Advertisement

3D Intervertebral Disc Localization and Segmentation from MR Images by Data-Driven Regression and Classification

  • Cheng Chen
  • D. Belavy
  • Guoyan Zheng
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8679)

Abstract

In this paper we propose a new fully-automatic method for localizing and segmenting 3D intervertebral discs from MR images, where the two problems are solved in a unified data-driven regression and classification framework. We estimate the output (image displacements for localization, or fg/bg labels for segmentation) of image points by exploiting both training data and geometric constraints simultaneously. The problem is formulated in a unified objective function which is then solved globally and efficiently. We validate our method on MR images of 25 patients. Taking manually labeled data as the ground truth, our method achieves a mean localization error of 1.3 mm, a mean Dice metric of 87%, and a mean surface distance of 1.3 mm. Our method can be applied to other localization and segmentation tasks.

Keywords

Intervertebral Disc Geometric Constraint Disc Center Training Point Sagittal Slice 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Schmidt, S., Kappes, J.H., Bergtholdt, M., Pekar, V., Dries, S.P.M., Bystrov, D., Schnörr, C.: Spine detection and labeling using a parts-based graphical model. In: Karssemeijer, N., Lelieveldt, B. (eds.) IPMI 2007. LNCS, vol. 4584, pp. 122–133. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  2. 2.
    Corso, J.J., Alomari, R.S., Chaudhary, V.: Lumbar disc localization and labeling with a probabilistic model on both pixel and object features. In: Metaxas, D., Axel, L., Fichtinger, G., Székely, G. (eds.) MICCAI 2008, Part I. LNCS, vol. 5241, pp. 202–210. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  3. 3.
    Glocker, B., Feulner, J., Criminisi, A., Haynor, D.R., Konukoglu, E.: Automatic localization and identification of vertebrae in arbitrary field-of-view CT scans. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012, Part III. LNCS, vol. 7512, pp. 590–598. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  4. 4.
    Glocker, B., Zikic, D., Konukoglu, E., Haynor, D.R., Criminisi, A.: Vertebrae localization in pathological spine CT via dense classification from sparse annotations. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013, Part II. LNCS, vol. 8150, pp. 262–270. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  5. 5.
    Chevrefils, C., Cheriet, F., Aubin, C.E., Grimard, G.: Texture analysis for automatic segmentation of intervertebral disks of scoliotic spines from mr images. IEEE Trans. on Information Technology in Biomedicine 13, 608–620 (2009)CrossRefGoogle Scholar
  6. 6.
    Michopoulou, S.K., Costaridou, L., Panagiotopoulos, E., Speller, R., Panayiotakis, G., Todd-Pokropek, A.: Atalas-based segmentation of degenerated lumbar intervertebral discs from mr images of the spine. IEEE Trans. on Biomedical Engineering 56(9), 2225–2231 (2009)CrossRefGoogle Scholar
  7. 7.
    Ben Ayed, I., Punithakumar, K., Garvin, G., Romano, W., Li, S.: Graph cuts with invariant object-interaction priors: Application to intervertebral disc segmentation. In: Székely, G., Hahn, H.K. (eds.) IPMI 2011. LNCS, vol. 6801, pp. 221–232. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  8. 8.
    Neubert, A., Fripp, J., Shen, K., Salvado, O., Schwarz, R., Lauer, L., Engstrom, C., Crozier, S.: Automatic 3D segmentation of vertebral bodies and intervertebral discs from mri. In: International Conference on Ditial Imaging Computing: Techniques and Applications (2011)Google Scholar
  9. 9.
    Law, M.W.K., Tay, K., Leung, A., Garvin, G.J., Li, S.: Intervertebral disc segmentation in mr images using anisotropic oriented flux. Medical Image Analysis 17, 43–61 (2013)CrossRefGoogle Scholar
  10. 10.
    Chen, C., Xie, W., Franke, J., Grutzner, P.A., Nolte, L.-P., Zheng, G.: Automatic x-ray landmark detection and shape segmentation via data-driven joint estimation of image displacements. Medical Image Analysis 18, 487–499 (2014)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Cheng Chen
    • 1
  • D. Belavy
    • 2
  • Guoyan Zheng
    • 1
  1. 1.Institute for Surgical Technology and BiomechanicsUniversity of BernSwitzerland
  2. 2.Department of RadiologyCharite University Medicine BerlinGermany

Personalised recommendations