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

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 testing, 90% for training). Field Strength/Sequence 3 T/1.5 T; retrospectively gated gradient recalled echo‐based 4D flow MRI. Assessment Accuracy of the 3D CNN segmentations trained on data from single site (single‐site CNNs) and data across both sites (multi‐site CNN) were evaluated by geometrical similarity (Dice score, human segmentation as ground truth) and net flow quantification at the ascending aorta (Qs), main pulmonary artery (Qp), and their balance (Qp/Qs), between human observers, single‐site and multi‐site CNNs. Statistical Tests Kruskal–Wallis test, Wilcoxon rank‐sum test, and Bland–Altman analysis. A P‐value