| Publikationstyp: | Konferenz: Sonstiges |
| Art der Begutachtung: | Keine Begutachtung |
| Titel: | Aneurysm shape as a diagnostic tool : a machine learning approach |
| Autor/-in: | Juchler, Norman Schilling, Sabine Bijlenga, Philippe Rüfenacht, Daniel Kurtcuoglu, Vartan Hirsch, Sven |
| Angaben zur Konferenz: | International Neurovascular Exploratory Workshop (iNEW'2018), Zürich, 7-9 February 2018 |
| Erscheinungsdatum: | 8-Feb-2018 |
| Sprache: | Englisch |
| Schlagwörter: | Intracranial aneurysms |
| Fachgebiet (DDC): | 006: Spezielle Computerverfahren 616: Innere Medizin und Krankheiten |
| Zusammenfassung: | Recent 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. |
| URI: | https://digitalcollection.zhaw.ch/handle/11475/12079 |
| Volltext Version: | Publizierte Version |
| Lizenz (gemäss Verlagsvertrag): | Lizenz gemäss Verlagsvertrag |
| Departement: | Life Sciences und Facility Management |
| Organisationseinheit: | Institut für Computational Life Sciences (ICLS) |
| Publiziert im Rahmen des ZHAW-Projekts: | AneuX |
| Enthalten in den Sammlungen: | Publikationen Life Sciences und Facility Management |
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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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