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뇌과학이미징연구단
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Two-step deep neural network for segmentation of deep white matter hyperintensities in migraineurs

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dc.contributor.authorJisu Hong-
dc.contributor.authorBo-yong Park-
dc.contributor.authorMi Ji Lee-
dc.contributor.authorChin-Sang Chung-
dc.contributor.authorJihoon Cha-
dc.contributor.authorHyunjin Park-
dc.date.available2019-10-11T08:05:45Z-
dc.date.created2019-09-30-
dc.date.issued2020-01-
dc.identifier.issn0169-2607-
dc.identifier.urihttps://pr.ibs.re.kr/handle/8788114/6254-
dc.description.abstractBackground and Objective: Patients with migraine show an increased presence of white matter hyperin- tensities (WMHs), especially deep WMHs. Segmentation of small, deep WMHs is a critical issue in man- aging 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 F 1 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. ©2019 Elsevier B.V. All rights reserved.-
dc.description.uri1-
dc.language영어-
dc.publisherELSEVIER IRELAND LTD-
dc.titleTwo-step deep neural network for segmentation of deep white matter hyperintensities in migraineurs-
dc.typeArticle-
dc.type.rimsART-
dc.identifier.wosid000498062700023-
dc.identifier.scopusid2-s2.0-85073763385-
dc.identifier.rimsid69991-
dc.contributor.affiliatedAuthorJisu Hong-
dc.contributor.affiliatedAuthorBo-yong Park-
dc.contributor.affiliatedAuthorHyunjin Park-
dc.identifier.doi10.1016/j.cmpb.2019.105065-
dc.identifier.bibliographicCitationCOMPUTER METHODS AND PROGRAMS IN BIOMEDICINE, v.183, pp.105068-
dc.citation.titleCOMPUTER METHODS AND PROGRAMS IN BIOMEDICINE-
dc.citation.volume183-
dc.citation.startPage105068-
dc.description.journalClass1-
dc.description.isOpenAccessN-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.subject.keywordAuthorDeep neural network-
dc.subject.keywordAuthorDeep white matter hyperintensity-
dc.subject.keywordAuthorMigraine-
dc.subject.keywordAuthorSegmentation-
Appears in Collections:
Center for Neuroscience Imaging Research (뇌과학 이미징 연구단) > 1. Journal Papers (저널논문)
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