Utility of deep learning networks for the generation of artificial cardiac magnetic resonance images in congenital heart disease.

ABSTRACT:

Background

Deep learning algorithms are increasingly used for automatic medical imaging analysis and cardiac chamber segmentation. Especially in congenital heart disease, obtaining a sufficient number of training images and data anonymity issues remain of concern.

Methods

Progressive generative adversarial networks (PG-GAN) were trained on cardiac magnetic resonance imaging (MRI) frames from a nationwide prospective study to generate synthetic MRI frames. These synthetic frames were subsequently used to train segmentation networks (U-Net) and the quality of the synthetic training images, as well as the performance of the segmentation network was compared to U-Net-based solutions trained entirely on patient data.

Results

Cardiac MRI data from 303 patients with Tetralog

SUBMITTER: Diller GP 

PROVIDER: S-EPMC7542728 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature