![]() ![]() Mean pressure and velocity fields across 99 planes along the vessel centrelines were extracted, and a mean average percentage error (MAPE) was calculated for each vessel pair (ML vs GT). CFD simulations were set up from GT and ML segmentations using a semi-automatic meshing and simulation pipeline. Segmentation performance was primarily evaluated using Dice score. ![]() These were used to train and optimize a U-Net model, using a 70-10-10 train-validation-test split. 3D CMR images were manually segmented to obtain ground-truth (GT) background, aorta and pulmonary artery labels. Methodsĩ0 CHD patients were retrospectively selected for this study. This study aims to develop and validate a machine learning (ML) method for segmentation of the aorta and pulmonary arteries for CFD studies. However, segmentation is time-consuming and requires expert input. This requires patient-specific anatomy, typically obtained from segmented 3D cardiovascular magnetic resonance (CMR) images. Computational fluid dynamics (CFD) is increasingly used for the assessment of blood flow conditions in patients with congenital heart disease (CHD). ![]()
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