Abstract #0088

Machine learning for automatic three-dimensional segmentation of the aorta in 4D flow MRI

Martijn Froeling¹, Emile S. Farag², R. Nils Planken³, Tim Leiner¹, and Pim van Ooij³

¹Department of Radiology, University Medical Center Utrecht, Utrecht, Netherlands, ²Department of Cardiothoracic Surgery, Amsterdam University Medical Centers, AMC, Amsterdam, Netherlands, ³Department of Radiology & Nuclear Medicine, Amsterdam University Medical Centers, AMC, Amsterdam, Netherlands

In this study we present a machine learning convolutional neural network (CNN) for automatic segmentation of the aorta used for peak systolic wall shear stress (WSS) assessment from 4D flow MRI data. The automated three-dimensional WSS profiles (WSS_MACHINE) were compared with WSS calculated using manually (WSS_MAN) created segmentations. Bland-Altman and orthogonal regression analysis revealed good agreement between WSS_MAN and WSS_MACHINE in terms of small mean differences and slopes and intercepts close to unity and zero respectively. The CNN has the ability to drastically accelerate aortic segmentation from 4D flow MRI data, which will greatly improve the clinical applicability of WSS.

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