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Conference Paper: A semi-supervised level-set loss for white matter hyperintensities segmentation on FLAIR without manual labels

TitleA semi-supervised level-set loss for white matter hyperintensities segmentation on FLAIR without manual labels
Authors
Huang, FXia, PVardhanabhuti, VHui, ESKLau, GKKMak, HKFCao, P
Issue Date2021
PublisherInternational Society for Magnetic Resonance in Medicine.
Citation
Proceedings of the 29th Annual Meeting & Exhibition of the International Society for Magnetic Resonance in Medicine (ISMRM), Virtual Conference, Vancouver, BC, Canada, 15-20 May 2021, paper no. 3493 How to Cite?
AbstractWe propose a semi-supervised training scheme for white matter hyperintensity (WMHs) segmentation using V-Net on FLAIR images. The training procedure does not require manual labeling data but only a few domain knowledge of WMHs. The segmentation result obtained by the V-Net with the proposed scheme outperformed that obtained by the supervised loss with manual labels, showing great potential and generalizability in medical image applications.
DescriptionSession Number: D-63 - Digital Posters: Emerging Applications of AI in Neuroimaging for CES I - no. 3493
Persistent Identifierhttp://hdl.handle.net/10722/305515

 

DC FieldValueLanguage
dc.contributor.authorHuang, F-
dc.contributor.authorXia, P-
dc.contributor.authorVardhanabhuti, V-
dc.contributor.authorHui, ESK-
dc.contributor.authorLau, GKK-
dc.contributor.authorMak, HKF-
dc.contributor.authorCao, P-
dc.date.accessioned2021-10-20T10:10:30Z-
dc.date.available2021-10-20T10:10:30Z-
dc.date.issued2021-
dc.identifier.citationProceedings of the 29th Annual Meeting & Exhibition of the International Society for Magnetic Resonance in Medicine (ISMRM), Virtual Conference, Vancouver, BC, Canada, 15-20 May 2021, paper no. 3493-
dc.identifier.urihttp://hdl.handle.net/10722/305515-
dc.descriptionSession Number: D-63 - Digital Posters: Emerging Applications of AI in Neuroimaging for CES I - no. 3493-
dc.description.abstractWe propose a semi-supervised training scheme for white matter hyperintensity (WMHs) segmentation using V-Net on FLAIR images. The training procedure does not require manual labeling data but only a few domain knowledge of WMHs. The segmentation result obtained by the V-Net with the proposed scheme outperformed that obtained by the supervised loss with manual labels, showing great potential and generalizability in medical image applications.-
dc.languageeng-
dc.publisherInternational Society for Magnetic Resonance in Medicine.-
dc.relation.ispartofISMRM (International Society of Magnetic Resonance Imaging) Virtual Conference & Exhibition, 2021-
dc.titleA semi-supervised level-set loss for white matter hyperintensities segmentation on FLAIR without manual labels-
dc.typeConference_Paper-
dc.identifier.emailHuang, F: fhuang@hku.hk-
dc.identifier.emailVardhanabhuti, V: varv@hku.hk-
dc.identifier.emailLau, GKK: gkklau@hku.hk-
dc.identifier.emailMak, HKF: makkf@hku.hk-
dc.identifier.emailCao, P: caopeng1@hku.hk-
dc.identifier.authorityVardhanabhuti, V=rp01900-
dc.identifier.authorityLau, GKK=rp01499-
dc.identifier.authorityMak, HKF=rp00533-
dc.identifier.authorityCao, P=rp02474-
dc.identifier.hkuros326806-
dc.identifier.spage3493-
dc.identifier.epage3493-

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