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Publication type: Article in scientific journal
Type of review: Peer review (publication)
Title: Radiomics approach to quantify shape irregularity from crowd-based qualitative assessment of intracranial aneurysms
Authors: Juchler, Norman
Schilling, Sabine
Glüge, Stefan
Bijlenga, Philippe
Rüfenacht, Daniel
Kurtcuoglu, Vartan
Hirsch, Sven
et. al: No
DOI: 10.1080/21681163.2020.1728579
Published in: Computer Methods in Biomechanics and Biomedical Engineering : Imaging & Visualization
Volume(Issue): 8
Issue: 5
Page(s): 538
Pages to: 546
Issue Date: 17-Mar-2020
Publisher / Ed. Institution: Taylor & Francis
ISSN: 2168-1163
Language: English
Subjects: Intracranial aneurysm; Morphology; Radiomics; Multi-rater assessment
Subject (DDC): 616.8: Neurology, diseases of nervous system
Abstract: The morphological assessment of anatomical structures is clinically relevant, but often falls short of quantitative or standardised criteria. Whilst human observers are able to assess morphological characteristics qualitatively, the development of robust shape features remains challenging. In this study, we employ psychometric and radiomic methods to develop quantitative models of the perceived irregularity of intracranial aneurysms (IAs). First, we collect morphological characteristics (e.g. irregularity, asymmetry) in imaging-derived data and aggregated the data using rank-based analysis. Second, we compute regression models relating quantitative shape features to the aggregated qualitative ratings (ordinal or binary). We apply our method for quantifying perceived shape irregularity to a dataset of 134 IAs using a pool of 179 different shape indices. Ratings given by 39 participants show good agreement with the aggregated ratings (Spearman rank correlation ρSp=0.84). The best-performing regression model based on quantitative shape features predicts the perceived irregularity with R2:0.84±0.05.
Further description: This is an Accepted Manuscript of an article published by Taylor & Francis in Computer Methods in Biomechanics and Biomedical Engineering : Imaging & Visualization on 17.03.2020, available online:
Fulltext version: Accepted version
License (according to publishing contract): Licence according to publishing contract
Restricted until: 2021-03-18
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

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