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postgraduate thesis: Development of a multi-stage V-net model for liver and lesion segmentation in contrast-enhanced abdominal CT scans

TitleDevelopment of a multi-stage V-net model for liver and lesion segmentation in contrast-enhanced abdominal CT scans
Authors
Zhang, Sailong張賽龍
Issue Date2020
PublisherThe University of Hong Kong (Pokfulam, Hong Kong)
Citation
Zhang, S. [張賽龍]. (2020). Development of a multi-stage V-net model for liver and lesion segmentation in contrast-enhanced abdominal CT scans. (Thesis). University of Hong Kong, Pokfulam, Hong Kong SAR.
DegreeMaster of Medical Sciences
SubjectImage segmentation
Liver - Imaging
Diagnostic imaging - Digital techniques
Dept/ProgramDiagnostic Radiology
Persistent Identifierhttp://hdl.handle.net/10722/297707

 

DC FieldValueLanguage
dc.contributor.authorZhang, Sailong-
dc.contributor.author張賽龍-
dc.date.accessioned2021-03-24T02:58:46Z-
dc.date.available2021-03-24T02:58:46Z-
dc.date.issued2020-
dc.identifier.citationZhang, S. [張賽龍]. (2020). Development of a multi-stage V-net model for liver and lesion segmentation in contrast-enhanced abdominal CT scans. (Thesis). University of Hong Kong, Pokfulam, Hong Kong SAR.-
dc.identifier.urihttp://hdl.handle.net/10722/297707-
dc.languageeng-
dc.publisherThe University of Hong Kong (Pokfulam, Hong Kong)-
dc.relation.ispartofHKU Theses Online (HKUTO)-
dc.rightsThe author retains all proprietary rights, (such as patent rights) and the right to use in future works.-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.subject.lcshImage segmentation-
dc.subject.lcshLiver - Imaging-
dc.subject.lcshDiagnostic imaging - Digital techniques-
dc.titleDevelopment of a multi-stage V-net model for liver and lesion segmentation in contrast-enhanced abdominal CT scans-
dc.typePG_Thesis-
dc.description.thesisnameMaster of Medical Sciences-
dc.description.thesislevelMaster-
dc.description.thesisdisciplineDiagnostic Radiology-
dc.description.naturepublished_or_final_version-
dc.date.hkucongregation2021-
dc.identifier.mmsid991044345254803414-

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