Header

UZH-Logo

Maintenance Infos

Radiomics approach to quantify shape irregularity from crowd-based qualitative assessment of intracranial aneurysms


Juchler, Norman; Schilling, Sabine; Glüge, Stefan; Bijlenga, Philippe; Rüfenacht, Daniel; Kurtcuoglu, Vartan; Hirsch, Sven (2020). Radiomics approach to quantify shape irregularity from crowd-based qualitative assessment of intracranial aneurysms. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 8(5):538-546.

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.

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.

Statistics

Citations

Dimensions.ai Metrics
5 citations in Web of Science®
5 citations in Scopus®
Google Scholar™

Altmetrics

Downloads

33 downloads since deposited on 25 Mar 2021
10 downloads since 12 months
Detailed statistics

Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:04 Faculty of Medicine > Institute of Physiology
07 Faculty of Science > Institute of Physiology
Dewey Decimal Classification:570 Life sciences; biology
610 Medicine & health
Scopus Subject Areas:Physical Sciences > Computational Mechanics
Physical Sciences > Biomedical Engineering
Health Sciences > Radiology, Nuclear Medicine and Imaging
Physical Sciences > Computer Science Applications
Uncontrolled Keywords:Radiology Nuclear Medicine and imaging, Computational Mechanics, Biomedical Engineering, Computer Science Applications
Language:English
Date:2 September 2020
Deposited On:25 Mar 2021 13:23
Last Modified:26 Mar 2024 02:35
Publisher:Taylor & Francis
ISSN:2168-1163
OA Status:Green
Publisher DOI:https://doi.org/10.1080/21681163.2020.1728579
  • Content: Accepted Version