Vertebrae Detection and Labelling in Lumbar MR Images

Conference paper
Part of the Lecture Notes in Computational Vision and Biomechanics book series (LNCVB, volume 17)

Abstract

We describe a method to automatically detect and label the vertebrae in human lumbar spine MRI scans. Our contribution is to show that marrying two strong algorithms (the DPM object detector of Felzenszwalb et al. [1], and inference using dynamic programming on chains) together with appropriate modelling, results in a simple, computationally cheap procedure, that achieves state-of-the-art performance. The training of the algorithm is principled, and heuristics are not required. The method is evaluated quantitatively on a dataset of 371 MRI scans, and it is shown that the method copes with pathologies such as scoliosis, joined vertebrae, deformed vertebrae and disks, and imaging artifacts. We also demonstrate that the same method is applicable (without retraining) to CT scans.

Keywords

Spine HOG MRI Detection Vertebrae SVM 

Notes

Acknowledgments

Acknowledgements for the dataset.

References

  1. 1.
    Felzenszwalb, P., Mcallester, D., Ramanan, D.: A discriminatively trained, multiscale, deformable part model. In: Proceedings of CVPR (2008)Google Scholar
  2. 2.
    Pfirmann, C.W.A., Metzdorf, A., Zanetti, M., Hodler, J., Boos, N.: Magnetic resonance classification of lumbar intervertebral disc degeneration. Spine 26(17), 1873–1878 (2001)CrossRefGoogle Scholar
  3. 3.
    Fardon, D.F., Milette, P.C.: Nomenclature and classification of lumbar disc pathology. Spine 26(5), E93–E113 (2001)CrossRefGoogle Scholar
  4. 4.
    Alomari, R.S., Corso, J.J., Chaudhary, V., Dhillon, G.: Desiccation diagnosis in lumbar discs from clinical mri with a probabilistic model. In: IEEE International Symposium on Biomedical Imaging: From Nano to Macro. ISBI ’09, pp. 546–549 (2009)Google Scholar
  5. 5.
    Alomari, R.S., Corso, J.J., Chaudhary, V., Dhillon, G.: Computer-aided diagnosis of lumbar disc pathology from clinical lower spine MRI. Int. J. Comput. Assist. Radiol. Surg. 5(3), 287–293 (2010)CrossRefGoogle Scholar
  6. 6.
    Ghosh, S., Alomari, R.S., Chaudhary, V., Dhillon, G.: Computer-aided diagnosis for lumbar mri using heterogeneous classifiers. In: IEEE International Symposium on Biomedical Imaging: From Nano to Macro (2011)Google Scholar
  7. 7.
    Michopoulou, S., Costaridou, L., Vlychou, M., Speller, R., Todd-Pokropek, A.: Texture-based quantification of lumbar intervertebral disc degeneration from conventional t2-weighted MRI. Acta Radiol. 52(1), 91–98 (2011)CrossRefGoogle Scholar
  8. 8.
    Ghosh, S., Alomari, R.S., Chaudhary, V., Dhillon, G.: Automatic lumbar vertebra segmentation from clinical ct for wedge compression fracture diagnosis. In: SPIE 7963, Medical Imaging 2011: Computer-Aided Diagnosis (2011)Google Scholar
  9. 9.
    Wels, M., Kelm, B.M., Tsymbal, A., Hammon, M., Soza, G., Sühling, M., Cavallaro, A., Comaniciu, D.: Multi-stage osteolytic spinal bone lesion detection from ct data with internal sensitivity control. In: Proceedings of SPIE 8315, Medical Imaging 2012: Computer-Aided Diagnosis (2012)Google Scholar
  10. 10.
    Dalal, N., Triggs, B.: Histogram of oriented gradients for human detection. In: Proceedings of CVPR, vol. 2, pp. 886–893 (2005)Google Scholar
  11. 11.
    Fischler, M., Elschlager, R.: The representation and matching of pictorial structures. IEEE Trans. Comput. c–22(1), 67–92 (1973)CrossRefGoogle Scholar
  12. 12.
    Felzenszwalb, P., Huttenlocher, D.: Pictorial structures for object recognition. IJCV 61(1), 55–79 (2005)CrossRefGoogle Scholar
  13. 13.
    Oktay, A.B., Akgul, Y.S.: Simultaneous localization of lumbar vertebrae and intervertebral discs with SVM based MRF. IEEE Trans. Med. Imaging 1179–1182 (2013)Google Scholar
  14. 14.
    Ghosh, S., Malgireddy, M.R., Chaudhary, V., Dhillon, G.: A new approach to automatic disc localization in clinical lumbar MRI: Combining machine learning with heuristics. In: International Symposium on Biomedical Imaging (2012)Google Scholar
  15. 15.
    Zhan, Y., Maneesh, D., Harder, M., Zhou, X.S.: Robust MR spine detection using hierarchical learning and local articulated model. Med. Image Comput. Comput.-Assist. Interv.—MICCAI—LNCS 7510, 141–148 (2012)Google Scholar
  16. 16.
    Chwialkowski, M.P., Shile, P.E., Pfeifer, D., Parkey, R.W., Peshock, R.M.: Automated localization and identification of lower spinal anatomy in magnetic resonance images. Comput. Biomed. Res. 24(2) (1989)Google Scholar
  17. 17.
    Aslan, M.S., Ali, A., Rara, H., Farag, A.A.: An automated vertebra identification and segmentation in CT images. In: Proceedings of IEEE 17th International Conference on Image Processing (2010)Google Scholar
  18. 18.
    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: Medical Image Computing and Computer-Assisted Intervention (2012)Google Scholar
  19. 19.
    Pekar, V., Bystrov, D., Heese, H.S., Dries, S.P.M., Schmidt, S., Grewer, R., Harder, C.J.D., Bergmans, R.C., Simonetti, A.W., Muiswinkel, A.M.V.: Automated planning of scan geometries in spine mri scans. In: Medical Image Computing and Computer-Assisted Intervention, vol. 10, pp. 601–608 (2007)Google Scholar
  20. 20.
    Alomari, R.S., Corso, J.J., Chaudhary, V.: Labeling of lumbar discs using both pixel- and object-level features with a two-level probabilistic model. IEEE Trans. Med. Imaging 30(1), 1–10 (2011)CrossRefGoogle Scholar
  21. 21.
    Kelm, B.M., Wels, M., Zhou, K.S., Seifert, S., Suehling, M., Zheng, Y., Comaniciu, D.: Spine detection in ct and mr using iterated marginal space learning. Med. Image Anal (2012)Google Scholar
  22. 22.
    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
  23. 23.
    Felzenszwalb, P.F., Grishick, R.B., McAllester, D., Ramanan, D.: Object detection with discriminatively trained part based models. IEEE PAMI 32(9), 1627–1645 (2010)CrossRefGoogle Scholar
  24. 24.
    Potesil, V., Lootus, M., El-Labban, A., Kadir, T.: Landmark localization in images with variable field of view. In: International Symposium on Biomedical Imaging (2013)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Engineering Science DepartmentOxford UniversityOxfordUK

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