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Abstract #2229

Verification of fully automated deep learning-based 4D segmentation of the thoracic aorta from 4D flow MRI

Michael Baran Scott1, Haben Baran Berhane2, Kevin Ryan Kalisz1, Tugce Agirlar Trabzonlu1, Jeesoo Lee1, Marci Messina1, Chris Malaisrie1, Patrick McCarthy1, James Carr1, Alexander J Barker3, Ryan Avery1, and Michael Markl1
1Northwestern University, Chicago, IL, United States, 2Lurie Children's Hospital of Chicago, Chicago, IL, United States, 3University of Colorado, Anschutz Medical Campus, Aurora, CO, United States

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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