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: internationalNeurovascularExploratoryWorkshop, iNEW'2018, Zurich, Switzerland, 7-9 February 2018
Issue Date: 8-Feb-2018
License (according to publishing contract) : Licence according to publishing contract
Type of review: No review
Language : English
Subjects : Intracranial aneurysms
Subject (DDC) : 005: Computer programming, programs and data
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.
Departement: Life Sciences and Facility Management
Organisational Unit: Institute of Applied Simulation (IAS)
Publication type: Conference Other
URI: https://digitalcollection.zhaw.ch/handle/11475/12079
Appears in Collections:Publikationen Life Sciences und Facility Management

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