ArticleJournal of the Royal Society, Interface2019
Influence of image segmentation on one-dimensional fluid dynamics predictions in the mouse pulmonary arteries.
Article in Journal of the Royal Society, Interface, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed, 24 citations in OpenAlex.
- A One-Dimensional (1D) Computational Fluid Dynamics Study of Fontan-Associated Liver Disease (FALD).International journal for numerical methods in biomedical engineering · 2026Article
- Digital twins for noninvasively measuring predictive markers of right heart failure.NPJ digital medicine · 2025Article
- Dissecting contributions of pulmonary arterial remodeling to right ventricular afterload in pulmonary hypertension.Bioengineering & translational medicine · 2025Article
- Parameter selection and optimization of a computational network model of blood flow in single-ventricle patients.Journal of the Royal Society, Interface · 2025Article
- Article
- A computational study of aortic reconstruction in single ventricle patients.Biomechanics and modeling in mechanobiology · 2023Article
- Geometric Uncertainty in Patient-Specific Cardiovascular Modeling with Convolutional Dropout Networks.Computer methods in applied mechanics and engineering · 2021Article
- A multiscale model of vascular function in chronic thromboembolic pulmonary hypertension.American journal of physiology. Heart and circulatory physiology · 2021Article
- Markov chain Monte Carlo with Gaussian processes for fast parameter estimation and uncertainty quantification in a 1D fluid-dynamics model of the pulmonary circulation.International journal for numerical methods in biomedical engineering · 2021Article
- Assessing model mismatch and model selection in a Bayesian uncertainty quantification analysis of a fluid-dynamics model of pulmonary blood circulation.Journal of the Royal Society, Interface · 2020Article
- Image-based scaling laws for somatic growth and pulmonary artery morphometry from infancy to adulthood.American journal of physiology. Heart and circulatory physiology · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 4 institutions in 2 countries.
Funding
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.