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Towards automatic classification of cardiovascular magnetic resonance Task Force Criteria for diagnosis of arrhythmogenic right ventricular cardiomyopathy

Bourfiss, Mimount; Sander, Jorg; de Vos, Bob D; Te Riele, Anneline SJM; Asselbergs, Folkert W; Isgum, Ivana; Velthuis, Birgitta K; (2022) Towards automatic classification of cardiovascular magnetic resonance Task Force Criteria for diagnosis of arrhythmogenic right ventricular cardiomyopathy. Clinical Research in Cardiology 10.1007/s00392-022-02088-x. (In press). Green open access

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Abstract

Background: Arrhythmogenic right ventricular cardiomyopathy (ARVC) is diagnosed according to the Task Force Criteria (TFC) in which cardiovascular magnetic resonance (CMR) imaging plays an important role. Our study aims to apply an automatic deep learning-based segmentation for right and left ventricular CMR assessment and evaluate this approach for classification of the CMR TFC. Methods: We included 227 subjects suspected of ARVC who underwent CMR. Subjects were classified into (1) ARVC patients fulfilling TFC; (2) at-risk family members; and (3) controls. To perform automatic segmentation, a Bayesian Dilated Residual Neural Network was trained and tested. Performance of automatic versus manual segmentation was assessed using Dice-coefficient and Hausdorff distance. Since automatic segmentation is most challenging in basal slices, manual correction of the automatic segmentation in the most basal slice was simulated (automatic−basal). CMR TFC calculated using manual and automatic−basal segmentation were compared using Cohen’s Kappa (κ). Results: Automatic segmentation was trained on CMRs of 70 subjects (39.6 ± 18.1 years, 47% female) and tested on 157 subjects (36.9 ± 17.6 years, 59% female). Dice-coefficient and Hausdorff distance showed good agreement between manual and automatic segmentations (≥ 0.89 and ≤ 10.6 mm, respectively) which further improved after simulated correction of the most basal slice (≥ 0.92 and ≤ 9.2 mm, p < 0.001). Pearson correlation of volumetric and functional CMR measurements was good to excellent (automatic (r = 0.78–0.99, p < 0.001) and automatic−basal (r = 0.88–0.99, p < 0.001) measurements). CMR TFC classification using automatic−basal segmentations was comparable to manual segmentations (κ 0.98 ± 0.02) with comparable diagnostic performance. Conclusions: Combining automatic segmentation of CMRs with correction of the most basal slice results in accurate CMR TFC classification of subjects suspected of ARVC. Graphical abstract: [Figure not available: see fulltext.].

Type: Article
Title: Towards automatic classification of cardiovascular magnetic resonance Task Force Criteria for diagnosis of arrhythmogenic right ventricular cardiomyopathy
Location: Germany
Open access status: An open access version is available from UCL Discovery
DOI: 10.1007/s00392-022-02088-x
Publisher version: https://doi.org/10.1007/s00392-022-02088-x
Language: English
Additional information: This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Keywords: Science & Technology, Life Sciences & Biomedicine, Cardiac & Cardiovascular Systems, Cardiovascular System & Cardiology, Arrhythmogenic right ventricular cardiomyopathy, Cardiac magnetic resonance imaging, Deep learning, Automatic segmentation, INTERSTUDY REPRODUCIBILITY, VOLUMES
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Health Informatics > Clinical Epidemiology
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Health Informatics
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
URI: https://discovery.ucl.ac.uk/id/eprint/10158562
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