Hepatic segmentation is an important but tedious clinical task used in a variety of applications. Existing techniques are relatively narrow in scope, requiring a particular type of MRI sequence or CT for accurate segmentation. We developed a Convolutional Neural Network (CNN) capable of automated liver segmentation on single-shot fast spin echo, T1-weighted, or opposed phase proton-density (OP-PD) weighted sequences using separate training/validation and testing data sets. Compared to human segmenters, the CNN performed well, with volumetric DICE coefficients of 0.92-0.95. The CNN performed least consistently on OP-PD sequences, which had the smallest number of cases in the training/validation data set.