Segmentation of Multiple Knee Bones from CT for Orthopedic Knee Surgery Planning

  • Dijia Wu
  • Michal Sofka
  • Neil Birkbeck
  • S. Kevin Zhou
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8673)


Patient-specific orthopedic knee surgery planning requires precisely segmenting from 3D CT images multiple knee bones, namely femur, tibia, fibula, and patella, around the knee joint with severe pathologies. In this work, we propose a fully automated, highly precise, and computationally efficient segmentation approach for multiple bones. First, each bone is initially segmented using a model-based marginal space learning framework for pose estimation followed by non-rigid boundary deformation. To recover shape details, we then refine the bone segmentation using graph cut that incorporates the shape priors derived from the initial segmentation. Finally we remove overlap between neighboring bones using multi-layer graph partition. In experiments, we achieve simultaneous segmentation of femur, tibia, patella, and fibula with an overall accuracy of less than 1mm surface-to-surface error in less than 90s on hundreds of 3D CT scans with pathological knee joints.


Segmentation Result Initial Segmentation Statistical Shape Model Bone Segmentation Pairwise Term 
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.


  1. 1.
    Bourgeat, P., et al.: MR image segmentation of the knee bone using phase information. Med. Image Anal. 11, 325–335 (2007)CrossRefGoogle Scholar
  2. 2.
    Ababneh, S.Y., et al.: Automatic graph-cut based segmentation of bones from knee magnetic resonance images for osteoarthritis research. Med. Image Anal. 15 (2011)Google Scholar
  3. 3.
    Cootes, T.F., et al.: Active Shape Models - Their Training and Application. Comput. Vis. Image Und. 61(1), 38–59 (1995)CrossRefGoogle Scholar
  4. 4.
    Seim, H., et al.: Model-based auto-segmentation of knee bones and cartilage in MRI data. In: Medical Image Analysis for the Clinic: A Grand Challenge, Beijing (2010)Google Scholar
  5. 5.
    Schmid, J., Magnenat-Thalmann, N.: MRI Bone Segmentation Using Deformable Models and Shape Priors. In: Metaxas, D., Axel, L., Fichtinger, G., Székely, G. (eds.) MICCAI 2008, Part I. LNCS, vol. 5241, pp. 119–126. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  6. 6.
    Fripp, J., et al.: Automatic segmentation of the bone and extraction of the bone-cartilage interface from magnetic resonance images of the knee. Phys. Med. Biol. 52(6), 1617–1631 (2007)CrossRefGoogle Scholar
  7. 7.
    Boykov, Y.: Graph cuts and efficient N–D image segmentation. Int. J. Comput. Vision 70(2), 109–131 (2006)CrossRefGoogle Scholar
  8. 8.
    Liu, L., Raber, D., Nopachai, D., Commean, P., Sinacore, D., Prior, F., Pless, R., Ju, T.: Interactive separation of segmented bones in CT volumes using graph cut. In: Metaxas, D., Axel, L., Fichtinger, G., Székely, G. (eds.) MICCAI 2008, Part I. LNCS, vol. 5241, pp. 296–304. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  9. 9.
    Freedman, D., Zhang, T.: Interactive Graph Cut Based Segmentation With Shape Priors. In: Proc. CVPR (2005)Google Scholar
  10. 10.
    Shim, H., et al.: Knee cartilage: efficient and reproducible segmentation on high-spatial-resolution MR images with the semiautomated graph-cut algorithm method. Radiology 251(2), 548–556 (2009)CrossRefGoogle Scholar
  11. 11.
    Li, K., et al.: Optimal surface segmentation in volumetric images-a graph-theoretic approach. IEEE Trans. Pattern Anal. Mach. Intell. 28(1), 119–134 (2006)CrossRefGoogle Scholar
  12. 12.
    Yin, Y., et al.: LOGISMOS–layered optimal graph image segmentation of multiple objects and surfaces: Cartilage segmentation in the knee joint. IEEE Trans. Med. Imag. 29(12), 2023–2037 (2010)CrossRefGoogle Scholar
  13. 13.
    Kainmueller, D., et al.: Multi-Object Segmentation with Coupled Deformable Models. In: Annals of BMVA (2009)Google Scholar
  14. 14.
    Zheng, Y., et al.: Four-Chamber Heart Modeling and Automatic Segmentation for 3-D Cardiac CT Volumes Using Marginal Space Learning and Steerable Features. IEEE Trans. Med. Imag. 27(11), 1668–1681 (2008)CrossRefGoogle Scholar
  15. 15.
    Tu, Z.: Probabilistic Boosting-Tree: Learning Discriminative Models for Classification, Recognition, and Clustering. In: Proc. ICCV, vol. 2, pp. 1589–1596 (2005)Google Scholar
  16. 16.
    Zhang, J., et al.: Joint Real-Time Object Detection and Pose Estimation Using Probabilistic Boosting Network. In: Proc. CVPR (2007)Google Scholar
  17. 17.
    Felzenszwalb, P., Huttenlocher, D.: Distance Transforms of Sampled Functions Cornell Computing and Information Science (2004)Google Scholar
  18. 18.
    Delong, A., Boykov, Y.: Globally Optimal Segmentation of Multi-Region Objects. In: Proc. ICCV (2009)Google Scholar
  19. 19.
    Kolmogorov, V., Zabin, R.: What Energy Functions Can Be Minimized Via Graph Cuts? IEEE Trans. Pattern Anal. Mach. Intell. 26(2), 147–159 (2004)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Dijia Wu
    • 1
  • Michal Sofka
    • 1
  • Neil Birkbeck
    • 1
  • S. Kevin Zhou
    • 1
  1. 1.Imaging & Computer VisionSiemens Corporate TechnologyPrincetonUSA

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