PURPOSE: Despite the great potential and flexibility of 4D flow MRI for hemodynamic analysis, a major limitation is the need for time-consuming and user-dependent post-processing. We propose a fast four-step algorithm for rapid, robust, and repeatable flow measurements in the great vessels based on automatic placement of measurement planes and vessel segmentation. METHODS: Our algorithm works by 1) subsampling the 3D image into 3D patches, 2) predicting the probability of each patch containing individual vessels and location/orientation of the vessel within the patch via a convolutional neural network, 3) selecting the predicted planes with highest probabilities for each vessel, and 4) shifting the plane centers to the maximum velocity within each plane. The method was trained on 283 scans and evaluated on 40 unseen scans by comparing algorithm-derived processing times, plane locations, and flow measurements to those of two manual observers (graduate students) using t-tests, Pearson correlation, and Bland-Altman analysis. RESULTS: The average processing time for the algorithm (18 seconds) was shorter than observer 1 (362 seconds; P