Upcoming technologies enable routine collection of highly multiplexed (20-60 channel), subcellular resolution images of mammalian tissues for research and diagnosis. Extracting single cell data from such images requires accurate image segmentation, a challenging problem commonly tackled with deep learning. In this paper, we report two findings that substantially improve image segmentation of tissues using a range of machine learning architectures. First, we unexpectedly find that the inclusion of intentionally defocused and saturated images in training data substantially improves subsequent image segmentation. Such real augmentation outperforms computational augmentation (Gaussian blurring). In addition, we find that it is practical to image the nuclear envelope in multiple tissues using an antibody cocktail thereby better identifying nuclear outlines and improving segmentation. The two approaches cumulatively and substantially improve segmentation on a wide range of tissue types. We speculate that the use of real augmentations will have applications in image processing outside of microscopy.