Custom implant design for large cranial defects

  • Filipe M. M. MarreirosEmail author
  • Y. Heuzé
  • M. Verius
  • C. Unterhofer
  • W. Freysinger
  • W. Recheis
Original Article



The aim of this work was to introduce a computer-aided design (CAD) tool that enables the design of large skull defect (>100 \(\mathrm{cm}^2\)) implants. Functional and aesthetically correct custom implants are extremely important for patients with large cranial defects. For these cases, preoperative fabrication of implants is recommended to avoid problems of donor site morbidity, sufficiency of donor material and quality. Finally, crafting the correct shape is a non-trivial task increasingly complicated by defect size.


We present a CAD tool to design such implants for the neurocranium. A combination of geometric morphometrics and radial basis functions, namely thin-plate splines, allows semiautomatic implant generation. The method uses symmetry and the best fitting shape to estimate missing data directly within the radiologic volume data. In addition, this approach delivers correct implant fitting via a boundary fitting approach.


This method generates a smooth implant surface, free of sharp edges that follows the main contours of the boundary, enabling accurate implant placement in the defect site intraoperatively. The present approach is evaluated and compared to existing methods. A mean error of 89.29 % (72.64–100 %) missing landmarks with an error less or equal to 1 mm was obtained.


In conclusion, the results show that our CAD tool can generate patient-specific implants with high accuracy.


Cranial reconstruction Reconstructive surgery Geometric morphometrics Radial basis functions and thin-plate spline 



The authors would like to thank Brenda Frazier for editorial suggestions. We would like also to thank Philipp Gunz and Demetrios Halazonetis for their explanations of GM methods used in this work. This work was supported by the EU FP6 Marie Curie Actions EVAN, contract number: MRTN-CT-2005-019564.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

For this type of study, formal consent is not required, since all the patient data were anonymized.


  1. 1.
    Parthasarathy J (2014) 3D modeling, custom implants and its future perspectives in craniofacial surgery. Ann Maxillofac Surg 4(1):9–18CrossRefPubMedPubMedCentralGoogle Scholar
  2. 2.
    Liao YL, Lu CF, Wu CT, Lee JD, Lee ST, Sun YN, Wu YT (2013) Using three-dimensional multigrid-based snake and multiresolution image registration for reconstruction of cranial defect. Med Biol Eng Comput 51(19):89–101CrossRefPubMedGoogle Scholar
  3. 3.
    Rotaru H, Stan H, Florian IS, Schumacher R, Park YT, Kim SG, Chezan H, Balc N, Baciut M (2012) Cranioplasty with custom-made implants: analyzing the cases of 10 patients. J Oral Maxillofac Surg 70(2):169–176CrossRefGoogle Scholar
  4. 4.
    Liao YL, Lu CF, Sun YN, Wu CT, Lee JD, Lee ST, Wu YT (2011) Three-dimensional reconstruction of cranial defect using active contour model and image registration. Med Biol Eng Comput 49(2):203–211CrossRefPubMedGoogle Scholar
  5. 5.
    Lüthi M, Albrecht T, Vetter T (2009) Building shape models from lousy data. MICCAI LNCS 5762:1–8Google Scholar
  6. 6.
    Wu T, Engelhardt M, Fieten L, Popovic A, Radermacher K (2006) Anatomically constrained deformation for design of cranial implant: Methodology and validation. MICCAI LNCS 4190:9–16Google Scholar
  7. 7.
    Hierl T, Wollny G, Schulze FP, Scholz E, Schmidt JG, Berti G, Hendricks J, Hemprich A (2006) CAD-CAM implants in esthetic and reconstructive craniofacial surgery. J Comput Inf Technol 1:65–70CrossRefGoogle Scholar
  8. 8.
    Dean D, Min KJ, Bond A (2003) Computer aided design of pre-fabricated cranial plates. J Craniofac Surg 14:819–832CrossRefPubMedGoogle Scholar
  9. 9.
    Min KJ, Dean D (2003) Highly accurate CAD tools for cranial implants. MICCAI LNCS 2878:99–107Google Scholar
  10. 10.
    Min KJ, (2003) Computer aided design of cranial implants using deformable templates, PhD thesis, Case Western Reserve University, Cleveland OhioGoogle Scholar
  11. 11.
    Eufinger H, Saylor B (2001) Computer-assisted prefabrication of individual craniofacial implants. AORN J 74:648–654CrossRefPubMedGoogle Scholar
  12. 12.
    Hsu J, Tseng C (2001) Application of three-dimensional orthogonal neural network to craniomaxillary reconstruction. Comput Med Imaging Graph 25:477–482CrossRefPubMedGoogle Scholar
  13. 13.
    Hsu J, Tseng C (2000) Application of three-dimensional orthogonal neural network to craniomaxillary reconstruction. J Med Eng Technol 24:262–266CrossRefPubMedGoogle Scholar
  14. 14.
    Carr JC, Fright WR, Beatson RK (1997) Surface interpolation with radial basis functions for medical imaging. IEEE Trans Med Imaging 16:96–107CrossRefPubMedGoogle Scholar
  15. 15.
    Wehmöller M, Eufinger H, Kruse D, Massberg W (1995) CAD by processing of computed tomography data and CAM of individually designed prostheses. Int J Oral Maxillofac Surg 24:90–97CrossRefPubMedGoogle Scholar
  16. 16.
    Bookstein FL (1991) Morphometric tools for landmark data: geometry and biology. cambridge University Press, CambridgeGoogle Scholar
  17. 17.
    Slice DE (2005) Modern morphometrics in physical anthropology. Kluwer Academic-Prenum Publishers, New YorkCrossRefGoogle Scholar
  18. 18.
    Stegmann MB, Gomez DD (2002) A brief introduction to statistical shape analysis, technical report, informatics and mathematical modelling. Technical University of Denmark, DTU, CopenhagenGoogle Scholar
  19. 19.
    Carr JC, Beatson RK, Cherrie JB, Mitchell TJ, Fright WR, McCallum BC, Evans TR, (2001) Reconstruction and representation of 3D objects with radial basis functions. Computer Graphics (SIGGRAPH 01 Conf. Proc.), :67–76Google Scholar
  20. 20.
    Heuzé Y, Marreiros FMM, Verius M, Eder R, Huttary R, Recheis W (2008) The use of procrustes average shape in the design of custom implant surface for large skull defects. Int J Comput Assist Radiol Surg 3(1):283–284Google Scholar
  21. 21.
    Bookstein FL (1989) Principal warps: thin-plate splines and the decomposition of deformations. IEEE Trans Pattern Anal Mach Intell 6:567–585CrossRefGoogle Scholar
  22. 22.
    Bookstein FL (1997) Landmark methods for forms without landmarks: morphometrics of group differences in outline shape. Med Image Anal 1:225–243CrossRefPubMedGoogle Scholar
  23. 23.
    Gunz P, Mitteroecker P, Bookstein FL (2005) Semilandmarks in three dimensions. In: Modern morphometrics in physical anthropology. Kluwer Academic/Plenum Publishers, New York, pp 73–98Google Scholar
  24. 24.
    Rohlf FJ, Slice D (1990) Extensions of the procrustes method for the optimal superimposition of landmarks. Syst Zool 39:40–59CrossRefGoogle Scholar
  25. 25.
    Schroeder W, Martin K, Lorensen B (1997) The visualization toolkit: an object-oriented approach to 3D graphics. Prentice Hall, New JerseyGoogle Scholar
  26. 26.
    Rosenfeld A, Pfaltz JL (1966) Sequential operations in digital picture processing. J ACM 13(4):471–494CrossRefGoogle Scholar
  27. 27.
    Shapiro LG, Stockman GC (2002) Computer vision. Prentice Hall, New JerseyGoogle Scholar
  28. 28.
    Moore-Jansen PH, Ousely SD, Jantz R (1994) Data collection procedures for forensic skeletal material. University of tennessee Forensic Anthropology Series, TennesseeGoogle Scholar
  29. 29.
    Aeillo L, Dean C (1990) An introduction to human evolutionary anatomy. Academic Press, LondonGoogle Scholar
  30. 30.
    Drebin RA, Carpenter L, Hanrahan P (1988) Volume rendering. Comput Graphics 22(4):65–74Google Scholar
  31. 31.
    Levoy M (1988) Display of surfaces from volume data. IEEE Comput Graphics Appl 8(3):29–37CrossRefGoogle Scholar
  32. 32.
    Levoy M (1990) Efficient ray tracing of volume data. ACM Trans Graph 9:245–261CrossRefGoogle Scholar
  33. 33.
    Sobel I, Feldman G (1968) A \(3 \times 3\) isotropic gradient operator for image processing.
  34. 34.
    Deutsch ES (1972) Thinning algorithms on rectangular, hexagonal, and triangular arrays. Commun ACM 15:827–837CrossRefGoogle Scholar
  35. 35.
    Nyquist H (1928) Certain topics in telegraph transmission theory. Trans Am Inst Elect Eng 47:617–644CrossRefGoogle Scholar
  36. 36.
    Shannon CE (1949) Communication in the presence of noise. Proc of the IRE 37(1):10–21CrossRefGoogle Scholar
  37. 37.
    Jin X, Sun H, Peng Q (2003) Subdivision interpolating implicit surfaces. Comput Graphics 27:763–772CrossRefGoogle Scholar
  38. 38.
    Poukens J, Laeven P, Beerens M, Nijenhuis G, Sloten JV, Stoelinga P, Kessler P (2008) A classification of cranial implants based on the degree of difficulty in computer design and manufacture. Int J Med Robot Comput Assist Surg 4:46–50CrossRefGoogle Scholar
  39. 39.
    Turk G, O’Brien JF (2002) Modelling with implicit surfaces that interpolate. ACM Trans Graph 21:855–873CrossRefGoogle Scholar

Copyright information

© CARS 2016

Authors and Affiliations

  • Filipe M. M. Marreiros
    • 1
    • 2
    • 3
    Email author
  • Y. Heuzé
    • 4
  • M. Verius
    • 5
  • C. Unterhofer
    • 6
  • W. Freysinger
    • 7
  • W. Recheis
    • 5
  1. 1.Center for Medical Image Science and Visualization (CMIV)Linköping UniversityLinköpingSweden
  2. 2.Department of Medical and Health Sciences (IMH)Linköping UniversityLinköpingSweden
  3. 3.Department of Science and Technology (ITN) - Media and Information Technology (MIT)Linköping UniversityLinköpingSweden
  4. 4.University of Bordeaux, UMR 5199 PACEA, Bordeaux Archaeological Sciences Cluster of ExcellenceUniversité de BordeauxPessac CedexFrance
  5. 5.Department of RadiologyInnsbruck Medical UniversityInnsbruckAustria
  6. 6.Clinical Department of NeurosurgeryInnsbruck Medical UniversityInnsbruckAustria
  7. 7.Department of Otorhinolaryngology (ENT), Hearing, Speech and Voice DisordersInnsbruck Medical UniversityInnsbruckAustria

Personalised recommendations