Advertisement

Automated CT Segmentation of Diseased Hip Using Hierarchical and Conditional Statistical Shape Models

  • Futoshi Yokota
  • Toshiyuki Okada
  • Masaki Takao
  • Nobuhiko Sugano
  • Yukio Tada
  • Noriyuki Tomiyama
  • Yoshinobu Sato
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8150)

Abstract

Segmentation of the femur and pelvis is a prerequisite for patient-specific planning and simulation for hip surgery. Accurate boundary determination of the femoral head and acetabulum is the primary challenge in diseased hip joints because of deformed shapes and extreme narrowness of the joint space. To overcome this difficulty, we investigated a multi-stage method in which the hierarchical hip statistical shape model (SSM) is initially utilized to complete segmentation of the pelvis and distal femur, and then the conditional femoral head SSM is used under the condition that the regions segmented during the previous stage are known. CT data from 100 diseased patients categorized on the basis of their disease type and severity, which included 200 hemi-hips, were used to validate the method, which delivered significantly increased segmentation accuracy for the femoral head.

Keywords

Femoral Head Distal Femur Canonical Correlation Analysis Segmentation Accuracy Coronal View 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Seim, H., Kainmueller, D., Heller, M., Lamecker, H., Zachow, S., Hege, H.C.: Automatic Segmentation of the Pelvic Bones from CT Data Based on a Statistical Shape Model. In: Eurographics Workshop on Visual Computing for Biomedicine, pp. 93–100 (2008)Google Scholar
  2. 2.
    Kainmueller, D., Lamecker, H., Zachow, S., Hege, H.C.: An Articulated Statistical Shape Model for Accurate Hip Joint Segmentation. In: Conf. Proc. IEEE Eng. Med. Biol. Soc., pp. 6345–6351 (2009)Google Scholar
  3. 3.
    Schmid, J., Kim, J., Magnenat-Thalmann, N.: Robust statistical shape models for MRI bone segmentation in presence of small field of view. Med. Image Anal. 15(1), 155–168 (2011)CrossRefGoogle Scholar
  4. 4.
    Kainmueller, D., Lamecker, H., Zachow, S., Hege, H.-C.: Coupling Deformable Models for Multi-object Segmentation. In: Bello, F., Edwards, E. (eds.) ISBMS 2008. LNCS, vol. 5104, pp. 69–78. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  5. 5.
    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. In: Yang, G.-Z., Hawkes, D., Rueckert, D., Noble, A., Taylor, C. (eds.) MICCAI 2009, Part II. LNCS, vol. 5762, pp. 811–818. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  6. 6.
    Yang, Y.M., Rueckert, D., Bull, A.: Predicting the shapes of bones at a joint: application to the shoulder. Comput. Methods Biomech. Biomed. Engin. 11(1), 19–30 (2008)CrossRefGoogle Scholar
  7. 7.
    de Bruijne, M., Lund, M.T., Tanko, L.B., Pettersen, P.C., Nielsen, M.: Quantitative vertebral morphometry using neighbor-conditional shape models. Med. Image Anal. 11(5), 503–512 (2007)CrossRefGoogle Scholar
  8. 8.
    Zoroofi, R.A., Sato, Y., Sasama, T., Nishii, T., Sugano, N., Yonenobu, K., Yoshikawa, H., Ochi, T., Tamura, S.: Automated Segmentation of Acetabulum and Femoral Head From 3-D CT Images. IEEE Trans. Inf. Technol. Biomed. 7(4), 329–343 (2003)CrossRefGoogle Scholar
  9. 9.
    Rueckert, D., Sonoda, L.I., Hayes, C., Hill, D.L.G., Leach, M.O., Hawkes, D.J.: Non-rigid registration using freeform deformations: Application to breast MR images. IEEE Trans. Med. Image 18(8), 712–721 (1999)CrossRefGoogle Scholar
  10. 10.
    Yokota, F., Okada, T., Takao, M., Sugano, N., Tada, Y., Tomiyama, N., Sato, Y.: Automated Localization of Pelvic Anatomical Coordinate System from 3D CT Data of the Hip Using Statistical Atlas. Med. Imag. Tech. 30(1), 43–52 (2012) (in Japanese)Google Scholar
  11. 11.
    Fillard, P., Pennec, X., Thompson, P.M., Ayache, N.: Evaluating Brain Anatomical Correlations via Canonical Correlation Analysis of Sulcal Lines. In: MICCAI 2007 Workshop: Statistical Registration, HAL-CCSD (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Futoshi Yokota
    • 1
  • Toshiyuki Okada
    • 2
  • Masaki Takao
    • 2
  • Nobuhiko Sugano
    • 2
  • Yukio Tada
    • 3
  • Noriyuki Tomiyama
    • 2
  • Yoshinobu Sato
    • 2
  1. 1.Graduate School of EngineeringKobe UniversityJapan
  2. 2.Graduate School of MedicineOsaka UniversityJapan
  3. 3.Graduate School of System InformaticsKobe UniversityJapan

Personalised recommendations