Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-19849
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dc.contributor.authorJuchler, Norman-
dc.contributor.authorSchilling, Sabine-
dc.contributor.authorGlüge, Stefan-
dc.contributor.authorBijlenga, Philippe-
dc.contributor.authorRüfenacht, Daniel-
dc.contributor.authorKurtcuoglu, Vartan-
dc.contributor.authorHirsch, Sven-
dc.date.accessioned2020-03-19T15:55:24Z-
dc.date.available2020-03-19T15:55:24Z-
dc.date.issued2020-03-17-
dc.identifier.issn2168-1163de_CH
dc.identifier.issn2168-1171de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/19849-
dc.descriptionThis is an Accepted Manuscript of an article published by Taylor & Francis in Computer Methods in Biomechanics and Biomedical Engineering : Imaging & Visualization on 17.03.2020, available online: https://www.tandfonline.com/doi/full/10.1080/21681163.2020.1728579de_CH
dc.description.abstractThe morphological assessment of anatomical structures is clinically relevant, but often falls short of quantitative or standardised criteria. Whilst human observers are able to assess morphological characteristics qualitatively, the development of robust shape features remains challenging. In this study, we employ psychometric and radiomic methods to develop quantitative models of the perceived irregularity of intracranial aneurysms (IAs). First, we collect morphological characteristics (e.g. irregularity, asymmetry) in imaging-derived data and aggregated the data using rank-based analysis. Second, we compute regression models relating quantitative shape features to the aggregated qualitative ratings (ordinal or binary). We apply our method for quantifying perceived shape irregularity to a dataset of 134 IAs using a pool of 179 different shape indices. Ratings given by 39 participants show good agreement with the aggregated ratings (Spearman rank correlation ρSp=0.84). The best-performing regression model based on quantitative shape features predicts the perceived irregularity with R2:0.84±0.05.de_CH
dc.language.isoende_CH
dc.publisherTaylor & Francisde_CH
dc.relation.ispartofComputer Methods in Biomechanics and Biomedical Engineering : Imaging & Visualizationde_CH
dc.rightsLicence according to publishing contractde_CH
dc.subjectIntracranial aneurysmde_CH
dc.subjectMorphologyde_CH
dc.subjectRadiomicsde_CH
dc.subjectMulti-rater assessmentde_CH
dc.subject.ddc616.8: Neurologie und Krankheiten des Nervensystemsde_CH
dc.titleRadiomics approach to quantify shape irregularity from crowd-based qualitative assessment of intracranial aneurysmsde_CH
dc.typeBeitrag in wissenschaftlicher Zeitschriftde_CH
dcterms.typeTextde_CH
zhaw.departementLife Sciences und Facility Managementde_CH
zhaw.organisationalunitInstitut für Computational Life Sciences (ICLS)de_CH
dc.identifier.doi10.1080/21681163.2020.1728579de_CH
dc.identifier.doi10.21256/zhaw-19849-
zhaw.funding.euNode_CH
zhaw.issue5de_CH
zhaw.originated.zhawYesde_CH
zhaw.pages.end546de_CH
zhaw.pages.start538de_CH
zhaw.publication.statusacceptedVersionde_CH
zhaw.volume8de_CH
zhaw.embargo.end2021-03-18de_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
zhaw.webfeedBiomedical Simulationde_CH
zhaw.funding.zhawAneuXde_CH
zhaw.author.additionalNode_CH
Appears in collections:Publikationen Life Sciences und Facility Management

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