Fully automated 3D aortic segmentation of 4D flow MRI for hemodynamic analysis using deep learning.

ABSTRACT:

Purpose

To generate fully automated and fast 4D-flow MRI-based 3D segmentations of the aorta using deep learning for reproducible quantification of aortic flow, peak velocity, and dimensions.

Methods

A total of 1018 subjects with aortic 4D-flow MRI (528 with bicuspid aortic valve, 376 with tricuspid aortic valve and aortic dilation, 114 healthy controls) comprised the data set. A convolutional neural network was trained to generate 3D aortic segmentations from 4D-flow data. Manual segmentations served as the ground truth (N = 499 training, N = 101 validation, N = 418 testing). Dice scores, Hausdorff distance, and average symmetrical surface distance were calculated to assess performance. Aortic flow, peak velocity, and lumen dimensions were quantified at the ascending, arch,

SUBMITTER: Berhane H 

PROVIDER: S-EPMC7724647 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature