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Normscan: open-source Python software to create average models from CT scans

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International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

Age-matched average 3D models facilitate both surgical planning and intraoperative guidance of cranial birth defects such as craniosynostosis. We aimed to develop an algorithm that accepts any number of CT scans as input and generates highly accurate, average models with minimal user input that are ready for 3D printing and clinical use.

Methods

Using a compiled database of ‘normal’ pediatric computed tomography (CT) scans, we report Normscan, an open-source platform built in Python that allows users to generate normative models of CT scans through user-defined landmarks. We use the basion, nasion, and left and right porions as anatomical landmarks for initial correspondence and then register the models using the iterative closest points algorithm before downstream averaging.

Results

Normscan is fast and easy to use via our user interface and also creates highly accurate average models of any number of input models. Additionally, it is highly repeatable, with coefficients of variance for the surface area and volume of the average model being less than 3% across ten independent trials. Average models can then be 3D printed and/or visualized in augmented reality.

Conclusions

Normscan provides an end-to-end pipeline for the creation of average models of skulls. These models can be used for the generation of databases of specific demographic anatomical models as well as for intraoperative guidance and surgical planning. While Normscan was designed for craniosynostosis repair, due to the modular nature of the algorithm, Normscan has many applications in other areas of surgical planning and research.

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Acknowledgements

We gratefully acknowledge Robert Finedor for thoughtful discussions on techniques to optimize the algorithm. We also thank the Alkureishi lab for their support, thoughtful academic guidance on this project, and for creating a wonderful working environment.

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Authors and Affiliations

  1. Division of Plastic, Reconstructive, and Cosmetic Surgery, The Craniofacial Center, University of Illinois at Chicago, 811 S Paulina St., Chicago, IL, 60612, USA

    George R. Nahass, Mitchell A. Marques, Naji Bou Zeid, Linping Zhao & Lee W. T. Alkureishi

  2. University of Illinois College of Medicine, Chicago, IL, 60612, USA

    George R. Nahass & Mitchell A. Marques

  3. Richard and Loan Hill Department of Biomedical Engineering, University of Illinois at Chicago, Chicago, IL, 60612, USA

    George R. Nahass & Lee W. T. Alkureishi

  4. Shriners Children’s Chicago Hospital, Chicago, IL, 60707, USA

    Lee W. T. Alkureishi

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  1. George R. Nahass
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  2. Mitchell A. Marques
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  4. Linping Zhao
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Contributions

GRN and LA designed the study; GRN performed the experiments and analyzed the data; MAM and NBZ performed statistical analysis, LA supervised the experiments; GRN, NBZ, and LA wrote the manuscript.

Corresponding author

Correspondence to Lee W. T. Alkureishi.

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Conflicts of interest

The authors have no conflicts of interest.

Previous presentations

This work was presented as a poster at the The Midwest Association of Plastic Surgeons 61st Annual Meeting in July 2023 in Lake Geneva, WI.

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Nahass, G.R., Marques, M.A., Bou Zeid, N. et al. Normscan: open-source Python software to create average models from CT scans. Int J CARS (2024). https://doi.org/10.1007/s11548-024-03185-0

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