Advertisement

Lumbar Spine Disc Herniation Diagnosis with a Joint Shape Model

  • Raja S. Alomari
  • Jason J. Corso
  • Vipin Chaudhary
  • Gurmeet Dhillon
Conference paper
Part of the Lecture Notes in Computational Vision and Biomechanics book series (LNCVB, volume 17)

Abstract

Lower Back Pain (LBP) is the second most common neurological ailment in the United States after the headache. It costs over $100 billion annually in treatment and related rehabilitation costs including worker compensation. In fact, it is the most common reason for lost wages and missed work days. Degenerative Disc Disease (DDD) is the major abnormality that causes LBP. Moreover, Magnetic Resonance Imaging (MRI) test is the main clinically approved non-invasive imaging modality for the diagnosis of DDD. However, there is over 50 % inter- and intra-observer variability in the MRI interpretation that urges the need for standardized mechanisms in MRI interpretation. In this chapter, we propose a Computer Aided Diagnosis (CAD) System for Disc Degenerative Disease detection from clinical Magnetic Resonance Imaging (MRI). This CAD produces a reproducible and clinically accurate diagnosis of the DDD for lumbar spine. We design a classifier to automatically detect degenerated disc (also clinically known as Herniation) using shape potentials. We extract these shape potentials by jointly applying an active shape model (ASM) and a gradient vector flow snake model (GVF-snake). The ASM roughly segments the disc by the detection of a certain point distribution around the disc. Then, we use this point distribution to initialize a GVF-snake model to delineate the posterior disc segment. We then extract the set of shape potentials for our Gibbs-based classifier. The whole work flow is fully automated given the full clinical MRI. We validate our model on 65 clinical MRI cases (6 discs each) and achieve an average of 93.9 % classification accuracy. Our shape-based classifier is superior in classification accuracy compared to the state-of-the-art work on this problem that reports 86 and 91 % on 34 and 33 cases, respectively.

Keywords

Lumbar spine diagnosis MRI Disc degenerative disease 

Notes

Acknowledgments

This research was funded in part by NSF Grants DBI 0959870 and CNS 0855220 and NYSTAR grants 60701 and 41702.

References

  1. 1.
    Crow, W.T., Willis, D.R.: Estimating cost of care for patients with acute low back pain: a retrospective review of patient records. J. Am. Osteopath. Assoc. 109(4), 229–233 (2009)Google Scholar
  2. 2.
    Nelson, J., O’Neil, C., Richardson, C.J.: Treatment of low back pain: exploring the costs. Health and Wellness (2012)Google Scholar
  3. 3.
    NINDS: National institute of neurological disorders and stroke (ninds): Low back pain fact sheet. NIND brochure (2008)Google Scholar
  4. 4.
    van Rijn, J.C., Klemets, N., Reitsma, J.B., Majoie, C.B.L.M., Hulsmans, F.J., Peul, W.C., Stam, J., Bossuyt, P.M., den Heeten, G.J.: Observer variation in MRI evaluation of patients suspected of lumbar disk herniation. AJR Am. J. Roentgenol. 184(1), 299–303 (2005)Google Scholar
  5. 5.
    Atlas, S.J., Deyo, R.A.: Evaluating and managing acute low back pain in the primary care setting. J. Gen. Intern. Med. 16(2), 120–131 (2011)CrossRefGoogle Scholar
  6. 6.
    Swarm: Interactive incorporation (viewmedica)—patient educatuion system. (2007)Google Scholar
  7. 7.
    Fardon, D.F., Milette, P.C.: Nomenclature and classification of lumbar disc pathology. SPINE 26(5), E93–E113 (2001)CrossRefGoogle Scholar
  8. 8.
    Snell, R.S.: Clinical Anatomy by Regions. 8th edn. Lippincott, Williams & Wilkins, Philadelphia, Baltimore (2007)Google Scholar
  9. 9.
    Alomari, R.S., Corso, J.J., Chaudhary, V., Dhillon, G.: Toward a clinical lumbar CAD: herniation diagnosis. Int. J. Comput. Assist. Radiol. Surg. 6, 119–126 (2011)CrossRefGoogle Scholar
  10. 10.
    Alomari, R.S., Corso, J.J., Chaudhary, V., Dhillon, G.: Automatic diagnosis of lumbar disc herniation with shape and appearance features from MRI. In: Proceedings of SPIE Conference on Medical Imaging (SPIE) (2010)Google Scholar
  11. 11.
    Michopoulou, S., Costaridou, L., Panagiotopoulos, E., Speller, R., Panayiotakis, G., Todd-Pokropek, A.: Atlas-based segmentation of degenerated lumbar intervertebral discs from mr images of the spine. IEEE Trans. Biomed. Imaging 56(9), 2225–2231 (2009)CrossRefGoogle Scholar
  12. 12.
    Bounds, D., Lloyd, P., Mathew, B., Waddell, G.: A multilayer perceptron network for the diagnosis of low back pain. In: Proceedings of IEEE International Conference on Neural Networks, vol. 2, pp 481–489. San Diego, (1988)Google Scholar
  13. 13.
    Vaughn, M.: Using an artificial neural network to assist orthopaedic surgeons in the diagnosis of low back pain. http://www.marilyn-vaughn.co.uk/lbpainresearchstudy.htm (2000)
  14. 14.
    Tsai, M.D., Jou, S.B., Hsieh, M.S.: A new method for lumbar herniated inter-vertebral disc diagnosis based on image analysis of transverse sections. Comput. Med. Imaging Graph. 26(6), 369–380 (2002)CrossRefGoogle Scholar
  15. 15.
    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
  16. 16.
    Corso, J.J., Alomari, R.S., Chaudhary, V.: Lumbar disc localization and labeling with a probabilistic model on both pixel and object features. In: Proceedings of Medical Image Computing and Computer Aided Intervention (MICCAI). LNCS Part 1. vol 5241, pp. 202–210. Springer, Berlin (2008)Google Scholar
  17. 17.
    Cootes, T.F., Taylor, C.J.: Statistical models of appearance for medical image analysis and computer vision. In: Proceedings of SPIE Conference on Medical, Imaging (SPIE). pp. 236–248 (2001)Google Scholar
  18. 18.
    Xu, C., Prince, J.L.: Handbook of Medical Imaging. Academic, Baltimore (2000)Google Scholar
  19. 19.
    Liu, F., Zhao, B., Kijewski, P., Wang, L., Schwartz, L.: Liver segmentation for CT images using GVF snake. Med. Phys. 32(12), 3699–3706 (2005)CrossRefGoogle Scholar
  20. 20.
    Alomari, R.S., Corso, J.J., Chaudhary, V., Dhillon, G.: Desiccation diagnosis in lumbar discs from clinical MRI with a probabilistic model. In: Proceedings of IEEE International Symposium on Biomedical, Imaging (ISBI). pp. 546–549 (2009)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Raja S. Alomari
    • 1
    • 2
  • Jason J. Corso
    • 1
  • Vipin Chaudhary
    • 1
  • Gurmeet Dhillon
    • 3
  1. 1.State University of New York (SUNY) at BuffaloBuffaloUSA
  2. 2.The University of JordanAmmanJordan
  3. 3.Proscan Radiology BuffaloBuffaloUSA

Personalised recommendations