Hepatic iron content (HIC) quantification requires segmentation. Deep learning and Frangi Filtering allow to fully automate segmentation. 664 manually segmented data sets were available for training and testing a UNET. Data sets segmented by UNET were Frangi filtered for vessel exclusion, HIC was calculated using a published calibration, and correlated with HIC obtained with manual segmentation. Very good correlation (R2 > 0.99) with a correlation line close to unity was found. Fully automated HIC quantification using deep learning and Frangi filtering can lead to significant time savings in clinical practice.