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dc.contributor.authorSchneider, Matthias-
dc.contributor.authorHirsch, Sven-
dc.contributor.authorWeber, Bruno-
dc.contributor.authorSzékely, Gábor-
dc.contributor.authorMenze, Bjoern H.-
dc.date.accessioned2018-12-06T13:13:40Z-
dc.date.available2018-12-06T13:13:40Z-
dc.date.issued2014-
dc.identifier.isbn978-3-319-10469-0de_CH
dc.identifier.isbn978-3-319-10470-6de_CH
dc.identifier.issn0302-9743de_CH
dc.identifier.issn1611-3349de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/13618-
dc.description.abstractThis paper describes a new approach for the reconstruction of complete 3-D arterial trees from partially incomplete image data. We utilize a physiologically motivated simulation framework to iteratively generate artificial, yet physiologically meaningful, vasculatures for the correction of vascular connectivity. The generative approach is guided by a simplified angiogenesis model, while at the same time topological and morphological evidence extracted from the image data is considered to form functionally adequate tree models. We evaluate the effectiveness of our method on four synthetic datasets using different metrics to assess topological and functional differences. Our experiments show that the proposed generative approach is superior to state-of-the-art approaches that only consider topology for vessel reconstruction and performs consistently well across different problem sizes and topologies.de_CH
dc.language.isoende_CH
dc.publisherSpringerde_CH
dc.relation.ispartofseriesLecture Notes in Computer Sciencede_CH
dc.rightsLicence according to publishing contractde_CH
dc.subject.ddc610: Medizin und Gesundheitde_CH
dc.titleTGIF : topological gap in-fill for vascular networksde_CH
dc.typeKonferenz: Paperde_CH
dcterms.typeTextde_CH
zhaw.departementLife Sciences und Facility Managementde_CH
zhaw.organisationalunitInstitut für Computational Life Sciences (ICLS)de_CH
zhaw.publisher.placeChamde_CH
dc.identifier.doi10.1007/978-3-319-10470-6_12de_CH
zhaw.conference.detailsMICCAI, 17th International Conference, Boston, USA, 14-18 September 2014de_CH
zhaw.funding.euNode_CH
zhaw.originated.zhawYesde_CH
zhaw.pages.end96de_CH
zhaw.pages.start89de_CH
zhaw.publication.statuspublishedVersionde_CH
zhaw.series.number8674de_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
zhaw.title.proceedingsMedical Image Computing and Computer-Assisted Intervention – MICCAI 2014 Part IIde_CH
zhaw.webfeedBiomedical Simulationde_CH
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Schneider, M., Hirsch, S., Weber, B., Székely, G., & Menze, B. H. (2014). TGIF : topological gap in-fill for vascular networks [Conference paper]. Medical Image Computing and Computer-Assisted Intervention – MICCAI 2014 Part II, 89–96. https://doi.org/10.1007/978-3-319-10470-6_12
Schneider, M. et al. (2014) ‘TGIF : topological gap in-fill for vascular networks’, in Medical Image Computing and Computer-Assisted Intervention – MICCAI 2014 Part II. Cham: Springer, pp. 89–96. Available at: https://doi.org/10.1007/978-3-319-10470-6_12.
M. Schneider, S. Hirsch, B. Weber, G. Székely, and B. H. Menze, “TGIF : topological gap in-fill for vascular networks,” in Medical Image Computing and Computer-Assisted Intervention – MICCAI 2014 Part II, 2014, pp. 89–96. doi: 10.1007/978-3-319-10470-6_12.
SCHNEIDER, Matthias, Sven HIRSCH, Bruno WEBER, Gábor SZÉKELY und Bjoern H. MENZE, 2014. TGIF : topological gap in-fill for vascular networks. In: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2014 Part II. Conference paper. Cham: Springer. 2014. S. 89–96. ISBN 978-3-319-10469-0
Schneider, Matthias, Sven Hirsch, Bruno Weber, Gábor Székely, and Bjoern H. Menze. 2014. “TGIF : Topological Gap In-Fill for Vascular Networks.” Conference paper. In Medical Image Computing and Computer-Assisted Intervention – MICCAI 2014 Part II, 89–96. Cham: Springer. https://doi.org/10.1007/978-3-319-10470-6_12.
Schneider, Matthias, et al. “TGIF : Topological Gap In-Fill for Vascular Networks.” Medical Image Computing and Computer-Assisted Intervention – MICCAI 2014 Part II, Springer, 2014, pp. 89–96, https://doi.org/10.1007/978-3-319-10470-6_12.


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