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dc.contributor.authorJuchler, Norman-
dc.contributor.authorSchilling, Sabine-
dc.contributor.authorBijlenga, Philippe-
dc.contributor.authorRüfenacht, Daniel-
dc.contributor.authorKurtcuoglu, Vartan-
dc.contributor.authorHirsch, Sven-
dc.date.accessioned2018-10-24T08:53:56Z-
dc.date.available2018-10-24T08:53:56Z-
dc.date.issued2018-02-08-
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/12079-
dc.description.abstractRecent studies have found supporting evidence that the shape of an intracranial aneurysm can be used as a proxy for disease status. Although the shape, as seen in 3D imaging data, already plays a role in the clinical assessment of aneurysms today, tools to quantify and compare aneurysm morphology in a generic, standardized way are still lacking. Here, we present a machine learning approach based on a broad spectrum of shape descriptors to predict the aneurysm rupture status. Results are based on a dataset consisting of over 400 segmented aneurysm models. We extended our analysis by including human ratings of aneurysm shape. A correlation analysis of these ratings with quantifiable morphological parameters allowed us to identify shape descriptors mimicking the human assessment. Preliminary results based on 134 geometric aneurysm models and 15 assessments of human raters show that human assessment of irregular shape correlates well with curvature metrics, spread of the writhe number distribution and non-sphericity index.de_CH
dc.language.isoende_CH
dc.rightsLicence according to publishing contractde_CH
dc.subjectIntracranial aneurysmsde_CH
dc.subject.ddc006: Spezielle Computerverfahrende_CH
dc.subject.ddc616: Innere Medizin und Krankheitende_CH
dc.titleAneurysm shape as a diagnostic tool : a machine learning approachde_CH
dc.typeKonferenz: Sonstigesde_CH
dcterms.typeTextde_CH
zhaw.departementLife Sciences und Facility Managementde_CH
zhaw.organisationalunitInstitut für Computational Life Sciences (ICLS)de_CH
zhaw.conference.detailsInternational Neurovascular Exploratory Workshop (iNEW'2018), Zürich, 7-9 February 2018de_CH
zhaw.funding.euNode_CH
zhaw.originated.zhawYesde_CH
zhaw.publication.statuspublishedVersionde_CH
zhaw.publication.reviewKeine Begutachtungde_CH
zhaw.webfeedBiomedical Simulationde_CH
zhaw.webfeedMedical Image Analysis & Data Modelingde_CH
zhaw.funding.zhawAneuXde_CH
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Juchler, N., Schilling, S., Bijlenga, P., Rüfenacht, D., Kurtcuoglu, V., & Hirsch, S. (2018, February 8). Aneurysm shape as a diagnostic tool : a machine learning approach. International Neurovascular Exploratory Workshop (iNEW′2018), Zürich, 7-9 February 2018.
Juchler, N. et al. (2018) ‘Aneurysm shape as a diagnostic tool : a machine learning approach’, in International Neurovascular Exploratory Workshop (iNEW′2018), Zürich, 7-9 February 2018.
N. Juchler, S. Schilling, P. Bijlenga, D. Rüfenacht, V. Kurtcuoglu, and S. Hirsch, “Aneurysm shape as a diagnostic tool : a machine learning approach,” in International Neurovascular Exploratory Workshop (iNEW′2018), Zürich, 7-9 February 2018, Feb. 2018.
JUCHLER, Norman, Sabine SCHILLING, Philippe BIJLENGA, Daniel RÜFENACHT, Vartan KURTCUOGLU und Sven HIRSCH, 2018. Aneurysm shape as a diagnostic tool : a machine learning approach. In: International Neurovascular Exploratory Workshop (iNEW′2018), Zürich, 7-9 February 2018. Conference presentation. 8 Februar 2018
Juchler, Norman, Sabine Schilling, Philippe Bijlenga, Daniel Rüfenacht, Vartan Kurtcuoglu, and Sven Hirsch. 2018. “Aneurysm Shape as a Diagnostic Tool : A Machine Learning Approach.” Conference presentation. In International Neurovascular Exploratory Workshop (iNEW′2018), Zürich, 7-9 February 2018.
Juchler, Norman, et al. “Aneurysm Shape as a Diagnostic Tool : A Machine Learning Approach.” International Neurovascular Exploratory Workshop (iNEW′2018), Zürich, 7-9 February 2018, 2018.


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