Automated Intervertebral Disc Detection from Low Resolution, Sparse MRI Images for the Planning of Scan Geometries

  • Xiao Dong
  • Huanxiang Lu
  • Yasuo Sakurai
  • Hitoshi Yamagata
  • Guoyan Zheng
  • Mauricio Reyes
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6357)

Abstract

Robust and accurate identification of intervertebral discs from low resolution, sparse MRI scans is essential for the automated scan planning of the MRI spine scan. This paper presents a graphical model based solution for the detection of both the positions and orientations of intervertebral discs from low resolution, sparse MRI scans. Compared with the existing graphical model based methods, the proposed method does not need a training process using training data and it also has the capability to automatically determine the number of vertebrae visible in the image. Experiments on 25 low resolution, sparse spine MRI data sets verified its performance.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Pekar, V., Bystrov, D., Heese, H.S., Dries, S., Schmidt, S., Grewer, R., den Harder, C.J., Bergmans, R.C., Simonetti, A.W., van Muiswinkel, A.: Automated planning of scan geometries in spine mri scans. In: Ayache, N., Ourselin, S., Maeder, A. (eds.) MICCAI 2007, Part I. LNCS, vol. 4791, pp. 601–608. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  2. 2.
    Weiss, K.L., Storrs, J.M., Banto, R.B.: Automated spine survey iterative scan technique. Radiology 239, 255–262 (2006)CrossRefGoogle Scholar
  3. 3.
    Peng, Z., Zhong, J., Wee, W., Lee, J.: Automated vertebra detection and segmentation from the whole spine mr images. In: IEEE EMBS 2005, vol. 3, pp. 122–133 (2005)Google Scholar
  4. 4.
    Klinder, T., Ostermann, J., Ehm, M., Franz, A., Kneser, R., Loternz, C.: Automated model-based vertebra detection, identification, and segmentation in ct images. Medical Image Analysis 13, 471–482 (2009)CrossRefGoogle Scholar
  5. 5.
    Štern, D., Likar, B., Pernuš, F., Vrtovec, T.: Automated detection of spinal centrelines, vertebral bodies and intervertebral discs in ct and mr images of lumbar spine. Physics in Medicine and Biology 55, 247–264 (2010)CrossRefGoogle Scholar
  6. 6.
    Schmidt, S., Kappes, J.H., Bergtholdt, M., Pekar, V., Dries, S., Bystrov, D., Schnörr, C.: Spine detection and labeling using a parts-based graphical model. In: MICCAI 2007, pp. 122–133 (2007)Google Scholar
  7. 7.
    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
  8. 8.
    Dong, X., Zheng, G.: Automated vertebra identification from X-ray images. In: Campilho, A., Kamel, M. (eds.) ICIAR 2010. LNCS, vol. 6112, pp. 1–9. Springer, Heidelberg (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Xiao Dong
    • 1
  • Huanxiang Lu
    • 1
  • Yasuo Sakurai
    • 2
  • Hitoshi Yamagata
    • 2
  • Guoyan Zheng
    • 1
  • Mauricio Reyes
    • 1
  1. 1.Institute for Surgical Technology and BiomechanicsUniversity of BernBernSwitzerland
  2. 2.Toshiba Medical Systems CorporationOtawaraJapan

Personalised recommendations