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Publikationstyp: Beitrag in wissenschaftlicher Zeitschrift
Art der Begutachtung: Peer review (Publikation)
Titel: Radiomics approach to quantify shape irregularity from crowd-based qualitative assessment of intracranial aneurysms
Autor/-in: Juchler, Norman
Schilling, Sabine
Glüge, Stefan
Bijlenga, Philippe
Rüfenacht, Daniel
Kurtcuoglu, Vartan
Hirsch, Sven
et. al: No
DOI: 10.1080/21681163.2020.1728579
10.21256/zhaw-19849
Erschienen in: Computer Methods in Biomechanics and Biomedical Engineering : Imaging & Visualization
Band(Heft): 8
Heft: 5
Seite(n): 538
Seiten bis: 546
Erscheinungsdatum: 17-Mär-2020
Verlag / Hrsg. Institution: Taylor & Francis
ISSN: 2168-1163
2168-1171
Sprache: Englisch
Schlagwörter: Intracranial aneurysm; Morphology; Radiomics; Multi-rater assessment
Fachgebiet (DDC): 616.8: Neurologie und Krankheiten des Nervensystems
Zusammenfassung: 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.
Weitere Angaben: 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
URI: https://digitalcollection.zhaw.ch/handle/11475/19849
Volltext Version: Akzeptierte Version
Lizenz (gemäss Verlagsvertrag): Lizenz gemäss Verlagsvertrag
Gesperrt bis: 2021-03-18
Departement: Life Sciences und Facility Management
Organisationseinheit: Institut für Computational Life Sciences (ICLS)
Publiziert im Rahmen des ZHAW-Projekts: AneuX
Enthalten in den Sammlungen:Publikationen Life Sciences und Facility Management

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