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Purpose: To develop and evaluate an end-to-end deep learning pipeline forsegmentation and analysis of cardiac magnetic resonance images to providecore-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 examscollected from 13 institutions in the UK, US and Canada. The data was used totrain and evaluate a pipeline containing three deep-learning models. Thepipeline's performance was assessed on the Dice and IoU score between theautomated and reference standard manual segmentation. Cardiac function valueswere calculated from both the automated and manual segmentation and evaluatedusing Bland-Altman analysis and paired t-tests. The overall pipeline wasfurther evaluated qualitatively on 475 unseen patient exams.  Results: For the 50 testing dataset, the pipeline achieved a median Dicescore of 0.91 (0.89-0.94) for end-diastolic volume, 0.86 (0.82-0.89) forend-systolic volume, and 0.74 (0.70-0.77) for myocardial mass. The deeplearning-derived end-diastolic volume, end-systolic volume, myocardial mass,stroke volume and ejection fraction had no statistical difference compared tothe same values derived from manual segmentation with p values all greater than0.05. For the 475 unseen patient exams, the pipeline achieved 68% adequatesegmentation in both systole and diastole, 26% needed minor adjustments ineither systole or diastole, 5% needed major adjustments, and the cropping modelonly 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 >4500cardiac magnetic resonance exams currently in the FORCE registry as well as anynew patients that are recruited.

Deep Learning Pipeline for Preprocessing and Segmenting Cardiac Magnetic Resonance of Single Ventricle Patients from an Image Registry