1. Radiomics approach to quantify shape irregularity from crowd-based qualitative assessment of intracranial aneurysms
- Author
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Sabine Schilling, Norman Juchler, Philippe Bijlenga, Sven Hirsch, Stefan Glüge, Vartan Kurtcuoglu, Daniel Rüfenacht, University of Zurich, Juchler, Norman, and Hirsch, Sven
- Subjects
Intracranial aneurys ,Morphology ,2206 Computational Mechanics ,Computer science ,Biomedical Engineering ,Computational Mechanics ,2204 Biomedical Engineering ,610 Medicine & health ,616.8: Neurologie und Krankheiten des Nervensystems ,02 engineering and technology ,10052 Institute of Physiology ,030218 nuclear medicine & medical imaging ,03 medical and health sciences ,0302 clinical medicine ,Radiomics ,Computational mechanics ,1706 Computer Science Applications ,0202 electrical engineering, electronic engineering, information engineering ,2741 Radiology, Nuclear Medicine and Imaging ,Radiology, Nuclear Medicine and imaging ,Programming method ,Intracranial aneurysm ,Data science ,Computer Science Applications ,Visualization ,ddc:616.8 ,Radiology Nuclear Medicine and imaging ,570 Life sciences ,biology ,Mmorphology ,020201 artificial intelligence & image processing ,Multi-rater assessment - Abstract
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: https://www.tandfonline.com/doi/full/10.1080/21681163.2020.1728579, 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.
- Published
- 2020