanguage
dc.contributor.author차지훈-
dc.date.accessioned2020-06-17T00:24:10Z-
dc.date.available2020-06-17T00:24:10Z-
dc.date.issued2020-01-
dc.identifier.issn0169-2607-
dc.identifier.urihttps://ir.ymlib.yonsei.ac.kr/handle/22282913/175949-
dc.description.abstractBackground and objective: Patients with migraine show an increased presence of white matter hyperintensities (WMHs), especially deep WMHs. Segmentation of small, deep WMHs is a critical issue in managing migraine care. Here, we aim to develop a novel approach to segmenting deep WMHs using deep neural networks based on the U-Net. Methods: 148 non-elderly subjects with migraine were recruited for this study. Our model consists of two networks: the first identifies potential deep WMH candidates, and the second reduces the false positives within the candidates. The first network for initial segmentation includes four down-sampling layers and four up-sampling layers to sort the candidates. The second network for false positive reduction uses a smaller field-of-view and depth than the first network to increase utilization of local information. Results: Our proposed model segments deep WMHs with a high true positive rate of 0.88, a low false discovery rate of 0.13, and F1 score of 0.88 tested with ten-fold cross-validation. Our model was automatic and performed better than existing models based on conventional machine learning. Conclusion: We developed a novel segmentation framework tailored for deep WMHs using U-Net. Our algorithm is open-access to promote future research in quantifying deep WMHs and might contribute to the effective management of WMHs in migraineurs.-
dc.description.statementOfResponsibilityrestriction-
dc.languageEnglish-
dc.publisherElsevier Scientific Publishers-
dc.relation.isPartOfCOMPUTER METHODS AND PROGRAMS IN BIOMEDICINE-
dc.rightsCC BY-NC-ND 2.0 KR-
dc.titleTwo-step Deep Neural Network for Segmentation of Deep White Matter Hyperintensities in Migraineurs-
dc.typeArticle-
dc.contributor.collegeCollege of Medicine (의과대학)-
dc.contributor.departmentDept. of Radiology (영상의학교실)-
dc.contributor.googleauthorJisu Hong-
dc.contributor.googleauthorBo-Yong Park-
dc.contributor.googleauthorMi Ji Lee-
dc.contributor.googleauthorChin-Sang Chung-
dc.contributor.googleauthorJihoon Cha-
dc.contributor.googleauthorHyunjin Park-
dc.identifier.doi10.1016/j.cmpb.2019.105065-
dc.contributor.localIdA05808-
dc.relation.journalcodeJ00637-
dc.identifier.eissn1872-7565-
dc.identifier.pmid31522090-
dc.identifier.urlhttps://www.sciencedirect.com/science/article/pii/S0169260719305851-
dc.subject.keywordDeep neural network-
dc.subject.keywordDeep white matter hyperintensity-
dc.subject.keywordMigraine-
dc.subject.keywordSegmentation-
dc.contributor.alternativeNameCha, Jihoon-
dc.contributor.affiliatedAuthor차지훈-
dc.citation.volume183-
dc.citation.startPagee105065-
dc.identifier.bibliographicCitationCOMPUTER METHODS AND PROGRAMS IN BIOMEDICINE, Vol.183 : e105065, 2020-01-
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Radiology (영상의학교실) > 1. Journal Papers

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