When making computational fluid dynamics (CFD) based estimations of wall shear stress (WSS) in the human aorta, medical image converting processes to 3D geometries are important as the result is strongly dependent on the quality of the geometry [1]. The image interpretation process or segmentation can be more or less automated; however in clinical work today the gold standard is to manually interpret the medical image information. This combined magnetic resonance imaging (MRI) and CFD method aims to estimate WSS in human arteries in-vivo as WSS is strongly linked to atherosclerosis [2]. More or less automated segmentation has been used in previous studies but normally based on a stack of 2D individually segmented slices which is combined into a 3D model [3]. The aim of this work is to compare manual 2D and automatic 3D segmentations.

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