Application of Statistical Shape Modeling for CAOS: A Tutorial

Chapter

Abstract

Statistical shape models (SSMs) are useful for representing intersubject variabilities of anatomical shapes and anatomical shape deformations specific to diseases (e.g., osteoarthritis) as well as preoperative planning of anatomical reconstructive surgery (e.g., fracture reduction and arthroplasty). This chapter presents the mathematical foundations of such applications for SSMs, especially aiming at intuitive understanding of the role of SSMs in Bayes estimation, which is a basic framework of various estimations and prediction problems, including anatomical reconstruction and diagnostic/therapeutic applications.

Keywords

Musculoskeletal anatomy modeling Bayesian estimation Hip replacement 

References

  1. 1.
    Cootes TF, Taylor CJ, Cooper DH, Graham J. Active shape models-their training and application. Comput Vision Image Understand. 1995;61(1):38–59.CrossRefGoogle Scholar
  2. 2.
    Heimann T, Meinzer H-P. Statistical shape models for 3D medical image segmentation: a review. Med Image Anal. 2009;13(4):543–63.CrossRefGoogle Scholar
  3. 3.
    Sarkalkan N, Weinans H, Zadpoor AA. Statistical shape and appearance models of bones. Bone. 2014;60:129–40.CrossRefGoogle Scholar
  4. 4.
    Lamecker H, Zachow S. Statistical shape modeling of musculoskeletal structures and its applications. In: Computational radiology for orthopaedic interventions. New York, NY: Springer International Publishing; 2016. p. 1–23.Google Scholar
  5. 5.
    Styner MA, Rajamani KT, Nolte L-P, Zsemlye G, Székely G, Taylor CJ, Davies RH. Evaluation of 3D correspondence methods for model building. In: Biennial International Conference on Information Processing in Medical Imaging. Berlin: Springer; 2003. p. 63–75.CrossRefGoogle Scholar
  6. 6.
    Fleute M, Lavallée S. Building a complete surface model from sparse data using statistical shape models: application to computer assisted knee surgery. In: Medical Image Computing and Computer-Assisted Intervention—MICCAI’98. London: Springer; 1998. p. 879–87.CrossRefGoogle Scholar
  7. 7.
    Fleute M, Lavallée S, Julliard R. Incorporating a statistically based shape model into a system for computer-assisted anterior cruciate ligament surgery. Med Image Anal. 1999;3(3):209–22.CrossRefGoogle Scholar
  8. 8.
    Yokota F, Okada T, Takao M, Sugano N, Tada Y, Sato Y. Automated segmentation of the femur and pelvis from 3D CT data of diseased hip using hierarchical statistical shape model of joint structure. Med Image Comput Comput Assist Interv. 2009;12:811–8.PubMedGoogle Scholar
  9. 9.
    Zheng G, Gollmer S, Schumann S, Dong X, Feilkas T, Ballester MAG. A 2D/3D correspondence building method for reconstruction of a patient-specific 3D bone surface model using point distribution models and calibrated X-ray images. Med Image Anal. 2009;13(6):883–99.CrossRefGoogle Scholar
  10. 10.
    Baka N, Kaptein BL, de Bruijne M, van Walsum T, Giphart JE, Niessen WJ, Lelieveldt BPF. 2D–3D shape reconstruction of the distal femur from stereo X-ray imaging using statistical shape models. Med Image Anal. 2011;15(6):840–50.CrossRefGoogle Scholar
  11. 11.
    Barratt DC, Chan CSK, Edwards PJ, Penney GP, Slomczykowski M, Carter TJ, Hawkes DJ. Instantiation and registration of statistical shape models of the femur and pelvis using 3D ultrasound imaging. Med Image Anal. 2008;12(3):358–74.CrossRefGoogle Scholar
  12. 12.
    Baudin P-Y, Azzabou N, Carlier PG, Paragios N. Prior knowledge, random walks and human skeletal muscle segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. Berlin: Springer; 2012. p. 569–76.Google Scholar
  13. 13.
    Fripp J, Crozier S, Warfield SK, Ourselin S. Automatic segmentation and quantitative analysis of the articular cartilages from magnetic resonance images of the knee. IEEE Trans Med Imaging. 2010;29(1):55–64.CrossRefGoogle Scholar
  14. 14.
    Gregory JS, Waarsing JH, Day J, Pols HA, Reijman M, Weinans H, Aspden RM. Early identification of radiographic osteoarthritis of the hip using an active shape model to quantify changes in bone morphometric features: can hip shape tell us anything about the progression of osteoarthritis? Arthritis Rheum. 2007;56(11):3634–43.CrossRefGoogle Scholar
  15. 15.
    Bredbenner TL, Eliason TD, Potter RS, Mason RL, Havill LM, Nicolella DP. Statistical shape modeling describes variation in tibia and femur surface geometry between Control and Incidence groups from the osteoarthritis initiative database. J Biomech. 2010;43(9):1780–6.CrossRefGoogle Scholar
  16. 16.
    Kagiyama Y, Otomaru I, Takao M, Sugano N, Nakamoto M, Yokota F, Tomiyama N, Tada Y, Sato Y. CT-based automated planning of acetabular cup for total hip arthroplasty (THA) based on hybrid use of two statistical atlases. Int J Comput Assist Radiol Surg. 2016;11(12):2253–71.CrossRefGoogle Scholar
  17. 17.
    Gong RH, Stewart J, Abolmaesumi P. Multiple-object 2-D–3-D registration for noninvasive pose identification of fracture fragments. IEEE Trans Biomed Eng. 2011;58(6):1592–601.CrossRefGoogle Scholar
  18. 18.
    Sleiman HB, Ritacco LE, Aponte-Tinao L, Muscolo DL, Nolte L-P, Reyes M. Allograft selection for transepiphyseal tumor resection around the knee using three-dimensional surface registration. Ann Biomed Eng. 2011;39(6):1720–7.CrossRefGoogle Scholar
  19. 19.
    Krol Z, Skadlubowicz P, Hefti F, Krieg AH. Virtual reconstruction of pelvic tumor defects based on a gender-specific statistical shape model. Comput Aided Surg. 2013;18(5-6):142–53.CrossRefGoogle Scholar
  20. 20.
    Rueckert D, Sonoda LI, Hayes C, Hill DLG, Leach MO, Hawkes DJ. Nonrigid registration using free-form deformations: application to breast MR images. IEEE Trans Med Imaging. 1999;18(8):712–21.CrossRefGoogle Scholar
  21. 21.
    Faisal Beg M, Miller MI, Trouvé A, Younes L. Computing large deformation metric mappings via geodesic flows of diffeomorphisms. Int J Comput Vision. 2005;61(2):139–57.CrossRefGoogle Scholar
  22. 22.
    Vercauteren T, Pennec X, Perchant A, Ayache N. Diffeomorphic demons: efficient non-parametric image registration. Neuroimage. 2009;45(1):S61–72.CrossRefGoogle Scholar
  23. 23.
    Frangi AF, Rueckert D, Schnabel JA, Niessen WJ. Automatic construction of multiple-object three-dimensional statistical shape models: application to cardiac modeling. IEEE Trans Med Imaging. 2002;21(9):1151–66.CrossRefGoogle Scholar
  24. 24.
    Yokota F, Okada T, Takao M, Sugano N, Tada Y, Tomiyama N, Sato Y. Automated CT segmentation of diseased hip using hierarchical and conditional statistical shape models. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. Berlin: Springer; 2013. p. 190–7.Google Scholar
  25. 25.
    Huber PJ. Robust statistics. Berlin: Springer; 2011.CrossRefGoogle Scholar
  26. 26.
    Golland P, Grimson WEL, Shenton ME, Kikinis R. Detection and analysis of statistical differences in anatomical shape. Med Image Anal. 2005;9(1):69–86.CrossRefGoogle Scholar
  27. 27.
    Whitmarsh T, Humbert L, De Craene M, Barquero LMDR, Frangi AF. Reconstructing the 3D shape and bone mineral density distribution of the proximal femur from dual-energy X-ray absorptiometry. IEEE Trans Med Imaging. 2011;30(12):2101–14.CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Nara Institute of Science and TechnologyIkomaJapan

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