  Purpose: To develop and evaluate an end-to-end deep learning pipeline for
segmentation and analysis of cardiac magnetic resonance images to provide
core-lab processing for a multi-centre registry of Fontan patients.
  Materials and Methods: This retrospective study used training (n = 175),
validation (n = 25) and testing (n = 50) cardiac magnetic resonance image exams
collected from 13 institutions in the UK, US and Canada. The data was used to
train and evaluate a pipeline containing three deep-learning models. The
pipeline's performance was assessed on the Dice and IoU score between the
automated and reference standard manual segmentation. Cardiac function values
were calculated from both the automated and manual segmentation and evaluated
using Bland-Altman analysis and paired t-tests. The overall pipeline was
further evaluated qualitatively on 475 unseen patient exams.
  Results: For the 50 testing dataset, the pipeline achieved a median Dice
score of 0.91 (0.89-0.94) for end-diastolic volume, 0.86 (0.82-0.89) for
end-systolic volume, and 0.74 (0.70-0.77) for myocardial mass. The deep
learning-derived end-diastolic volume, end-systolic volume, myocardial mass,
stroke volume and ejection fraction had no statistical difference compared to
the same values derived from manual segmentation with p values all greater than
0.05. For the 475 unseen patient exams, the pipeline achieved 68% adequate
segmentation in both systole and diastole, 26% needed minor adjustments in
either systole or diastole, 5% needed major adjustments, and the cropping model
only failed in 0.4%.
  Conclusion: Deep learning pipeline can provide standardised 'core-lab'
segmentation for Fontan patients. This pipeline can now be applied to the >4500
cardiac magnetic resonance exams currently in the FORCE registry as well as any
new patients that are recruited.
