Segmentation of the hippocampal formation on T1-weighted structural MR scans is a prerequisite for most imaging studies in Alzheimer’s disease. In this work, we evaluated the performance and accuracy of deep learning-based hippocampus segmentation combined with manual ground truth (GT) data that originates from high-resolution T2-weighted MR images. Results were evaluated against the GT-labels and compared to segmentation results obtained with FreeSurfer. All learning approaches outperformed FreeSurfer in terms of accuracy and speed, where experiments utilizing the T2-based GT-labels yielded the best results. Thus, using T2-weighted images for training a deep learning model can improve automated HC segmentation.