This study aims to refine an automated workflow for brain magnetic resonance images (MRIs) that generates three-dimensional (3D) point clouds in the Polygon File Format (.ply) to be deployed into augmented reality (AR) head mount displays (HMD). Our current work involves enhancing and refining core features, along with improving the point-cloud application to optimize the brain MRI intake. Our brain image segmentation algorithm and web-based point cloud generator show promise for clinical workflows, where high-quality point-cloud AR models can be generated from patient MRIs.

We hope to spearhead future development of features for visualizing, manipulating, and annotating medical scans in AR environments. The automated workflow can be implemented with MRI containing color differentiated neuroimaging segments, which can further increase physicians’ abilities to visualize and interpret MRI brain scans. Our brain image segmentation algorithm and web-based point cloud generator show promise for eventual use in clinical workflows, where high-quality point-cloud AR models can be generated from patient MRIs. By expanding the functions of an integrated AR workflow, we hope to spearhead future development of features for visualizing, manipulating, and annotating medical scans in AR environments.