Virtual Imaging Trials (VIT) offer a cost-effective and scalable approach for evaluating medical imaging technologies. Computational phantoms, which mimic real patient anatomy and physiology, play a central role in VIT. However, current phantom libraries face limitations in sample size and diversity. This study presents a framework for creating realistic computational phantoms using a suite of four deep learning segmentation models, with three forms of automated quality control. The result is a release of over 2,500 computational phantoms with up to 140 structures, available in both voxelized and surface mesh formats. Phantoms may be requested at cvit.duke.edu/resources.