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Conference Paper: MRI-SegFlow V2.0: a novel unsupervised deep learning pipeline enabling accurate semantic segmentation of lumbar MR images with preliminary validation

TitleMRI-SegFlow V2.0: a novel unsupervised deep learning pipeline enabling accurate semantic segmentation of lumbar MR images with preliminary validation
Authors
Issue Date2021
PublisherInternational Society for the Study of the Lumbar Spine.
Citation
The International Society for the Study of the Lumbar Spine (ISSLS) Virtual Annual Meeting 2021: Reflecting the Past and Embracing the Future, 2-4 June 2021 How to Cite?
DescriptionGeneral Posters - Imaging, biomechanics, Artificial intelligence (AI) - no. GP154
Persistent Identifierhttp://hdl.handle.net/10722/300725

 

DC FieldValueLanguage
dc.contributor.authorKuang, X-
dc.contributor.authorCheung, JPY-
dc.contributor.authorWu, H-
dc.contributor.authorLam, C-
dc.contributor.authorChoy, R-
dc.contributor.authorChan, D-
dc.contributor.authorZhang, T-
dc.date.accessioned2021-06-18T14:56:10Z-
dc.date.available2021-06-18T14:56:10Z-
dc.date.issued2021-
dc.identifier.citationThe International Society for the Study of the Lumbar Spine (ISSLS) Virtual Annual Meeting 2021: Reflecting the Past and Embracing the Future, 2-4 June 2021-
dc.identifier.urihttp://hdl.handle.net/10722/300725-
dc.descriptionGeneral Posters - Imaging, biomechanics, Artificial intelligence (AI) - no. GP154-
dc.languageeng-
dc.publisherInternational Society for the Study of the Lumbar Spine. -
dc.relation.ispartofISSLS (International Society for the Study of the Lumbar Spine) Virtual Annual Meeting 2021-
dc.titleMRI-SegFlow V2.0: a novel unsupervised deep learning pipeline enabling accurate semantic segmentation of lumbar MR images with preliminary validation-
dc.typeConference_Paper-
dc.identifier.emailCheung, JPY: cheungjp@hku.hk-
dc.identifier.emailZhang, T: tgzhang@hku.hk-
dc.identifier.authorityCheung, JPY=rp01685-
dc.identifier.authorityZhang, T=rp02821-
dc.identifier.hkuros322955-

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