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.
This abstract and the presentation materials are available to members only; a login is required.