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Thickness and design features of clinical cranial implants—what should automated methods strive to replicate?

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

Abstract

Purpose

New deep learning and statistical shape modelling approaches aim to automate the design process for patient-specific cranial implants, as highlighted by the MICCAI AutoImplant Challenges. To ensure applicability, it is important to determine if the training data used in developing these algorithms represent the geometry of implants designed for clinical use.

Methods

Calavera Surgical Design provided a dataset of 206 post-craniectomy skull geometries and their clinically used implants. The MUG500+ dataset includes 29 post-craniectomy skull geometries and implants designed for automating design. For both implant and skull shapes, the inner and outer cortical surfaces were segmented, and the thickness between them was measured. For the implants, a ‘rim’ was defined that transitions from the repaired defect to the surrounding skull. For unilateral defect cases, skull implants were mirrored to the contra-lateral side and thickness differences were quantified.

Results

The average thickness of the clinically used implants was 6.0 ± 0.5 mm, which approximates the thickness on the contra-lateral side of the skull (relative difference of −0.3 ± 1.4 mm). The average thickness of the MUG500+ implants was 2.9 ± 1.0 mm, significantly thinner than the intact skull thickness (relative difference of 2.9 ± 1.2 mm). Rim transitions in the clinical implants (average width of 8.3 ± 3.4 mm) were used to cap and create a smooth boundary with the skull.

Conclusions

For implant modelers or manufacturers, this shape analysis quantified differences of cranial implants (thickness, rim width, surface area, and volume) to help guide future automated design algorithms. After skull completion, a thicker implant can be more versatile for cases involving muscle hollowing or thin skulls, and wider rims can smooth over the defect margins to provide more stability. For clinicians, the differing measurements and implant designs can help inform the options available for their patient specific treatment.

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Abbreviations

SSM:

Statistical shape modelling

DL:

Deep learning

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Acknowledgements

The authors would like to thank the AutoImplant Challenge community. Financial support was received from a Catalyst grant (FRN: 169989) from the Canadian Institutes of Health Research (CIHR) and a CREATE grant (543378-2020) from the Natural Sciences and Engineering Research Council of Canada (NSERC), and an INOVAIT grant (2020-1003).

Funding

This study was funded by the Canadian Institutes of Health Research (CIHR) (Catalyst grant FRN: 169989), the Natural Sciences and Engineering Research Council of Canada (NSERC) (CREATE grant 543378-2020), and by an INOVAIT grant through the Government of Canada’s Strategic Innovation Fund (2020-1003).

Author information

Authors and Affiliations

  1. Orthopaedic Biomechanics Laboratory, Sunnybrook Research Institute, Toronto, ON, Canada

    Z. Fishman, Michael Hardisty & C. M. Whyne

  2. Calavera Surgical Design Inc., Toronto, ON, Canada

    James G. Mainprize, Glenn Edwards & Oleh Antonyshyn

  3. Division of Plastic Surgery, Sunnybrook Health Sciences Centre, Toronto, ON, Canada

    Oleh Antonyshyn

  4. Department of Surgery, University of Toronto, Toronto, ON, Canada

    Michael Hardisty & C. M. Whyne

  5. Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada

    C. M. Whyne

Authors
  1. Z. Fishman
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  2. James G. Mainprize
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  3. Glenn Edwards
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  4. Oleh Antonyshyn
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  5. Michael Hardisty
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  6. C. M. Whyne
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Corresponding author

Correspondence to Z. Fishman.

Ethics declarations

Conflict of interest

Calavera Surgical Design Inc specializes in the design of craniofacial implants and surgical planning for reconstruction. For research grants shared between the Orthopaedic Biomechanics Lab and Calavera Surgical Design, Calavera provided in-kind consulting time and industry-matched funding. ZF has received research grants with Calavera Surgical Design Inc. JG Mainprize owns stock in Calavera Surgical Design Inc. GE owns stock in Calavera Surgical Design Inc. OA owns stock in Calavera Surgical Design Inc. MH has received research grants with Calavera Surgical Design Inc. CMW has received research grants with Calavera Surgical Design Inc.

Ethical appoval

Retrospective analysis of the skull data provided by Calavera Surgical Design was approved by the Sunnybrook Research Institute’s ethics board (Sun-4824). Skull geometries provided by the MUG500+ dataset are publicly available for research.

Human or animal rights

For this retrospective study, formal consent is not required. This article does not contain any studies with human participants or animals performed by any of the authors.

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Fishman, Z., Mainprize, J.G., Edwards, G. et al. Thickness and design features of clinical cranial implants—what should automated methods strive to replicate?. Int J CARS 19, 747–756 (2024). https://doi.org/10.1007/s11548-024-03068-4

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  • DOI: https://doi.org/10.1007/s11548-024-03068-4

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