1. ZHAW digitalcollection
  2. Life Sciences und Facility Management
  3. Publikationen
  4. Publikationen Life Sciences und Facility Management
Full metadata record
DC FieldValueLanguage
dc.contributor.authorJuchler, Norman-
dc.contributor.authorSchilling, Sabine-
dc.contributor.authorBijlenga, Philippe-
dc.contributor.authorKurtcuoglu, Vartan-
dc.contributor.authorHirsch, Sven-
dc.date.accessioned2018-10-23T14:37:48Z-
dc.date.available2018-10-23T14:37:48Z-
dc.date.issued2016-09-01-
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/12075-
dc.description.abstractIt is exceedingly challenging to assess the clinical significance of intracranial aneurysms. Currently, clinicians associate aneurysm shape irregularity with wall instability. However, there is no consensus on which shape features reliably predict aneurysm rupture risk. Here we present a machine learning approach to tackle this problem: We implemented a classification pipeline to identify shape features with predictive power of aneurysm instability. 3D models of aneurysms are extracted from medical imaging data (mostly 3D rotational angiography) using a standardized vessel segmentation protocol. A variety of established representations of the 3D shape are calculated for the extracted aneurysm segment. These include the calculation of Zernike moment invariants (ZMI) and simpler geometry indices such as undulation, ellipticity and non-sphericity. Feature reduction techniques (for ZMI) and classification methods are applied to find patterns linking shape features to aneurysm stability in an exploratory way. This processing pipeline was applied to a clinical dataset of approximately 250 aneurysms registered in the AneurysmDataBase (SwissNeuroFoundation and AneuriskWeb database. Classification based on ZMI alone allowed us to distinguish between sidewall and bifurcation aneurysms, but failed to forecast an aneurysm’s rupture status reliably. Remarkably, simpler geometry indices performed similarly well in rupture status prediction. It remains to be investigated whether further stratification of the aneurysms in terms of location, size and clinical factors will increase the robustness of the applied classification methods. This study was performed within the scope of the AneuX project, funded by SystemsX.ch, and received support by SNSF NCCR Kidney.CH.de_CH
dc.language.isoende_CH
dc.rightsLicence according to publishing contractde_CH
dc.subjectShape-based risk assessmentde_CH
dc.subjectIntracranial aneurysmsde_CH
dc.subject.ddc616: Innere Medizin und Krankheitende_CH
dc.titleShape-based assessment of intracranial aneurysm disease status – a machine learning approachde_CH
dc.typeKonferenz: Posterde_CH
dcterms.typeTextde_CH
zhaw.departementLife Sciences und Facility Managementde_CH
zhaw.organisationalunitInstitut für Computational Life Sciences (ICLS)de_CH
zhaw.conference.detailsAll SystemsX.ch Day, Bern, 1 September 2016de_CH
zhaw.funding.euNode_CH
zhaw.originated.zhawYesde_CH
zhaw.publication.statuspublishedVersionde_CH
zhaw.publication.reviewPeer review (Abstract)de_CH
zhaw.webfeedBiomedical Simulationde_CH
zhaw.webfeedMedical Image Analysis & Data Modelingde_CH
zhaw.funding.zhawAneuXde_CH
Appears in collections:Publikationen Life Sciences und Facility Management

Files in This Item:
There are no files associated with this item.
Show simple item record
Juchler, N., Schilling, S., Bijlenga, P., Kurtcuoglu, V., & Hirsch, S. (2016, September 1). Shape-based assessment of intracranial aneurysm disease status – a machine learning approach. All SystemsX.ch Day, Bern, 1 September 2016.
Juchler, N. et al. (2016) ‘Shape-based assessment of intracranial aneurysm disease status – a machine learning approach’, in All SystemsX.ch Day, Bern, 1 September 2016.
N. Juchler, S. Schilling, P. Bijlenga, V. Kurtcuoglu, and S. Hirsch, “Shape-based assessment of intracranial aneurysm disease status – a machine learning approach,” in All SystemsX.ch Day, Bern, 1 September 2016, Sep. 2016.
JUCHLER, Norman, Sabine SCHILLING, Philippe BIJLENGA, Vartan KURTCUOGLU und Sven HIRSCH, 2016. Shape-based assessment of intracranial aneurysm disease status – a machine learning approach. In: All SystemsX.ch Day, Bern, 1 September 2016. Conference poster. 1 September 2016
Juchler, Norman, Sabine Schilling, Philippe Bijlenga, Vartan Kurtcuoglu, and Sven Hirsch. 2016. “Shape-Based Assessment of Intracranial Aneurysm Disease Status – a Machine Learning Approach.” Conference poster. In All SystemsX.ch Day, Bern, 1 September 2016.
Juchler, Norman, et al. “Shape-Based Assessment of Intracranial Aneurysm Disease Status – a Machine Learning Approach.” All SystemsX.ch Day, Bern, 1 September 2016, 2016.


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.