Patient-Specific Modeling of Pelvic System from MRI for Numerical Simulation: Validation Using a Physical Model

Conference paper


Numerical simulation is useful to help understand the behavior of pelvic system, and eventually to assist the diagnostic and surgery. Patient-specific simulation is expected to optimize the treatment of patients. Despite the requirement of mechanical properties and loading, patient-specific simulation requires first 3D geometry adapted to patient. Manual 3D reconstruction of the patient-specific anatomy is time-consuming and introduces uncertainties. In this paper, we propose an efficient computer-assisted approach to modeling 3D geometries well suited to MRI data. A well-controlled physical model is also proposed, and manufactured, to estimate uncertainties of the presented method.


3D geometric modeling Physical model Pelvic system Magnetic resonance imaging 


  1. 1.
    Samuelsson EC, Victor FT, Tibblin G, Svärdsudd KF (1999) Signs of genital prolapse in a Swedish population of women 20 to 59 years of age and possible related factors. Am J Obstet Gynecol 180(2):299–305CrossRefGoogle Scholar
  2. 2.
    Swift SE (2000) The distribution of pelvic organ support in a population of female subjects seen for routine gynecologic health care. Am J Obstet Gynecol 183(2):277–285CrossRefGoogle Scholar
  3. 3.
    Bump RC, Mattiasson A, Bø K, Brubaker LP, DeLancey JO, Klarskov P, Shull BL, Smith AR (1996) The standardization of terminology of female pelvic organ prolapse and pelvic floor dysfunction. Am J Obstet Gynecol 175(1):10–17CrossRefGoogle Scholar
  4. 4.
    Dell’oro M, Collinet P, Robin G, Rubod C (2013) Multidisciplinary approach for deep endometriosis: interests and organization. Gynecol Obstet Fertil 41(1):58–64CrossRefGoogle Scholar
  5. 5.
    Mayeur O, Witz JF, Lecomte-Grosbras P, Brieu M, Cosson M, Miller K (2016) Influence of geometry and mechanical properties on the accuracy of patient-specific simulation of women pelvic floor. Ann Biomed Eng 44:202–212CrossRefGoogle Scholar
  6. 6.
    Lorensen WE, Cline HE (1987) Marching cubes: a high resolution 3D surface construction algorithm. In: Proceedings of the 14th annual conference on computer graphics and interactive techniques SIGGRAPH ‘87, pp 163–169Google Scholar
  7. 7.
    Shekhar R, Fayyad E, Yagel R, Cornhill JF (1996) Octree-based decimation of marching cubes surfaces. In: Proceedings visualization ‘96, pp 335–342Google Scholar
  8. 8.
    Paragios N, Chen Y, Faugeras O (2006) Handbook of mathematical models in computer vision. Springer, New YorkCrossRefGoogle Scholar
  9. 9.
    Kass M, Witkin A, Terzopoulos D (1988) Snakes: active contour models. Int J Comput Vis 1(4):321–331CrossRefGoogle Scholar
  10. 10.
    Chan TF, Vese LA (2001) Active contours without edges. IEEE Trans Image Process 10(2):266–277CrossRefGoogle Scholar
  11. 11.
    Li B, Acton ST (2007) Active contour external force using vector field convolution for image segmentation. IEEE Trans Image Process 16(8):2096–2106MathSciNetCrossRefGoogle Scholar
  12. 12.
    Semin B, Auradou H, François M (2011) Accurate measurement of curvilinear shapes by virtual image correlation. Eur Phys J Appl Phys 56(1):10701CrossRefGoogle Scholar
  13. 13.
    Réthoré J, François M (2014) Curve and boundaries measurement using B-splines and virtual images. Opt Lasers Eng 52:145–155CrossRefGoogle Scholar
  14. 14.
    Jiang Z, Witz JF, Lecomte-Grosbras P, Dequidt J, Duriez C, Cosson M, Cotin S, Brieu M (2015) B-spline based multi-organ detection in magnetic resonance imaging. Strain 51(3):235–247CrossRefGoogle Scholar
  15. 15.
    Jiang Z, Witz JF, Lecomte-Grosbras P, Dequidt J, Cotin S, Rubod C, Duriez C, Brieu M (2017) Multiorgan motion tracking in dynamic magnetic resonance imaging for evaluation of pelvic system mobility and shear strain. Strain 53(2):e12224CrossRefGoogle Scholar
  16. 16.
    Bay T, Chambelland JC, Raffin R, Daniel M, Bellemare ME (2011) Geometric modeling of pelvic organs. Conf Proc IEEE Eng Med Biol Soc 2011:4329–4332Google Scholar
  17. 17.
    Vallet A, Witz JF, Rubod C, Brieu M, Cosson M (2011) Simulation of pelvic mobility: topology optimization of ligamentous system. Comput Methods Biomech Biomed Eng 14(1):159–163Google Scholar
  18. 18.
    Mayeur O, Lamblin G, Lecomte-Grosbras P, Brieu M, Rubod C, Cosson M (2014) FE simulation for the understanding of the median cystocele prolapse occurrence. In: Bello F, Cotin S (eds) Biomedical simulation, vol 8789, pp 220–227CrossRefGoogle Scholar
  19. 19.
    Kikinis R, Pieper SD, Vosburgh K (2014) 3D slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Jolesz FA (ed) Intraoperative imaging image-guided therapy, vol 3(19), pp 277–289Google Scholar
  20. 20.
    Kamina P (2014) Anatomie clinique: Tome 4, Organes urinaires et génitaux, pelvis, coupes du tronc, 3rd edn. Maloine, ParisGoogle Scholar
  21. 21.
    Delancey JO (1992) Anatomic aspects of vaginal eversion after hysterectomy. Am J Obstet Gynecol 166(6):1717–1724CrossRefGoogle Scholar
  22. 22.
    Piegl L, Tiller W (1995) The NURBS book. Monographs in visual communication. Springer, BaselzbMATHGoogle Scholar
  23. 23.
    Buhmann MD (2003) Radial basis functions: theory and implementations. (Cambridge monographs on applied and computational mathematics). Cambridge University Press, CambridgeCrossRefGoogle Scholar
  24. 24.
    Wendland H (2005) Scattered data approximation. Cambridge University Press, CambridgezbMATHGoogle Scholar
  25. 25.
    Botsch M, Kobbelt L, Pauly M, Alliez P, Levy B (2010) Polygon mesh processing. CRC Press Taylor & Francis Group, Boca RatonCrossRefGoogle Scholar
  26. 26.
    Manzoni A, Quarteroni A, Rozza G (2012) Model reduction techniques for fast blood flow simulation in parametrized geometries. Int J Numer Methods Biomed Eng 28(6–7):604–625MathSciNetCrossRefGoogle Scholar
  27. 27.
    Mongillo M (2011) Choosing basis functions and shape parameters for radial basis function methods. SIAM Undergrad Res Online 4:2–6CrossRefGoogle Scholar
  28. 28.
    PyGeM: Python Geometrical Morphing. Available at:
  29. 29.
    Tezzele M, Salmoiraghi F, Mola A, Rozza G (2017) Dimension reduction in heterogeneous parametric spaces with application to naval engineering shape design problems. arXiv preprint arXiv:1709.03298Google Scholar
  30. 30.
    Jeanditgautier E, Mayeur O, Brieu M, Lamblin G, Rubod C, Cosson M (2016) Mobility and stress analysis of different surgical simulations during a sacral colpopexy, using a biomechanical model of the pelvic system. Int Urogynecol J 27(6):951–957CrossRefGoogle Scholar
  31. 31.
    Mayeur O, Jeanditgautier E, Witz JF, Lecomte-Grosbras P, Cosson M, Rubod C, Brieu M (2017) Evaluation of strains on levator ani muscle: damage induced during delivery for a prediction of patient risks. In: Computational biomechanics for medicine. Springer, Cham, pp 135–146CrossRefGoogle Scholar
  32. 32.
    Lepage J, Cosson M, Mayeur O, Brieu M, Rubod C (2016) Pedagogical childbirth simulators: utility in obstetrics. Eur J Obstet Gynecol Reprod Biol 197:41–47CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Univ. Lille, CNRS, Centrale Lille, FRE 2016 - LaMcube - Laboratoire de mécanique multiphysique multiéchelleLilleFrance
  2. 2.SATT NordLilleFrance
  3. 3.Neuroradiology Department, Univ. Lille, Inserm, CHU Lille, U1171 - Degenerative and Vascular Cognitive DisordersLilleFrance

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