Year: 2026
Type: article
Abstract: Introduction Virtual reality (VR) is seeing widespread use in medicine to assist with patient communication, medical education and training, and surgical planning. For the clinical team, VR has been shown to reduce the mental load associated with creating mental models of complicated surgical cases. Segmenting 3D medical images into digital twins, 3D models of patient specific anatomy, is a complicated, multi-step process requiring technical expertise. Methods We present a flexible pipeline and code that encompasses image processing, 3D segmentation, and mesh creation developed to convert clinical 3D medical images into VR-ready patient specific models. This pipeline is accompanied by example data and code. Results The pipeline produces VR-compatible models of patient-specific anatomy from 3D medical images, and the accompanying code, example dataset, and guidelines allow the workflow to be reproduced and adapted. Discussion By lowering the barrier of entry to converting medical images into VR, this work enables non-technical researchers and clinical experts to leverage VR for visualization of patient-specific anatomy across clinical, educational, and research domains.
Source: Frontiers in Virtual Reality
Institution: University of Illinois Urbana-Champaign
Language: English
Subfield: Biomedical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,353
OpenAlex ID: W7172324201