Validation of three-dimensional models of the distal femur created from surgical navigation point cloud data for intraoperative and postoperative analysis of total knee arthroplasty

  • David A. J. WilsonEmail author
  • Carolyn Anglin
  • Felix Ambellan
  • Carl Martin Grewe
  • Alexander Tack
  • Hans Lamecker
  • Michael Dunbar
  • Stefan Zachow
Original Article



Despite the success of total knee arthroplasty, there continues to be a significant proportion of patients who are dissatisfied. One explanation may be a shape mismatch between pre- and postoperative distal femurs. The purpose of this study was to investigate methods suitable for matching a statistical shape model (SSM) to intraoperatively acquired point cloud data from a surgical navigation system and to validate these against the preoperative magnetic resonance imaging (MRI) data from the same patients.


A total of 10 patients who underwent navigated total knee arthroplasty also had an MRI scan <2 months preoperatively. The standard surgical protocol was followed which included partial digitization of the distal femur. Two different methods were employed to fit the SSM to the digitized point cloud data, based on (1) iterative closest points and (2) Gaussian mixture models. The available MRI data were manually segmented and the reconstructed three-dimensional surfaces used as ground truth against which the SSM fit was compared.


For both approaches, the difference between the SSM-generated femur and the surface generated from MRI segmentation averaged less than 1.7 mm, with maximum errors occurring in less clinically important areas.


The results demonstrated good correspondence with the distal femoral morphology even in cases of sparse datasets. Application of this technique will allow for measurement of mismatch between pre- and postoperative femurs retrospectively on any case done using the surgical navigation system and could be integrated into the surgical navigation unit to provide real-time feedback.


Navigation Arthroplasty Statistical shape model Knee 



The authors wish to thank the Capital District Health Authority and Alberta Innovates Technology Futures for financial assistance with this project. Parts of this work were also funded by the German Federal Ministry of Education and Research (BMBF), Grant Nos. 01EC1406E and 01EC1408B. The authors would like to thank Valentin Mocanu (Dalhousie University), Robert Joachimsky (ZIB), and Agnieszka Putyra (ZIB) for assisting with the manual segmentations on which this work is based. The authors would also like to thank the reviewers whose excellent comments and feedback resulted in a significantly improved paper.

Compliance with ethical standards


Funding for this study was obtained in part through grants from Capital District Health Authority, Alberta Innovates and the German Federal Ministry of Education and Research.

Conflict of interest

Dr. Dunbar has performed paid consultancy services with Stryker Orthopaedics International, the manufacturer of the navigation system used. None of the other authors have any conflicts of interest to disclose.

Human participants

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

Informed consent

All research undertaken in this study was approved by the institutional ethics board, and informed consent was obtained from all participants.


  1. 1.
    Bourne RB, Chesworth BM, Davis AM, Mahomed NN, Charron KD (2010) Patient satisfaction after total knee arthroplasty: who is satisfied and who is not? Clin Orthop Relat Res 468(1):57–63. doi: 10.1007/s11999-009-1119-9 CrossRefPubMedGoogle Scholar
  2. 2.
    Akbari Shandiz M, Boulos P, Saevarsson SK, Yoo S, Miller S, Anglin C (2016) Changes in knee kinematics following total knee arthroplasty. Proc Inst Mech Eng H 230(4):265–278. doi: 10.1177/0954411916632491
  3. 3.
    Akbari Shandiz M (2015) Component placement in hip and knee replacement surgery: device development, imaging and biomechanics. PhD thesis, University of CalgaryGoogle Scholar
  4. 4.
    Mahfouz M, Abdel Fatah EE, Bowers LS, Scuderi G (2012) Three-dimensional morphology of the knee reveals ethnic differences. Clin Orthop Relat Res 470(1):172–185. doi: 10.1007/s11999-011-2089-2 CrossRefPubMedGoogle Scholar
  5. 5.
    Mahfouz MR, Abdel Fatah EE, Merkl BC, Mitchell JW (2009) Automatic and manual methodology for three-dimensional measurements of distal femoral gender differences and femoral component placement. J Knee Surg 22(4):294–304CrossRefPubMedGoogle Scholar
  6. 6.
    Noble PC, Conditt MA, Cook KF, Mathis KB (2006) The John Insall award: patient expectations affect satisfaction with total knee arthroplasty. Clin Orthop Relat Res 452:35–43. doi: 10.1097/01.blo.0000238825.63648.1e CrossRefPubMedGoogle Scholar
  7. 7.
    Kim SJ, MacDonald M, Hernandez J, Wixson RL (2005) Computer assisted navigation in total knee arthroplasty: improved coronal alignment. J Arthroplasty 20(7 Suppl 3):123–131. doi: 10.1016/j.arth.2005.05.003 CrossRefPubMedGoogle Scholar
  8. 8.
    Rebal BA, Babatunde OM, Lee JH, Geller JA, Patrick DA Jr, Macaulay W (2014) Imageless computer navigation in total knee arthroplasty provides superior short term functional outcomes: a meta-analysis. J Arthroplasty 29(5):938–944. doi: 10.1016/j.arth.2013.09.018 CrossRefPubMedGoogle Scholar
  9. 9.
    Dossett HG, Estrada NA, Swartz GJ, LeFevre GW, Kwasman BG (2014) A randomised controlled trial of kinematically and mechanically aligned total knee replacements: two-year clinical results. Bone Joint J 96–B(7):907–913. doi: 10.1302/0301-620X.96B7.32812 CrossRefPubMedGoogle Scholar
  10. 10.
    Lamecker H, Zachow S (2016) Statistical shape modeling of musculoskeletal structures and its applications. Comput Vis Biomech 23:1–23Google Scholar
  11. 11.
    Fleute M, Lavallee S, Julliard R (1999) Incorporating a statistically based shape model into a system for computer-assisted anterior cruciate ligament surgery. Med Image Anal 3(3):209–222CrossRefPubMedGoogle Scholar
  12. 12.
    Perrin N, Stindel E, Roux C (2005) BoneMorphing versus freehand localization of anatomical landmarks: consequences for the reproducibility of implant positioning in total knee arthroplasty. Comput Aided Surg 10(5–6):301–309. doi: 10.3109/10929080500389845 CrossRefPubMedGoogle Scholar
  13. 13.
    Rajamani KT, Styner MA, Talib H, Zheng G, Nolte LP, Gonzalez Ballester MA (2007) Statistical deformable bone models for robust 3D surface extrapolation from sparse data. Med Image Anal 11(2):99–109. doi: 10.1016/ CrossRefPubMedGoogle Scholar
  14. 14.
    Stindel E, Briard JL, Merloz P, Plaweski S, Dubrana F, Lefevre C, Troccaz J (2002) Bone morphing: 3D morphological data for total knee arthroplasty. Comput Aided Surg 7(3):156–168. doi: 10.1002/igs.10042
  15. 15.
    Baka N, Kaptein BL, de Bruijne M, van Walsum T, Giphart JE, Niessen WJ, Lelieveldt BP (2011) 2D–3D shape reconstruction of the distal femur from stereo X-ray imaging using statistical shape models. Med Image Anal 15(6):840–850. doi: 10.1016/ CrossRefPubMedGoogle Scholar
  16. 16.
    Tsai TY, Li JS, Wang S, Li P, Kwon YM, Li G (2015) Principal component analysis in construction of 3D human knee joint models using a statistical shape model method. Comput Methods Biomech Biomed Eng 18(7):721–729. doi: 10.1080/10255842.2013.843676 CrossRefGoogle Scholar
  17. 17.
    Hacihaliloghlu I, Rasoulian A, Rohling RN, Abolmaesumi P (2013) Statistical shape model to 3D ultrasound registration for spine interventions using enhanced local phase features. Med Image Comput Comput Assist Interv 16(Pt 2):361–368PubMedGoogle Scholar
  18. 18.
    Ehlke M, Heyland M, Mardian S, Duda GN, Zachow S (2015) 3D Assessment of Osteosynthesis based on 2D Radiographs. Paper presented at the Computer Assisted Orthopaedic Surgery VancouverGoogle Scholar
  19. 19.
    Amira for Research Partners (2017).
  20. 20.
    Elfring R, de la Fuente M, Radermacher K (2010) Assessment of optical localizer accuracy for computer aided surgery systems. Comput Aided Surg 15(1–3):1–12. doi: 10.3109/10929081003647239 CrossRefPubMedGoogle Scholar
  21. 21.
    Besl PJMN (1992) A method for registration of 3D shapes. IEEE Trans Pattern Anal Mach Intell 14:239–254CrossRefGoogle Scholar
  22. 22.
    Bernard F, Salamanca L, Thunberg J, Tack A, Jentsch D, Lamecker H, Zachow S, Hertel F, Goncalves J, Gemmar P (2017) Shape-aware surface reconstruction from sparse 3D point-clouds. Med Image Anal 38:77–89CrossRefPubMedGoogle Scholar
  23. 23.
    Lamecker H (2008) Variational and statistical shape modeling for 3D geometry reconstruction (Dissertation). Freie Universitaet BerlinGoogle Scholar
  24. 24.
    Schmid J, Magnenat-Thalmann N (2008) MRI bone segmentation using deformable models and shape priors. Med Image Comput Comput Assist Interv 11(Pt 1):119–126PubMedGoogle Scholar

Copyright information

© CARS 2017

Authors and Affiliations

  • David A. J. Wilson
    • 1
    Email author
  • Carolyn Anglin
    • 3
  • Felix Ambellan
    • 2
  • Carl Martin Grewe
    • 2
  • Alexander Tack
    • 2
  • Hans Lamecker
    • 2
    • 4
  • Michael Dunbar
    • 1
  • Stefan Zachow
    • 2
    • 4
  1. 1.Dalhousie UniversityHalifaxCanada
  2. 2.Zuse Institute Berlin (ZIB)BerlinGermany
  3. 3.University of CalgaryCalgaryCanada
  4. 4.1000 Shapes GmbHBerlinGermany

Personalised recommendations