Atlas-Based Registration for Accurate Segmentation of Thoracic and Lumbar Vertebrae in CT Data

  • Daniel ForsbergEmail author
Part of the Lecture Notes in Computational Vision and Biomechanics book series (LNCVB, volume 20)


Segmentation of the vertebrae in the spine is of relevance to many medical applications related to the spine. This paper describes a method based upon atlas-based registration for achieving an accurate segmentation of the thoracic and the lumbar vertebrae in the spine as imaged by computed tomography. The method has been evaluated on ten data sets provided as a part of the segmentation challenge hosted by the 2nd MICCAI workshop on Computational Methods and Clinical Applications for Spine Imaging. An average point-to-surface error of \(1.05\,\pm \,0.65\) mm and a mean DICE coefficient of \(0.94\,\pm \,0.03\) were obtained when comparing the computed segmentations with ground truth segmentations. These results are highly competitive when compared to the results of earlier presented methods.


  1. 1.
    Arsigny, V., Commowick, O., Ayache, N., Pennec, X.: A fast and Log-Euclidean polyaffine framework for locally linear registration. J. Math. Image. Vis. 33(2), 222–238 (2009)CrossRefMathSciNetGoogle Scholar
  2. 2.
    Cabezas, M., Oliver, A., Lladó, X., Freixenet, J., Bach Cuadra, M.: A review of Atlas-based segmentation for magnetic resonance brain images. Comput. Methods Progr. Biomed. 104(3), e158–e177 (2011)CrossRefGoogle Scholar
  3. 3.
    Fang, Q., Boas, D.A.: Tetrahedral mesh generation from volumetric binary and grayscale images. In: Biomedical Imaging: From Nano to Macro, 2009. ISBI’09. IEEE International Symposium on, pp. 1142–1145. IEEE (2009). doi: 10.1109/ISBI.2009.5193259
  4. 4.
    Farhi, E., Debab, Y., Willendrup, P.: iFit: A new data analysis framework, applications for data reduction and optimization of neutron scattering instrument simulations with mcstas. J. Neutron Res. 17(1), 5–18 (2014)Google Scholar
  5. 5.
    Forsberg, D., Eklund, A., Andersson, M., Knutsson, H.: Phase-based non-rigid 3D image registration—from minutes to seconds using CUDA. In: HP-MICCAI/MICCAI-DCI 2011 (2011)Google Scholar
  6. 6.
    Forsberg, D., Lundström, C., Andersson, M., Knutsson, H.: Model-based registration for assessment of spinal deformities in idiopathic scoliosis. Phys. Med. Biol. 59(2), 311–326 (2014)CrossRefGoogle Scholar
  7. 7.
    Forsberg, D., Lundström, C., Andersson, M., Vavruch, L., Tropp, H., Knutsson, H.: Fully automatic measurements of axial vertebral rotation for assessment of spinal deformity in idiopathic scoliosis. Phys. Med. Biol. 58(6), 1775–1787 (2013)CrossRefGoogle Scholar
  8. 8.
    Huang, J., Jian, F., Wu, H., Li, H.: An improved level set method for vertebra CT image segmentation. Biomed. Eng. Online 12(1), 48 (2013)CrossRefGoogle Scholar
  9. 9.
    Kim, Y., Kim, D.: A fully automatic vertebra segmentation method using 3D deformable fences. Comp. Med. Imag. Graph. 33(5), 343–352 (2009)CrossRefGoogle Scholar
  10. 10.
    Klinder, T., Ostermann, J., Ehm, M., Franz, A., Kneser, R., Lorenz, C.: Automated model-based vertebra detection, identification, and segmentation in CT images. Med. Image Anal. 13(3), 471–482 (2009)CrossRefGoogle Scholar
  11. 11.
    Knutsson, H., Andersson, M.: Morphons: Segmentation using elastic canvas and paint on priors. In: Image Processing (ICIP), 2005 IEEE International Conference on, pp. II-1226-9. IEEE (2005). doi: 10.1109/ICIP.2005.1530283
  12. 12.
    Lim, P.H., Bagci, U., Bai, L.: Introducing Willmore flow into level set segmentation of spinal vertebrae. Biomed. Eng. IEEE Trans. 60(1), 115–122 (2013)CrossRefGoogle Scholar
  13. 13.
    Ma, J., Lu, L., Zhan, Y., Zhou, X., Salganicoff, M., Krishnan, A.: Hierarchical segmentation and identification of thoracic vertebra using learning-based edge detection and coarse-to-fine deformable model. In: Medical Image Computing and Computer-Assisted Intervention MICCAI 2010, Lecture Notes in Computer Science, vol. 6361, pp. 19–27. Springer (2010)Google Scholar
  14. 14.
    Rasoulian, A., Rohling, R., Abolmaesumi, P.: Lumbar spine segmentation using a statistical multi-vertebrae anatomical shape+pose model. Med. Imag. IEEE Trans. 32(10), 1890–1900 (2013)CrossRefGoogle Scholar
  15. 15.
    Vrtovec, T.: Modality-independent determination of vertebral position and rotation in 3D. In: Medical Imaging and Augmented Reality, Lecture Notes in Computer Science, vol. 5128, pp. 89–97. Springer (2008)Google Scholar
  16. 16.
    Warfield, S.K., Zou, K.H., Wells, W.M.: Simultaneous truth and performance level estimation (STAPLE): an algorithm for the validation of image segmentation. Med. Imag. IEEE Trans. 23(7), 903–921 (2004)CrossRefGoogle Scholar
  17. 17.
    Yao, J., Burns, J., Munoz, H., Summers, R.: Detection of vertebral body fractures based on cortical shell unwrapping. In: Medical Image Computing and Computer-Assisted Intervention MICCAI 2012, Lecture Notes in Computer Science, vol. 7512, pp. 509–516. Springer (2012)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Center for Medical Image Science and Visualization (CMIV)Linköping UniversityLinköpingSweden
  2. 2.SectraLinköpingSweden

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