Meeting Banner
Abstract #4734

3D U-Net for Automated Segmentation of the Thoracic Aorta in 4D-Flow derived 3D PC-MRA

Haben Berhane1, Michael Scott2, Joshua Robinson1, Cynthia Rigsby1, and Michael Markl2

1Lurie Childrens Hospital of Chicago, Chicago, IL, United States, 2Northwestern University, Chicago, IL, United States

We developed a 3D convolutional neural network for the automatic segmentation of the thoracic aorta in 4D Flow-derived 3D PC-MRAs. Using 100 testing datasets, we obtained an average dice score of 0.94±0.03 and an average voxel-wise accuracy of 0.99. Additionally, our algorithm is robust enough to accurately segment a wide array of aortic geometries and disease, such as bicuspid aortic value, coarctation, and interrupted aortic arches.

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

Join Here