RheumaSCORE: A CAD System for Rheumatoid Arthritis Diagnosis and Follow-Up

  • Patrizia Parascandolo
  • Lorenzo CesarioEmail author
  • Loris Vosilla
  • Gianni Viano
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9281)


Recently, computer-aided diagnosis (CAD) has become one of the major research subjects in medical imaging and diagnostic radiology. The goal of a CAD is to improve the quality and productivity of physicians’ job by improving the accuracy and consistency of radiological diagnosis. This paper describes RheumaSCORE, a CAD system specialized for the diagnosis and treatment of patients affected by bone erosions, as a consequence of one of the most common and serious forms of arthritis, the Rheumatoid Arthritis (RA), and gives an overview of its main features.


Computer aided diagnosis (CAD) Rheumatoid arthritis Medical imaging Erosion scoring Rheumascore 


  1. 1.
    Doi, K., Giger, M.L., MacMahon, H., et al.: Computer-aided diagnosis: development of automated schemes for quantitative analysis of radiographic images. Sem. Ultrasound CT MR 13, 140–152 (1992)Google Scholar
  2. 2.
    Doi, K., Giger, M.L., Nishikawa, R.M., Hoffmann, K.R., MacMahon, H., Schmidt, R.A., et al.: Digital radiography: a useful clinical tool for computer-aided diagnosis by quantitative analysis of radiographic images. Acta. Radiol. 34, 426–439 (1993)Google Scholar
  3. 3.
    Giger, M.L., Huo, Z., Kupinski, M.A., Vyborny, C.J.: Computer aided diagnosis in mammography. In: Fitzpatrick, J.M., Sonka, M. (eds.) The Handbook of Medical Imaging: Medical Imaging Processing and Analysis, vol. 2, pp. 915–1004. SPIE, Bellingham (2000)CrossRefGoogle Scholar
  4. 4.
    Li, Q., Li, F., Armato III, S.G., Suzuki, K., Shiraishi, J., Abe, H., et al.: Computer-aided diagnosis in thoracic CT. Seminars in Ultrasound. CT MRI 26, 357–363 (2005)CrossRefGoogle Scholar
  5. 5.
    Yoshida, H., Dachman, A.H.: Computer-aided diagnosis for CT colonography. Seminars in Ultrasound, CT MRI 25, 404–410 (2004)CrossRefGoogle Scholar
  6. 6.
    Moberg, K., Bjurstam, N., Wilczek, B., Rostgard, L., Egge, E., Muren, C.: Computer assisted detection of interval breast cancers. Eur. J. Radiol. 39, 104–110 (2001)CrossRefGoogle Scholar
  7. 7.
    Arimura, H., Katsuragawa, S., Suzuki, K., Li, F., Shiraishi, J., Doi, K.: Computerized scheme for automated detection of lung nodules in low-dose CT images for lung cancer screening. Acad. Radiol. 11, 617–629 (2004)CrossRefGoogle Scholar
  8. 8.
    Summers, R.M., et al.: Automated polyp detection at CT colonography: Feasibility assessment in the human population. Radiology 219, 51–59 (2001)CrossRefGoogle Scholar
  9. 9.
    White, P.M., Teasdale, E.M., Wardlaw, J.M., Easton, V.: Intracranial aneurysms: CT angiography andMRangiography for detection—prospective blinded comparison in a large patient cohort. Radiology 219, 739–749 (2001)CrossRefGoogle Scholar
  10. 10.
    Kubassova, O., Boesen, M., Cimmino, M.A., Bliddal, H.: A computer-aided detection system for rheumatoid arthritis MRI data interpretation and quantification of synovial activity. European Journal of Radiology 74(3), 67–72 (2010)CrossRefGoogle Scholar
  11. 11.
  12. 12.
    Barbieri, F., Parascandolo, P., Vosilla, L., Cesario, L., Viano, G., Cimmino, M.A.: Assessing MRI erosions in the rheumatoid wrist: a comparison between RAMRIS and a semiautomated segmentation software. Ann. Rheum. Dis. 71(Suppl 3) (2012)Google Scholar
  13. 13.
    Sebastian, T.B., Tek, H., Crisco, J.J., Kimia, B.B.: Segmentation of carpal bones from CT images using skeletally coupled deformable models. Medical Image Analysis 7(1), 21–45 (2003)CrossRefGoogle Scholar
  14. 14.
    Duryea, J., Magalnick, M., Alli, S., Yao, L., Wilson, M., Goldbach-Mansky, R.: Semiautomated three-dimensional segmentation software to quantify carpal bone volume changes on wrist CT scans for arthritis assessment. Medical Physics 36 (2008)Google Scholar
  15. 15.
    Lodwick, G.S., Haun, C.L., Smith, W.E., et al.: Computer diagnosis of primary bone tumor. Radiology 80, 273–275 (1963)CrossRefGoogle Scholar
  16. 16.
    Myers, P.H., Nice, C.M., Becker, H.C., et al.: Automated computer analysis of radiographic images. Radiology 83, 1029–1033 (1964)CrossRefGoogle Scholar
  17. 17.
    Winsberg, F., Elkin, M., May, J., et al.: Detection of radiographic abnormalities in mammograms by means of optical scanning and computer analysis. Radiology 89, 211–215 (1967)CrossRefGoogle Scholar
  18. 18.
    Kruger, R.P., Towns, J.R., Hall, D.L., et al.: Automated radiographic diagnosis via feature extraction and classification of cardiac size and shape descriptors. IEEE Trans. Biomed. Eng. 19, 174–186 (1972)CrossRefGoogle Scholar
  19. 19.
    Kruger, R.P., Thompson, W.B., Turner, A.F.: Computer diagnosis of pneumoconiosis. IEEE Transactions on Systems, Man, and Cybernetics 4 (1974)Google Scholar
  20. 20.
    Toriwaki, J., Suenaga, Y., Negoro, T., et al.: Pattern recognition of chest x-ray images. Computer Graphics and Image Processing 2, 252–271 (1973)CrossRefGoogle Scholar
  21. 21.
    Engle, R.L.: Attempt to use computers as diagnostic aids in medical decision making: a thirty-year experience. Perspect. Biol. Med. 35, 207–219 (1992)CrossRefGoogle Scholar
  22. 22.
    Peloschek, P.L., Langs, G., Bischof, H., Kainberger, F., Kropatsch, W., Imhof, H.: Computer aided diagnosis (CAD) in rheumatoid arthritis : automated joint localization, estimation of the bone contour and consecutive detection of defects of the bone contour in metacarpal bones. In: Proceedings of the Annual Meeting of the Radiological Society of North America RSNA (2003)Google Scholar
  23. 23.
    Lorensen, W.E., Cline, H.E.: Marching cubes: a high resolution 3D surface construction algorithm. In: Proc. of ACM SIGGRAPH, pp. 163–169 (1987)Google Scholar
  24. 24.
    Markenson, J.A.: Worldwide trends in the socioeconomic impact and long-term prognosis of rheumatoid arthritis. Semin. Arthritis Rheum. 21, 4–12 (1991)CrossRefGoogle Scholar
  25. 25.
    Weinblatt, M.E.: Rheumatoid arthritis: treat now, not later [editorial]. Ann. Intern. Med. 124, 773–774 (1996)CrossRefGoogle Scholar
  26. 26.
    Østergaard, M., Hansen, M., Stoltenberg, M., Jensen, K.E., Szkudlarek, M., Pedersen-Zbinden, B., Lorenzen, I.: New radiographic bone erosions in the wrists of patients with rheumatoid arthritis are detectable with magnetic resonance imaging a median of two years earlier. Arthritis Rheum. 48, 2128–2131 (2003)CrossRefGoogle Scholar
  27. 27.
    Benton, N., Stewart, N., Crabbe, J., Robinson, E., Yeoman, S., McQueen, F.M.: MRI of the wrist in early rheumatoid arthritis can be used to predict functional outcome at 6 years. Ann. Rheum. Dis. 63, 555–561 (2004)CrossRefGoogle Scholar
  28. 28.
    McQueen, F.M., Benton, N., Perry, D., Crabbe, J., Robinson, E., Yeoman, S., McLean, L., Stewart, N.: Bone edema scored on magnetic resonance imaging scans of the dominant carpus at presentation predicts radiologic joint damage of the hands and feet six years later in patients with rheumatoid arthritis. Arthritis Rheum. 48, 1814–1827 (2003)CrossRefGoogle Scholar
  29. 29.
    Østergaard, M., Hansen, M., Stoltenberg, M., Jensen, K.E., Szkudlarek, M., Klarlund, M., Pedersen-Zbinden, M.: MRI bone erosions in radiographically non-eroded rheumatoid arthritis wrist joint bones give a 4-fold increased risk of radiographic erosions five years later. Arthritis Rheum. 46(Suppl.), S526–S527 (2002)Google Scholar
  30. 30.
    Ejbjerg, B., McQueen, F., Lassere, M., Haavardsholm, E., Conaghan, P., O’Connor, P., Bird, P., Peterfy, C., Edmonds, J., Szkudlarek, M., Genant, H., Emery, P., Ostergaard, M.: The EULAR-OMERACT rheumatoid arthritis MRI reference image atlas: the wrist joint. Ann. Rheum. Dis. 64(Suppl 1), 23–47 (2005)CrossRefGoogle Scholar
  31. 31.
    Catalano, C.E., Robbiano, F., Parascandolo, P., Cesario, L., Vosilla, L., Barbieri, F., Spagnuolo, M., Viano, G., Cimmino, M.A.: Exploiting 3D part-based analysis, description and indexing to support medical applications. In: Greenspan, H., Müller, H., Syeda-Mahmood, T. (eds.) MCBR-CDS 2012. LNCS, vol. 7723, pp. 21–32. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  32. 32.
    Koch, M., Schwing, A.G., Comaniciu, D., Pollefeys, M.: Fully automatic segmentation of wrist bones for arthritis patients. In: Proceedings of the 8th IEEE International Symposium on Biomedical Imaging, from Nano to Macro, ISBI, Chicago, Illinois, USA, pp 636–640Google Scholar
  33. 33.
    Włodarczyk, J., Czaplicka, K., Tabor, Z., Wojciechowski, W., Urbanik, A.: Segmentation of bones in magnetic resonance images of the wrist. International Journal of Computer Assisted Radiology and Surgery 10(4), 419–431 (2015)CrossRefGoogle Scholar
  34. 34.
    Crowley, A.R., Dong, J., McHaffie, A., Clarke, A.W., Reeves, Q., Williams, M., et al.: Measuring bone erosion and oedema in rheumatoid arthritis: a comparison of manual segmentation and RAMRIS methods. J. Magn. Reson. Imaging 33, 364–371 (2011)CrossRefGoogle Scholar
  35. 35.
    Chand, A.S., McHaffie, A., Clarke, A.W., Reeves, Q., Tan, Y.M., Dalbeth, N., et al.: Quantifying synovitis in rheumatoid arthritis using computer-assisted manual segmentation with 3 Tesla MRI scanning. J. Magn. Reson. Imaging 33, 1106–1113 (2011)CrossRefGoogle Scholar
  36. 36.
    Parascandolo, P., Cesario, L.,Vosilla, L., Pitikakis, M., Viano, G.: Smart brush: a real time segmentation tool for 3D medical images. In: 8th International Symposium on Presented at Image and Signal Processing and Analysis (ISPA), pp. 689–694 (2013)Google Scholar
  37. 37.
    Caselles, V., Kimmel, R., Sapiro, G.: Geodesic active contours. In: International Conference on Computer Vision, pp. 694–699. IEEE Computer Society Press (1995)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Patrizia Parascandolo
    • 1
  • Lorenzo Cesario
    • 1
    Email author
  • Loris Vosilla
    • 1
  • Gianni Viano
    • 1
  1. 1.Softeco Sismat S.r.l.GenoaItaly

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