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

## TL;DR

This study investigates how image segmentation variability affects one-dimensional CFD predictions of blood flow in mouse pulmonary arteries, highlighting the importance of accounting for segmentation uncertainty in cardiovascular modeling.

## Contribution

It introduces a method to quantify the impact of segmentation uncertainty on CFD blood flow predictions in pulmonary arteries, emphasizing network connectivity as a key factor.

## Key findings

- Network connectivity variability significantly influences haemodynamic predictions.
- Uncertainty in vessel radius and length has a smaller impact on predictions.
- Quantitative analysis of segmentation variability informs more reliable cardiovascular models.

## Abstract

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.

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Source: https://tomesphere.com/paper/1901.04116