Influence of image segmentation on one-dimensional fluid dynamics predictions in the mouse pulmonary arteries

Colebank, M. J., Paun, L. M., Qureshi, M. U., Chesler, N., Husmeier, D. , Olufsen, M. S. and Ellwein Fix, L. (2019) Influence of image segmentation on one-dimensional fluid dynamics predictions in the mouse pulmonary arteries. Journal of the Royal Society: Interface, 16, 20190284. (doi:10.1098/rsif.2019.0284)

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Abstract

Computational fluid dynamics (CFD) models are emerging tools for assisting in diagnostic assessment of cardiovascular disease. Recent advances in image segmentation have made subject-specific modelling of the cardiovascular system a feasible task, which is particularly important in the case of pulmonary hypertension, requiring a combination of invasive and non-invasive procedures for diagnosis. Uncertainty in image segmentation propagates to CFD model predictions, making the quantification of segmentation-induced uncertainty crucial for subject-specific models. This study quantifies the variability of one-dimensional CFD predictions by propagating the uncertainty of network geometry and connectivity to blood pressure and flow predictions. We analyse multiple segmentations of a single, excised mouse lung using different pre-segmentation parameters. A custom algorithm extracts vessel length, vessel radii and network connectivity for each segmented pulmonary network. Probability density functions are computed for vessel radius and length and then sampled to propagate uncertainties to haemodynamic predictions in a fixed network. In addition, we compute the uncertainty of model predictions to changes in network size and connectivity. Results show that variation in network connectivity is a larger contributor to haemodynamic uncertainty than vessel radius and length.

Item Type:Articles
Additional Information:This work was supported by the NSF-DMS (grant nos 1246991 and 1615820), the EPSRC (grant no. EP/N014642/1), the Royal Society of Edinburgh (grant no. 62335), the NIH (grant no. R01 HL-086939) and the American Heart Foundation (grant no. 19PRE34380459).
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Paun, Miss Luiza Mihaela and Husmeier, Professor Dirk and Qureshi, Mr Umar
Authors: Colebank, M. J., Paun, L. M., Qureshi, M. U., Chesler, N., Husmeier, D., Olufsen, M. S., and Ellwein Fix, L.
College/School:College of Science and Engineering > School of Mathematics and Statistics
College of Science and Engineering > School of Mathematics and Statistics > Statistics
Journal Name:Journal of the Royal Society: Interface
Publisher:The Royal Society
ISSN:1742-5689
ISSN (Online):1742-5662
Copyright Holders:Copyright © 2019 The Authors
First Published:First published in Journal of the Royal Society: Interface 16:20190284
Publisher Policy:Reproduced in accordance with the copyright policy of the publisher

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
694461EPSRC Centre for Multiscale soft tissue mechanics with application to heart & cancerRaymond OgdenEngineering and Physical Sciences Research Council (EPSRC)EP/N014642/1M&S - MATHEMATICS
3056550Inference of cardio-mechanical parameters in real time: moving mathematical modelling into the clinicDirk HusmeierThe Royal Society of Edinburgh (ROYSOCED)62335M&S - Statistics