  Computational fluid dynamics (CFD) models are emerging as tools for assisting
in diagnostic assessment of cardiovascular disease. Recent advances in image
segmentation has made subject-specific modelling of the cardiovascular system a
feasible task, which is particularly important in the case of pulmonary
hypertension (PH), which requires a combination of invasive and non-invasive
procedures for diagnosis. Uncertainty in image segmentation can easily
propagate to CFD model predictions, making uncertainty quantification crucial
for subject-specific models. This study quantifies the variability of
one-dimensional (1D) CFD predictions by propagating the uncertainty of network
geometry and connectivity to blood pressure and flow predictions. We analyse
multiple segmentations of an image of an excised mouse lung using different
pre-segmentation parameters. A custom algorithm extracts vessel length, vessel
radii, and network connectivity for each segmented pulmonary network. We
quantify uncertainty in geometric features by constructing probability
densities for vessel radius and length, and then sample from these
distributions and propagate uncertainties of haemodynamic predictions using a
1D CFD model. Results show that variation in network connectivity is a larger
contributor to haemodynamic uncertainty than vessel radius and length.
