Aneurysm shape as a diagnostic tool : a machine learning approach

Juchler, Norman; Schilling, Sabine; Bijlenga, Philippe; Rüfenacht, Daniel; Kurtcuoglu, Vartan; Hirsch, Sven

Publication type:	Conference other
Type of review:	No review
Title:	Aneurysm shape as a diagnostic tool : a machine learning approach
Authors:	Juchler, Norman Schilling, Sabine Bijlenga, Philippe Rüfenacht, Daniel Kurtcuoglu, Vartan Hirsch, Sven
Conference details:	International Neurovascular Exploratory Workshop (iNEW'2018), Zürich, 7-9 February 2018
Issue Date:	8-Feb-2018
Language:	English
Subjects:	Intracranial aneurysms
Subject (DDC):	006: Special computer methods 616: Internal medicine and diseases
Abstract:	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
Fulltext version:	Published version
License (according to publishing contract):	Licence according to publishing contract
Departement:	Life Sciences and Facility Management
Organisational Unit:	Institute of Computational Life Sciences (ICLS)
Published as part of the ZHAW project:	AneuX
Appears in collections:	Publikationen Life Sciences und Facility Management

Files in This Item:

There are no files associated with this item.

Show full item record

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.