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DC poleHodnotaJazyk
dc.contributor.authorZettler, Nico
dc.contributor.authorMastmeyer, Andre
dc.contributor.editorSkala, Václav
dc.date.accessioned2021-08-31T05:53:42Z
dc.date.available2021-08-31T05:53:42Z
dc.date.issued2021
dc.identifier.citationWSCG 2021: full papers proceedings: 29. International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, p. 41-50.en
dc.identifier.isbn978-80-86943-34-3
dc.identifier.issn2464-4617
dc.identifier.issn2464–4625(CD/DVD)
dc.identifier.urihttp://hdl.handle.net/11025/45008
dc.format10 s.cs
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.publisherVáclav Skala - UNION Agencycs
dc.rights© Václav Skala - UNION Agencycs
dc.subjectvymezení orgánucs
dc.subjectU-Netcs
dc.subjectarchitekturacs
dc.subjectbřichocs
dc.subjectsegmentacecs
dc.subject3D CT obrazcs
dc.titleComparison of 2D vs. 3D Unet Organ Segmentation in abdominal 3D CT imagesen
dc.typeconferenceObjecten
dc.typekonferenční příspěvekcs
dc.rights.accessopenAccessen
dc.type.versionpublishedVersionen
dc.description.abstract-translatedA two-step concept for 3D segmentation on 5 abdominal organs inside volumetric CT images is presented. Firsteach relevant organ’s volume of interest is extracted as bounding box. The extracted volume acts as input for asecond stage, wherein two compared U-Nets with different architectural dimensions re-construct an organ segmen-tation as label mask. In this work, we focus on comparing 2D U-Nets vs. 3D U-Net counterparts. Our initial resultsindicate Dice improvements of about 6% at maximum. In this study to our surprise, liver and kidneys for instancewere tackled significantly better using the faster and GPU-memory saving 2D U-Nets. For other abdominal keyorgans, there were no significant differences, but we observe highly significant advantages for the 2D U-Net interms of GPU computational efforts for all organs under study.en
dc.subject.translatedorgan boundsen
dc.subject.translatedU-Neten
dc.subject.translatedarchitectureen
dc.subject.translatedabdomenen
dc.subject.translatedsegmentationen
dc.subject.translated3D CT imagesen
dc.identifier.doihttps://doi.org/10.24132/CSRN.2021.3101.5
dc.type.statusPeer-revieweden
Vyskytuje se v kolekcích:WSCG 2021: Full Papers Proceedings

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