CT to VE: An automated approach to mesh generation and processing from medical datasets for interactive use in immersive simulations for surgical plan

CT scans and MRI images cannot be directly loaded into virtual reality headsets or surgical simulators. This project builds software that automatically converts medical scans into the 3D surface meshes needed for immersive environments, without requiring a technician to manually process each dataset. Currently, surgeons and medical students who want to view a patient’s anatomy in VR must rely on time-consuming, labour-intensive steps to extract usable geometry from standard medical image formats. This bottleneck limits the adoption of mixed-reality tools for surgical planning and education. The researchers aim to automate mesh generation and optimisation so that the process works reliably on both high-end workstations and low-cost consumer hardware. If successful, the framework could make VR-based surgical rehearsal a routine part of preoperative planning, rather than a specialised demonstration. Medical trainees could explore complex anatomical cases interactively, and surgeons could rehearse procedures on patient-specific models without dedicated technical support. The work is primarily a software engineering and computational geometry challenge—it does not develop new imaging techniques or clinical treatments—but it removes a practical barrier that currently keeps immersive simulation out of everyday surgical practice.