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Abstract #2647

Deep Learning based Automatic Multi-Regional Segmentation of the Aorta form 4D Flow MRI

Haben Berhane1, Michael Scott1, Justin Baraboo1, Cynthia Rigsby2, Joshua Robinson2, Bradley Allen3, Chris Malaisrie3, Patrick McCarthy3, Ryan Avery3, and Michael Markl1
1Biomedical Engineering, Northwestern University, Chicago, IL, United States, 2Lurie Childrens Hospital of Chicago, Chicago, IL, United States, 3Northwestern Radiology, Evanston, IL, United States

While 4D flow MRI is capable of providing extensive hemodynamic quantifications, it requires cumbersome and time-consuming pre-processing. In order to accelerate the process, we developed and validated a multi-label convolutional neural network (CNN) for automatic aortic 3D segmentation and regional-labeling (ascending, AAo; arch; and descending aorta, DAo). Utilizing 320 4D flow MRI datasets, we used a 10-fold cross validation for training and testing the CNN. The Dice scores across each region were AAo: 0.95 [0.93-0.98], arch: 0.90 [0.89-0.95], and DAo: 0.95 [0.94-0.98]. Across all flow metrics, Bland-Altman comparisons showed moderate to excellent agreement between the manual and automated regional segmentations.

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