Normscan is an open-source Python software designed to create average 3D models from CT scans that are ready for 3D printing and clinical use. Developed by George R. Nahass and colleagues, the software aids in surgical planning and intraoperative guidance for cranial birth defects like craniosynostosis.
Normscan utilizes a database of pediatric CT scans to generate normative models. Users input scans, define landmarks (basion, nasion, left and right porions), and the software uses the iterative closest points algorithm for model registration and averaging. This results in highly accurate 3D printable models.
The software is user-friendly, with a simple interface, and ensures high repeatability. The coefficients of variance for the surface area and volume of the average model are less than 3% across ten trials. Models generated by Normscan can be 3D printed or visualized in augmented reality.
Normscan offers a comprehensive pipeline for creating average skull models, beneficial for building demographic-specific anatomical databases and aiding surgical procedures. Initially designed for craniosynostosis repair, its modular design allows application in various surgical planning and research areas.
Normscan’s accuracy, ease of use, and versatility make it a valuable tool for clinical and research applications in 3D model generation from CT scans.
You can read the research paper, titled “Normscan: open-source Python software to create average models from CT scans” at this link.