Intervertebral disc classification by its degree of degeneration from T2-weighted magnetic resonance images

Abstract

Purpose

The primary goal of this article is to achieve an automatic and objective method to compute the Pfirrmann’s degeneration grade of intervertebral discs (IVD) from MRI. This grading system is used in the diagnosis and management of patients with low back pain (LBP). In addition, biomechanical models, which are employed to assess the treatment on patients with LBP, require this grading value to compute proper material properties.

Materials and methods

T2-weighted MR images of 48 patients were employed in this work. The 240 lumbar IVDs were divided into a training set (140) and a testing set (100). Three experts manually classified the whole set of IVDs using the Pfirrmann’s grading system and the ground truth was selected as the most voted value among them. The developed method employs active contour models to delineate the boundaries of the IVD. Subsequently, the classification is achieved using a trained Neural Network (NN) with eight designed features that contain shape and intensity information of the IVDs.

Results

The classification method was evaluated using the testing set, resulting in a mean specificity (95.5 %) and sensitivity (87.3 %) comparable to those of every expert with respect to the ground truth.

Conclusions

Our results show that the automatic method and humans perform equally well in terms of the classification accuracy. However, human annotations have inherent inter- and intra-observer variabilities, which lead to inconsistent assessments. In contrast, the proposed automatic method is objective, being only dependent on the input MRI.

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References

  1. 1.

    Sharan AD, Tang Simon Y, Vaccaro AR (2013) Basic science of spinal diseases. JP Medical Ltd, New Delhi

    Book  Google Scholar 

  2. 2.

    Freemont A, Watkins A, Le Maitre C, Jeziorska M, Hoyland J (2002) Current understanding of cellular and molecular events in intervertebral disc degeneration: implications for therapy. J Pathol 196(4):374–379

    CAS  Article  PubMed  Google Scholar 

  3. 3.

    Malandrino A, Pozo JM, Castro-Mateos I, Frangi AF, van Rijsbergen MM, Ito K, Wilke HJ, Dao TT, Tho MCHB, Noailly J (2015) On the relative relevance of subjectspecific geometries and degeneration-specific mechanical properties for the study of cell death in human intervertebral disk models. Front Bioeng Biotechnol 3(5)

  4. 4.

    Luoma K, Riihimaki H, Luukkonen R, Raininko R, Viikari-Juntura E, Lamminen A (2000) Low back pain in relation to lumbar disc degeneration. Spine 25(4):487–492

    CAS  Article  PubMed  Google Scholar 

  5. 5.

    Livshits G, Popham M, Malkin I, Sambrook PN, Macgregor AJ, Spector T, Williams FMK (2011) Lumbar disc degeneration and genetic factors are the main risk factors for low back pain in women: the UK twin spine study. Ann Rheum Dis 70(10):1740–1745

    Article  PubMed  PubMed Central  Google Scholar 

  6. 6.

    Fujiwara A, Tamai K, Yamato M, An HS, Yoshida H, Saotome K, Kurihashi A (1999) The relationship between facet joint osteoarthritis and disc degeneration of the lumbar spine: an MRI study. Eur Spine J 8(5):396–401

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  7. 7.

    Thompson J, Pearce R, Schechter M, Adams M, Tsang I, Bishop P (1990) Preliminary evaluation of a scheme for grading the gross morphology of the human intervertebral disc. Spine 15(5):411–415

    CAS  Article  PubMed  Google Scholar 

  8. 8.

    Pfirrmann C, Metzdorf A, Zanetti M, Hodler J, Boos N (2001) Magnetic resonance classification of lumbar intervertebral disc degeneration. Spine 26:1873–1878

    CAS  Article  PubMed  Google Scholar 

  9. 9.

    Jarman JP, Arpinar VE, Baruah D, Klein AP, Maiman DJ, Muftuler LT (2014) Intervertebral disc height loss demonstrates the threshold of major pathological changes during degeneration. Eur Spine J 1–7

  10. 10.

    Riesenburger RI, Safain MG, Ogbuji R, Hayes J, Hwang SW (2015) A novel classification system of lumbar disc degeneration. J Clin Neurosci 22(2):346–351

    Article  PubMed  Google Scholar 

  11. 11.

    Chwialkowski M, Shile P, Peshock R, Pfeifer D, Parkey R (1989) Automated detection and evaluation of lumbar discs in MR images. Eng Med Biol Soc 571–572

  12. 12.

    Michopoulou S, Boniatis I, Costaridou L, Cavouras D, Panagiotopoulos E, Panayiotakis G (2009) Computer assisted characterization of cervical intervertebral disc degeneration in MRI. J Instrum 4(05):P05022

    Article  Google Scholar 

  13. 13.

    Ghosh S, Alomari R, Chaudhary V, Dhillon G (2011) Composite features for automatic diagnosis of intervertebral disc herniation from lumbar MRI. Eng Med Biol Soc EMBC 5068–5071

  14. 14.

    Una lY, Kocer H, Akkurt H (2011) A comparison of feature extraction techniques for diagnosis of lumbar intervertebral degenerative disc disease. In: International Symposium on Innovations in Intelligent Systems and Applications (INISTA), pp 490–494

  15. 15.

    Alomari RS, Corso JJ, Chaudhary V, Dhillon G (2010) Automatic diagnosis of lumbar disc herniation with shape and appearance features from MRI. SPIE Med Imaging 76241A

  16. 16.

    Neubert A, Fripp J, Engstrom C, Walker D, Weber M, Schwarz R, Crozier S (2013) Three-dimensional morphological and signal intensity features for detection of intervertebral disc degeneration from magnetic resonance images. J Am Med Inf Assoc 20(6):1082–1090

  17. 17.

    Lootus M, Kadir T, Zisserman (2015) A.: automated radiological grading of spinal MRI In: recent advances in computational methods and clinical applications for spine imaging, pp 119–130

  18. 18.

    Ruiz-España S, Arana E, Moratal D (2015) Semiautomatic computer-aided classification of degenerative lumbar spine disease in magnetic resonance imaging. Comput Biol Med 62:196–205

    Article  PubMed  Google Scholar 

  19. 19.

    Castro-Mateos I, Pozo JM, Lazary A, Frangi AF (2014) 2D segmentation of intervertebral discs and its degree of degeneration from T2-weighted magnetic resonance images. SPIE Med Imaging 903517

  20. 20.

    Cannon RL, Dave JV, Bezdek JC (1986) Efficient implementation of the fuzzy c-means clustering algorithms. Pattern Anal Mach Intell IEEE Trans 2:248–255

    Article  Google Scholar 

  21. 21.

    Castro-Mateos I, Pozo JM, Eltes PE, Del Rio L, Lazary A, Frangi AF (2014) 3D segmentation of annulus fibrosus and nucleus pulposus from T2-weighted magnetic resonance images. Phys Med Biol 59(24):7847–7864

    Article  PubMed  Google Scholar 

  22. 22.

    Newcombe RG (1998) Two-sided confidence intervals for the single proportion: comparison of seven methods. Stat Med 17(8):857–872

    CAS  Article  PubMed  Google Scholar 

  23. 23.

    Bera AK, Bilias Y (2001) Rao’s score, Neyman’s C(α) and Silvey’s LM tests: an essay on historical developments and some new results. J Stat Plan Inference 97(1):9–44

    Article  Google Scholar 

  24. 24.

    Malandrino A, Noailly J, Lacroix D, Beard DA (2011) The effect of sustained compression on oxygen metabolic transport in the intervertebral disc decreases with degenerative changes. PLOS Comput Biol 7(8)

  25. 25.

    Schmidt S, Bergtholdt M, Dries S, Schnorr C (2007) Spine detection and labeling using a parts-based graphical model. In: Lect Notes Comput Sc (IPMI), pp 122–133

  26. 26.

    Stern D, Vrtovec T, Pernus F, Likar B (2010) Automated determination of the centers of vertebral bodies and intervertebral discs in CT and MR lumbar spine images. In: Proceedings of SPIE, pp 762350

  27. 27.

    Peng Z, Zhong J, Wee W, Lee JH (2005) Automated vertebra detection and segmentation from the whole spine MR images. Eng Med Biol Soc Ann IEEE-EMBS, pp 2527–2530

  28. 28.

    Corso JJ, Alomari RS, Chaudhary V (2008) Lumbar disc localization and labeling with a probabilistic model on both pixel and object features. In: Lect Notes Comput Sc (MICCAI), pp 202–210

  29. 29.

    Ellingson AM, Mehta H, Polly DW, Ellermann J, Nuckley DJ (2013) Disc degeneration assessed by quantitative T2*(T2 star) correlated with functional lumbar mechanics. Spine 38(24):E1533–E1540

    Article  PubMed  Google Scholar 

  30. 30.

    Hon JY, Bahri S, Gardner V, Muftuler LT (2014) In vivo quantification of lumbar disc degeneration: assessment of ADC value using a degenerative scoring system based on Pfirrmann framework. Eur Spine J 24(11):1–7

    Google Scholar 

Download references

Acknowledgments

The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007-2013) under Grant agreement number 269909, MySpine project.

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Correspondence to Isaac Castro-Mateos.

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None of the authors has any potential conflict of interest.

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Castro-Mateos, I., Hua, R., Pozo, J.M. et al. Intervertebral disc classification by its degree of degeneration from T2-weighted magnetic resonance images. Eur Spine J 25, 2721–2727 (2016). https://doi.org/10.1007/s00586-016-4654-6

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Keywords

  • Classification
  • IVD
  • Degeneration
  • MR
  • 2D
  • Automatic
  • Active contour model
  • Neural network