Advertisement

An Analysis by Synthesis Approach for Automatic Vertebral Shape Identification in Clinical QCT

  • Stefan ReinholdEmail author
  • Timo Damm
  • Lukas Huber
  • Reimer Andresen
  • Reinhard Barkmann
  • Claus-C. Glüer
  • Reinhard Koch
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11269)

Abstract

Quantitative computed tomography (QCT) is a widely used tool for osteoporosis diagnosis and monitoring. The assessment of cortical markers like cortical bone mineral density (BMD) and thickness is a demanding task, mainly because of the limited spatial resolution of QCT. We propose a direct model based method to automatically identify the surface through the center of the cortex of human vertebra. We develop a statistical bone model and analyze its probability distribution after the imaging process. Using an as-rigid-as-possible deformation we find the cortical surface that maximizes the likelihood of our model given the input volume. Using the European Spine Phantom (ESP) and a high resolution µCT scan of a cadaveric vertebra, we show that the proposed method is able to accurately identify the real center of cortex ex-vivo. To demonstrate the in-vivo applicability of our method we use manually obtained surfaces for comparison.

Keywords

Biomedical image analysis Quantitative computed tomography Cortex identification Bone densitometry Analysis by synthesis 

Notes

Acknowledgments

This work was part of the Diagnostik Bilanz Study which is part of the BioAsset project. BioAsset is funded by a grant of the Bundesministerium für Bildung und Forschung (BMBF), Germany, Föderkennzeichen 01EC1005. This work was also supported by the German Research Foundation, DFG, No. KO2044/9-1.

References

  1. 1.
    Andresen, R., Haidekker, M., Radmer, S., Banzer, D.: CT determination of bone mineral density and structural investigations on the axial skeleton for estimating the osteoporosis-related fracture risk by means of a risk score. Br. J. Radiol. 72(858), 569–578 (1999)CrossRefGoogle Scholar
  2. 2.
    Aslan, M.S., Ali, A., Chen, D., Arnold, B., Farag, A.A., Xiang, P.: 3D vertebrae segmentation using graph cuts with shape prior constraints. In: 2010 17th IEEE International Conference on Image Processing (ICIP), pp. 2193–2196. IEEE (2010)Google Scholar
  3. 3.
    Aslan, M.S., et al.: A novel 3D segmentation of vertebral bones from volumetric CT images using graph cuts. In: Bebis, G., et al. (eds.) ISVC 2009. LNCS, vol. 5876, pp. 519–528. Springer, Heidelberg (2009).  https://doi.org/10.1007/978-3-642-10520-3_49CrossRefGoogle Scholar
  4. 4.
    Chao, I., Pinkall, U., Sanan, P., Schröder, P.: A simple geometric model for elastic deformations. ACM Trans. Graph. (TOG) 29, 38 (2010)CrossRefGoogle Scholar
  5. 5.
    Cignoni, P., Rocchini, C., Scopigno, R.: Metro: measuring error on simplified surfaces. In: Computer Graphics Forum, vol. 17, pp. 167–174. Wiley Online Library (1998)Google Scholar
  6. 6.
    Consensus, A.: Consensus development conference: diagnosis, prophylaxis, and treatment of osteoporosis. Am. J. Med. 94(6), 646–650 (1993)CrossRefGoogle Scholar
  7. 7.
    Fuchs, J., Scheidt-Nave, C., Kuhnert, R.: 12-month prevalence of osteoporosis in Germany. Robert Koch-Institut, Epidemiologie und Gesundheitsberichterstattung (2017)Google Scholar
  8. 8.
    Genant, H.K., et al.: Effects of romosozumab compared with teriparatide on bone density and mass at the spine and hip in postmenopausal women with low bone mass. J. Bone Mineral Res. 32(1), 181–187 (2017)CrossRefGoogle Scholar
  9. 9.
    Giambini, H., Dragomir-Daescu, D., Huddleston, P.M., Camp, J.J., An, K.N., Nassr, A.: The effect of quantitative computed tomography acquisition protocols on bone mineral density estimation. J. Biomech. Eng. 137(11), 114502 (2015)CrossRefGoogle Scholar
  10. 10.
    Glüer, C.C., et al.: Comparative effects of teriparatide and risedronate in glucocorticoid-induced osteoporosis in men: 18-month results of the eurogiops trial. J. Bone Mineral Res. 28(6), 1355–1368 (2013)CrossRefGoogle Scholar
  11. 11.
    Guglielmi, G., Grimston, S.K., Fischer, K.C., Pacifici, R.: Osteoporosis: diagnosis with lateral and posteroanterior dual X-ray absorptiometry compared with quantitative CT. Radiology 192(3), 845–850 (1994)CrossRefGoogle Scholar
  12. 12.
    Haidekker, M., Andresen, R., Evertsz, C., Banzer, D., Peitgen, H.: Evaluation of the cortical structure in high resolution CT images of lumbar vertebrae by analysing low bone mineral density clusters and cortical profiles. Br. J. Radiol. 70(840), 1222–1228 (1997)CrossRefGoogle Scholar
  13. 13.
    Haidekker, M., Andresen, R., Werner, H.: Relationship between structural parameters, bone mineral density and fracture load in lumbar vertebrae, based on high-resolution computed tomography, quantitative computed tomography and compression tests. Osteoporos. Int. 9(5), 433–440 (1999)CrossRefGoogle Scholar
  14. 14.
    Jordt, A., Koch, R.: Fast tracking of deformable objects in depth and colour video. In: BMVC, pp. 1–11 (2011)Google Scholar
  15. 15.
    Kalender, W.: Technical foundations of spiral CT. J. belge de radiologie 78(2), 68–74 (1995)Google Scholar
  16. 16.
    Kalender, W.A., Felsenberg, D., Genant, H.K., Fischer, M., Dequeker, J., Reeve, J.: The European spine phantom–a tool for standardization and quality control in spinal bone mineral measurements by DXA and QCT. Eur. J. Radiol. 20(2), 83–92 (1995)CrossRefGoogle Scholar
  17. 17.
    Kang, Y., Engelke, K., Kalender, W.A.: A new accurate and precise 3-D segmentation method for skeletal structures in volumetric CT data. IEEE Trans. Med. Imaging 22(5), 586–598 (2003)CrossRefGoogle Scholar
  18. 18.
    Mastmeyer, A., Engelke, K., Fuchs, C., Kalender, W.A.: A hierarchical 3D segmentation method and the definition of vertebral body coordinate systems for QCT of the lumbar spine. Med. Image Anal. 10(4), 560–577 (2006)CrossRefGoogle Scholar
  19. 19.
    Mastmeyer, A., Engelke, K., Meller, S., Kalender, W.: A new 3D method to segment the lumbar vertebral bodies and to determine bone mineral density and geometry. In: Proceedings of Medical Image Understanding and Analysis. University of Bristol, UK (2005)Google Scholar
  20. 20.
    Meyer, M., Desbrun, M., Schröder, P., Barr, A.H.: Discrete differential-geometry operators for triangulated 2-manifolds. In: Hege, H.C., Polthier, K. (eds.) Visualization and Mathematics III. Mathematics and Visualization, pp. 35–57. Springer, Heidelberg (2003).  https://doi.org/10.1007/978-3-662-05105-4_2CrossRefGoogle Scholar
  21. 21.
    Ohkubo, M., et al.: Determination of point spread function in computed tomography accompanied with verification. Med. Phys. 36(6Part1), 2089–2097 (2009)CrossRefGoogle Scholar
  22. 22.
    Prevrhal, S., Engelke, K., Kalender, W.A.: Accuracy limits for the determination of cortical width and density: the influence of object size and CT imaging parameters. Phys. Med. Biol. 44(3), 751 (1999)CrossRefGoogle Scholar
  23. 23.
    Reinhold, S., Jordt, A., Koch, R.: Randomly sparsified synthesis for model-based deformation analysis. In: Rosenhahn, B., Andres, B. (eds.) GCPR 2016. LNCS, vol. 9796, pp. 143–154. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-45886-1_12CrossRefGoogle Scholar
  24. 24.
    Ritzel, H., Amling, M., Pösl, M., Hahn, M., Delling, G.: The thickness of human vertebral cortical bone and its changes in aging and osteoporosis: a histomorphometric analysis of the complete spinal column from thirty-seven autopsy specimens. J. Bone Mineral Res. 12(1), 89–95 (1997)CrossRefGoogle Scholar
  25. 25.
    Rockoff, S.D., Sweet, E., Bleustein, J.: The relative contribution of trabecular and cortical bone to the strength of human lumbar vertebrae. Calcif. Tissue Res. 3(1), 163–175 (1969)CrossRefGoogle Scholar
  26. 26.
    Siddiqi, K., Pizer, S.: Medial Representations: Mathematics, Algorithms and Applications, vol. 37. Springer, Heidelberg (2008)zbMATHGoogle Scholar
  27. 27.
    Silva, M., Wang, C., Keaveny, T., Hayes, W.: Direct and computed tomography thickness measurements of the human, lumbar vertebral shell and endplate. Bone 15(4), 409–414 (1994)CrossRefGoogle Scholar
  28. 28.
    Smith, M., Dunkow, P., Lang, D.: Treatment of osteoporosis: missed opportunities in the hospital fracture clinic. Ann. Roy. Coll. Surg. Engl. 86(5), 344 (2004)CrossRefGoogle Scholar
  29. 29.
    Sorkine, O., Alexa, M.: As-rigid-as-possible surface modeling. In: Symposium on Geometry Processing, vol. 4, pp. 109–116 (2007)Google Scholar
  30. 30.
    Treece, G.M., Gee, A.H., Mayhew, P., Poole, K.E.: High resolution cortical bone thickness measurement from clinical CT data. Med. Image Anal. 14(3), 276–290 (2010)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Computer ScienceKiel UniversityKielGermany
  2. 2.Section Biomedical Imaging, Molecular Imaging North Competence Center (MOIN CC), Department of Radiology and Neuroradiology, University Medical Center Schleswig-Holstein (UKSH)Kiel UniversityKielGermany
  3. 3.Institute of Diagnostic and Interventional Radiology/NeuroradiologyWestküstenklinikum Heide, Academic Teaching Hospital of the Universities of Kiel, Lübeck and HamburgHeideGermany

Personalised recommendations