A convolutional neural network (CNN) originally implemented for time-averaged 3D segmentation of the thoracic aorta from 4D flow MRI was retrained to generate time-resolved segmentations without generating additional reference data. To validate the segmentations, automatically generated time-resolved segmentations were compared against two 2D cine acquisitions in 20 patients. The CNN achieved average Dice scores 0.87±0.04 and 0.88±0.04 for candy-cane and cross-section views of the aorta across all patients and timepoints. Automated time-resolved segmentation of 4D flow MRI data will enable calculation of metrics such as wall shear stress and aortic compliance that are sensitive to wall location.
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