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Scientific article
English

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

Published inComputer Methods in Biomechanics and Biomedical Engineering. Imaging & visualization, vol. 8, no. 5, no. 4th MICCAI workshop on Deep Learning in Medical Image Analysis, p. 538-546
Publication date2020
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.

Keywords
  • Intracranial aneurys
  • Mmorphology
  • Radiomics
  • Multi-rater assessment
Citation (ISO format)
JUCHLER, Norman et al. Radiomics approach to quantify shape irregularity from crowd-based qualitative assessment of intracranial aneurysms. In: Computer Methods in Biomechanics and Biomedical Engineering. Imaging & visualization, 2020, vol. 8, n° 5, p. 538–546. doi: 10.1080/21681163.2020.1728579
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ISSN of the journal2168-1163
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