Segmentation of the Aorta and Pulmonary Arteries Based on 4D Flow MRI in the Pediatric Setting Using Fully Automated Multi-Site, Multi-Vendor, and Multi-Label Dense U-Net.

ABSTRACT:

Background

Automated segmentation using convolutional neural networks (CNNs) have been developed using four-dimensional (4D) flow magnetic resonance imaging (MRI). To broaden usability for congenital heart disease (CHD), training with multi-institution data is necessary. However, the performance impact of heterogeneous multi-site and multi-vendor data on CNNs is unclear.

Purpose

To investigate multi-site CNN segmentation of 4D flow MRI for pediatric blood flow measurement.

Study type

Retrospective.

Population

A total of 174 subjects across two sites (female: 46%; N = 38 healthy controls, N = 136 CHD patients). Participants from site 1 (N = 100), site 2 (N = 74), and both sites (N = 174) were divided into subgroups to conduct 10-fold cross validation (10% for te

SUBMITTER: Fujiwara T 

PROVIDER: S-EPMC9106805 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature