Titel: Shape-based assessment of intracranial aneurysm disease status
Autor/-in: Juchler, Norman
Schilling, Sabine
Vartan, Kurtcuoglu
Hirsch, Sven
Angaben zur Konferenz: ZNZ Symposium 2016, Zurich, 15 September 2016
Erscheinungsdatum: 15-Sep-2016
Lizenz (gemäss Verlagsvertrag): Lizenz gemäss Verlagsvertrag
Art der Begutachtung: Keine Angabe
Sprache: Englisch
Schlagwörter: Shape-based risk assessment; Intracranial aneurysms
Fachgebiet (DDC): 616: Innere Medizin und Krankheiten
Zusammenfassung: The risk assessment of intracranial aneurysms is an exceedingly difficult task. Clinicians associate aneurysm shape irregularity with disease instability. However, there is no consensus on which shape features reliably predict aneurysm instability. We have adopted a machine learning approach to identify shape features with predictive power for aneurysm instability: From imaging data 3D models of aneurysms are extracted that are used to train a classifier. A variety of representations of the 3D shape are calculated, these include the Zernike moment invariants (ZMI) and geometry indices such as aspect ratio, ellipticity and non-sphericity. The processing pipeline was applied to synthetic data and clinical datasets of 413 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. Simpler geometry indices performed similarly well in rupture status prediction. On synthetic data we showed that ZMI could encode shape irregularity. 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.
Departement: Life Sciences und Facility Management
Organisationseinheit: Institut für Angewandte Simulation (IAS)
Publikationstyp: Konferenz: Poster
URI: https://digitalcollection.zhaw.ch/handle/11475/12073
Publiziert im Rahmen des ZHAW-Projekts: AneuX
Enthalten in den Sammlungen:Publikationen Life Sciences und Facility Management

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