Tetralogy of Fallot (ToF) is a congenital heart disease that is typically repaired with surgery early in life, but right ventricular remodeling results in adverse events for many patients. This preliminary analysis of 8 patients investigated the feasibility of training a convolutional neural network to segment the right and left ventricles from 2-dimensional cardiovascular magnetic resonance images, resulting in Dice scores ranging from 0.73-0.91 for the left ventricular blood pool, left ventricular myocardium, and right ventricular blood pool. Machine learning shows promise to enable large-scale longitudinal studies of ToF.
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