Advertisement

Rheumatoid Arthritis Quantification using Appearance Models

  • G. Langs
  • P. Peloschek
  • H. Bischof
  • F. Kainberger

Abstract

Rheumatoid arthritis (RA) is a chronic disease that affects joints of the human skeleton. During therapy and during clinical trials, the accurate and precise measurement of the disease development is of crucial importance. Manual scoring frameworks exhibit high inter-reader variability and therefore constrain therapeutical monitoring or comparative evaluations during clinical trials.

In this chapter an automatic method for the quantification of rheumatoid arthritis is described. It is largely based on appearance models, and analyses a radiograph with regard to the two main indicators of RA progression: joint space width narrowing and erosions on the bones.

With the automatic approach a transition from global scoring methods that integrate over the entire anatomy, towards local measurements and the tracking of individual pathological changes becomes feasible. This is expected to improve both specificity and sensitivity of imaging biomarkers. It can improve therapy monitoring in particular if subtle changes occur, and can enhance the significance of clinical trials.

Keywords

Active Contour Appearance Model Joint Space Width Contour Point Training Population 
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.

References

  1. 1.
    A. Angwin, A. Lloyd, G. Heald, G. Nepom, M. Binks, and M. James. Radiographic hand joint space width assessed by computer is a sensitive measure of change in early rheumatoid arthritis. J Rheumatol, 31:1062–1072, 2004.Google Scholar
  2. 2.
    B. B. Bresnihan, R. Newmark, S. Robbins, and H. Genant. Effects of anakinra monotherapy on joint damage in patients with ra. extension of a 24-week randomized, placebo-controlled trial. Journal of Rheumatology, 2004.Google Scholar
  3. 3.
    J. M. Bathon, R. W. Martin, R. M. Fleischmann, J. R. Tesser, M. H. Schiff, E. C. Keystone, M. C. Genovese, M. C. Wasko, L. W. Moreland, A. L. Weaver, J. Markenson, and B. K. Finck. A comparison of etanercept and methotrexate in patients with early rheumatoid arthritis. N Engl J Med, 343(22):1586–1593, 2000.CrossRefGoogle Scholar
  4. 4.
    C. Bishop. Neural Networks for Pattern Recognition. Oxford Univ. Press, 1995.Google Scholar
  5. 5.
    J. Buckland-Wright, D. Macfarlane, S. Williams, and R. Ward. Accuracy and precision of joint space width measurements in standard and macroradiographs of osteoarthritic knees. Ann Rheum Dis, 54:872–880, 1995.CrossRefGoogle Scholar
  6. 6.
    T. Cootes, C. Taylor, D. Cooper, and J. Graham. Training models of shape from sets of examples. In Procceedings of BMVC’92, pages 266–275, 1992.Google Scholar
  7. 7.
    T. F. Cootes and C. J. Taylor. Combining elastic and statistical models of appearance variation. In ECCV (1), pages 149–163, 2000.Google Scholar
  8. 8.
    J. Duryea, Y. Jiang, M. Zakharevich, and H. Genant. Neural network based algorithm to quantify joint space width in joints of the hand for arthritis assessment. Med. Phys., 27(5):1185–1194, 2000.CrossRefGoogle Scholar
  9. 9.
    A. Finckh, H. Choi, and F. Wolfe. Progression of radiographic joint damage in different eras: trends towards milder disease in rheumatoid arthritis are attributable to improved treatment. Ann Rheum Dis, 65(6):1192–1197, 2006.CrossRefGoogle Scholar
  10. 10.
    A. Finckh, P. de Pablo, J. N. Katz, G. Neumann, Y. Lu, F. Wolfe, and J. Duryea. Performance of an automated computer-based scoring method to assess joint space narrowing in rheumatoid arthritis, a longitudinal study. Arthritis and Rheumatism, 54(5):1444–1450, 2006.Google Scholar
  11. 11.
    Y. Freund and R. Shapire. A decision-theoretic generalization of online learning and an application to boosting. Journal of Computer and System Sciences, 55:119–139, 1997.CrossRefzbMATHMathSciNetGoogle Scholar
  12. 12.
    J. A. Hanley and B. J. McNeil. The meaning and use of the area under a receiver operating characteristic (roc) curve. Radiology, 143(1):29–36, 1982.CrossRefGoogle Scholar
  13. 13.
    J. Higgs, D. Smith, K. D. Rosier, and R. C. Jr. Quantitative measurement of erosion growth and joint space loss in rheumatoid arthritis hand radiographs. J Rheumatol, 23:265–272, 1996.Google Scholar
  14. 14.
    M. James, G. Heald, J. Shorter, and R. Turner. Joint space measurement in hand radiographs using computerized image analysis. Arthritis Rheum, 38:891–901, 1995.CrossRefGoogle Scholar
  15. 15.
    E. Keystone, A. Kavanaugh, J. Sharp, H. Tannenbaum, Y. Hua, L. Teoh, S. Fischkoff, and E. Chartash. Radiographic, clinical, and functional outcomes of treatment with adalimumab (a human antitumor necrosis factor monoclonal antibody) in patients with active rheumatoid arthritis receiving concomitant methotrexate therapy: A randomized, placebo-controlled, 52-week trial. Arthritis and Rheumatism, 50(5):1400–1411, 2004.CrossRefGoogle Scholar
  16. 16.
    P. Křžek, J. Kittler, and V. Hlaváč. Feature selection based on the training set manipulation. In Proceedings of ICPR’06, volume 2, pages 658–661, 2006.Google Scholar
  17. 17.
    G. Langs. Autonomous Learning of Appearance Models in Medical Image Analysis. PhD thesis, Graz University of Technology, Institute for Computer Graphics and Vision, May 2007.Google Scholar
  18. 18.
    G. Langs, P. Peloschek, and H. Bischof. ASM driven snakes in rheumatoid arthritis assessment. In Proceedings of 13th Scandinavian Conference on Image Analysis, SCIA 2003, Goeteborg, Schweden, pages 454–461. Springer, 2003.Google Scholar
  19. 19.
    G. Langs, P. Peloschek, H. Bischof, and F. Kainberger. Automatic quantification of joint space narrowing and erosions in rheumatoid arthritis. Submitted to IEEE Transactions on Medical Imaging, Under Review.Google Scholar
  20. 20.
    G. Langs, P. Peloschek, H. Bischof, and F. Kainberger. Model-based erosion spotting and visualization in rheumatoid arthritis. Acad Radiol, 14(10):1179–1188, 2007.CrossRefGoogle Scholar
  21. 21.
    A. Larsen, K. Dale, and M. Eek. Radiographic evaluation of rheumatoid arthritis and related conditions by standard reference films. Acta Radiol Diagn, 18:481–491, 1977.Google Scholar
  22. 22.
    P. E. Lipsky, D. van der Heijde, E. W. S. Clair, D. E. Furst, F. C. Breedveld, J. R. Kalden, J. S. Smolen, M. Weisman, P. Emery, M. Feldmann, G. R. Harriman, and R. N. Maini. Infliximab and methotrexate in the treatment of rheumatoid arthritis. N Engl J Med, 343(22):1594–1602, 2000.CrossRefGoogle Scholar
  23. 23.
    C. Noelker and H. Ritter. GREFIT: Visual recognition of hand postures. In Gesture Workshop, pages 61–72, 1999.Google Scholar
  24. 24.
    P. Peloschek, G. Langs, M. Weber, J. Sailer, M. Reisegger, H. Imhof, H. Bischof, and F. Kainberger. An automatic model-based system for joint space measurements on hand radiographs: Initial experience. Radiology, 245(3):855–862, 2007.CrossRefGoogle Scholar
  25. 25.
    J. Sharp. Measurement of structural abnormalities in arthritis using radiographic images. Radiol Clin North Am, 42(1):109–119, 2004.CrossRefGoogle Scholar
  26. 26.
    J. Sharp, J. Gardner, and E. Bennett. Computer-based methods for measuring joint space and estimating erosion volume in the finger and wrist joints of patients with rheumatoid arthritis. Arthritis and Rheumatism, 43(6):1378–1386, 2000.CrossRefGoogle Scholar
  27. 27.
    J. Sharp, M. Lidsky, L. Collins, and J. Moreland. Methods of scoring the progression of radiologic changes in rheumatoid arthritis. Arthritis and Rheumatism, 14:706–720, 1971.CrossRefGoogle Scholar
  28. 28.
    J. Sharp, D. van der Heijde, J. Angwin, J. Duryea, H. Moens, J. Jacobs, J. Maillefert, and C. Strand. Measurement of joint space width and erosion size. J Rheumatol, 32(12):2456–2461, December 2005.Google Scholar
  29. 29.
    J. Sharp, F. Wolfe, M. Lassere, MaartenBoers, D. von der Heijde, A. Larsen, H. Paulus, R. Rau, and V. Strand. Variability of precision in scoring radiographic abnormalities in rheumatoid arthritis by experienced readers. Journal of Rheumatology, 31(6):1062–1072, 2004.Google Scholar
  30. 30.
    J. T. Sharp, J. Angwin, M. Boers, J. Duryea, G. von Ingersleben, J. R. Hall, J. A. Kauffman, R. Landewé, G. Langs, C. Lukas, J.-F. Maillefert, H. J. B. Moens, P. Peloschek, V. Strand, and D. van der Heijde. Computer based methods for measurement of joint space width: Update of an ongoing omeract project. Journal of Rheumatology, 34(4):874–83, 2007.Google Scholar
  31. 31.
    P. Shrout and J. Fleiss. Intraclass correlations: Uses in assessing rater reliability. Psychological Bulletin, 86(2):420–428, 1979.CrossRefGoogle Scholar
  32. 32.
    O. Sommer, A. Kladosek, V. Weiler, H. Czembirek, M. Boeck, and M. Stiskal. Rheumatoid arthritis: a practical guide to state-of-the-art imaging, image interpretation, and clinical implications. Radiographics, 25:381–398, 2005.Google Scholar
  33. 33.
    H. Swinkels, R. Laan, M. van ’t Hof, D. van der Heijde, N. de Vries, and P. van Riel. Modified sharp method: factors influencing reproducibility and variability. Semin Arthritis Rheum, 31(3):176–190, Dec 2001.CrossRefGoogle Scholar
  34. 34.
    D. van der Heijde. Radiographic imaging: the ’gold standard’ for assessment of disease progression in rheumatoid arthritis. Rheumatology, 39(Suppl 1):9–16, 2000.CrossRefGoogle Scholar
  35. 35.
    D. v.d.Heijde, A. Boonen, M. Boers, P. Kostense, and S. van der Linden. Reading radiographs in chronological order, in pairs or as single films has important implications ofor the discriminative power of rheumatoid erthritis clinical trials. Rheumatology, 38:1213–1220, 1999.Google Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • G. Langs
    • 1
  • P. Peloschek
    • 1
  • H. Bischof
    • 1
  • F. Kainberger
    • 1
  1. 1.Institute for Computer Graphics and VisionGraz University of TechnologyGrazAustria

Personalised recommendations