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Background The digitization of biological specimens has revolutionized morphology, generating massive 3D datasets such as microCT scans. While open-source platforms like 3D Slicer and SlicerMorph have democratized access to advanced visualization and analysis software, a significant “compute gap” persists. Processing high-resolution 3D data requires high-end GPUs and substantial RAM, resources that are frequently unavailable at Primarily Undergraduate Institutions (PUIs) and other educational settings. This “digital divide” prevents many researchers and students from utilizing the very data and software that have been made open to them. Methods We present MorphoCloud, a platform designed to bridge this hardware barrier by providing on-demand, research-grade computing environments via a web browser. MorphoCloud utilizes an “IssuesOps” architecture, where users manage their remote workstations entirely through GitHub Issues using natural-language commands (e.g., /create, /unshelve). The technology stack leverages GitHub Issues and Actions for front-end and orchestration respectively, JetStream2 for backend compute, and Apache Guacamole to deliver a high-performance, GPU-accelerated desktop experience to any modern browser. Results The platform enables a streamlined lifecycle for remote instances, which come pre-configured with the SlicerMorph ecosystem, R/RStudio, and AI-assisted segmentation tools like NNInteractive and MEMOs. Users have access to a persistent storage volume that is decoupled from the instance. For educational purposes, MorphoCloud supports “Workshop” instances that allow for bulk provisioning and stay online continuously for short-term events. This identical environment ensures that instructors can conduct complex 3D workflows without the typical troubleshooting delays caused by heterogeneous student hardware. Conclusion MorphoCloud demonstrates that true scientific accessibility requires not just open data and software, but also open infrastructure. By abstracting the complexities of cloud administration into a simple, command-driven interface, MorphoCloud empowers researchers at under-resourced institutions to engage in high-performance morphological analysis and AI-assisted segmentation.