Liver disease is a significant worldwide health burden, causing over two million deaths worldwide each year. Currently, the only curative treatment for end-stage liver disease is transplant, but a critical organ shortage leaves many patients to die on a waiting list. Regenerative medicine is an important branch of translational research that aims to restore normal function to damaged tissue. To this end, our group and other researchers have developed micropatterned tissue constructs that combine engineered liver cells, bioactive molecules, and biomaterials that expand and restore tissue function after implantation. Despite the therapeutic promise of these tissue-mimetic implants, there remains a significant knowledge gap regarding the engineered tissue’s morphology and its comparability to human liver tissue architecture, severely limiting its clinical translation. Clinicians must know, ideally via quantitative methods, that they are implanting healthy engineered liver tissue, rather than something that inadvertently remodels in the body to recapitulate diseased liver tissue. To address this, I have developed a user-friendly, quantitative tissue morphology analysis software package to aid the informed design of next-generation engineered human liver tissue. This software combines deep learning, morphological transformations, and statistical quantitative image analysis to generate high-throughput, end-to-end histology image analysis pipelines. This software allows researchers to train a deep convolutional neural network that classifies and clusters single cells in both 2D images, as well as 3D volumes. To show the efficacy of this software, analysis pipelines were generated to characterize cellular distribution in mouse embryos and engineered liver tissue constructs. Importantly, this work will provide researchers across many disciplines with a tool to understand the microstructural differences between engineered tissue and native/natural tissue, enabling the development of more physiologically relevant and biomimetic tissue-based therapies and disease models.