A convolutional neural network (CNN) is presented to quantify 4D flow MRI-based hemodynamics using automated segmentation of the proximal vasculature. The intent is to reduce time and user variability for cumbersome 4D flow MRI analyses; however, the pediatric setting is challenging given the complex arterial geometry often seen in congenital heart diseases. Multi-site and -vender datasets were used to train a CNN for 3D segmentation. Flow quantification was conducted with the automated segmentations to test if datasets from multiple institutions and vendors improves flow quantification. We found the multi-site approach improved flow measurements in the setting of complex disease.