Abstract
As part of the 2021 MICCAI AutoImplant Challenge, CT scans from 11 patients who had undergone cranioplasty using artificial implants were collected. Images of the reconstructed defective skulls before cranioplasty for these patients were shared with participating teams. Three teams submitted cranial implant designs. An experienced neurosurgeon evaluated the submissions to judge the feasibility of the implant designs for use in cranioplasty procedures. None of the submitted cranial implant designs were deemed feasible for use in cranioplasty procedures without modifications. While many implants adequately restored the skull shape by covering the defect area, most contained excess material outside of the defect, fit poorly within the defect and were too thick. Future research should move beyond solely restoring the skull shape and focus on designing implants that contain smooth transitions between skull and implant, cover the entire defect, contain no material outside of the defect, have minimal thickness, and are implantable.
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References
Li, J., Egger, J.: Towards the Automatization of Cranial Implant Design in Cranioplasty. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-64327-0
Avants, B.B., Tustison, N., Song, G.: Advanced normalization tools (ANTS). Insight J 2(365), 1–35 (2009)
Kodym, O., et al.: SkullBreak/SkullFix–Dataset for automatic cranial implant design and a benchmark for volumetric shape learning tasks. Data Brief 35, 106902 (2021)
Kikinis, R., Pieper, S., Vosburgh, K.: 3D slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Jolesz, F.A. (ed.) Intraoperative Imaging and Image-Guided Therapy, pp. 277–289. Springer, New York (2014). https://doi.org/10.1007/978-1-4614-7657-3_19
Li, J., et al.: AutoImplant 2020-first MICCAI challenge on automatic cranial implant design. IEEE Trans. Med. Imaging 40(9), 2329–2342 (2021)
Ellis, D., Aizenberg, M.: Deep learning using augmentation via registration: 1st place solution to the autoimplant 2020 challenge. In: Li, J., Egger, J. (eds.) AutoImplant. LNCS, vol. 12439, pp. 47–55. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-64327-0_6
Mukaka, M.M.: Statistics corner: a guide to appropriate use of correlation coefficient in medical research. Malawi Med. J. J. Med. Assoc. Malawi 24(3), 69–71 (2012)
Kodym, O., Španěl, M., Herout, A.: Deep learning for cranioplasty in clinical practice: going from synthetic to real patient data. Comput. Biol. Med. 137, 104766 (2021)
Li, J., et al.: Automatic skull defect restoration and cranial implant generation for cranioplasty. Med. Image Anal. 73, 102171 (2021)
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Ellis, D.G., Alvarez, C.M., Aizenberg, M.R. (2021). Qualitative Criteria for Feasible Cranial Implant Designs. In: Li, J., Egger, J. (eds) Towards the Automatization of Cranial Implant Design in Cranioplasty II. AutoImplant 2021. Lecture Notes in Computer Science(), vol 13123. Springer, Cham. https://doi.org/10.1007/978-3-030-92652-6_2
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DOI: https://doi.org/10.1007/978-3-030-92652-6_2
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