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Computer Aided Diagnosis: State-of-the-Art and Application to Musculoskeletal Diseases

  • Patrizia ParascandoloEmail author
  • Lorenzo Cesario
  • Loris Vosilla
  • Gianni Viano
Chapter

Abstract

Recently, computer-aided diagnosis (CAD) has become one of the major research subjects in medical imaging and diagnostic radiology. Many different types of CAD systems are being developed for detection and/or characterization of various lesions in medical imaging, including conventional projection radiography, computed tomography (CT), magnetic resonance imaging (MRI) and ultrasound (US).

The goal of a CAD is to improve the quality and productivity of physicians’ job by improving the accuracy and consistency of radiological diagnosis. CAD takes into account equally the roles of physicians and computers, whereas automated computer diagnosis is based on computer algorithms only. With CAD, the performance by computers does not have to be comparable to or better than that by physicians, but needs to be complementary to that by physicians. In fact, CAD systems are currently used to help in early diagnostic process and follow-up. This task is especially useful for musculoskeletal diseases, chronic pathologies which need early diagnosis, adequate follow-up, and timely monitoring of disease indicators.

This chapter describes the state of the art of CAD systems and discusses related issues and trends for musculoskeletal diseases, using as case study the Rheumatoid Arthritis (RA) and the software tool RheumaSCORE.

Keywords

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

Notes

Acknowledgments

This work is supported by the FP7 Marie Curie Initial Training Network “MultiScaleHuman”: Multi-scale Biological Modalities for Physiological Human Articulation (2011–2015), contract MRTN-CT-2011-289897. Softeco wishes to thank Esaote Spa and DIMI (Dipartimento di Medicina Interna, Clinica Reumatologica, Università degli Studi di Genova) for their collaboration. The RheumaSCORE software has been developed within the P.O.R. Liguria FESR (2007–2013)—Asse 1 “Innovazione e competitività”—Bando Azione 1.2.2—Progetto SIDARMA.

References

  1. 1.
    Doi, K., Giger, M. L., MacMahon, H., et al. (1992). Computer-aided diagnosis: Development of automated schemes for quantitative analysis of radiographic images. Sem Ultrasound CT MR, 13(2), 140–152.Google Scholar
  2. 2.
    Doi, K., Giger, M. L., Nishikawa, R. M., Hoffmann, K. R., MacMahon, H., Schmidt, R. A., et al. (1993). Digital radiography: A useful clinical tool for computer-aided diagnosis by quantitative analysis of radiographic images. Acta Radiologica, 34, 426–439.Google Scholar
  3. 3.
    Doi, K., MacMahon, H., Giger, M. L., & Hoffmann, K. R. (Eds.), (1999). Computer aided diagnosis in medical imaging. Amsterdam: Elsevier.Google Scholar
  4. 4.
    Doi, K., MacMahon, H., Katsuragawa, S., Nishikawa, R. M., & Jiang, Y. (1999). Computer-aided diagnosis in radiology: Potential and pitfalls. European Journal of Radiology, 31, 97–109.Google Scholar
  5. 5.
    Doi, K. (2000). Present status and future horizons for computer aided diagnosis in radiology. In: P. E. Sharp & A. C. Perkins (Eds.), Physics and engineering in medicine in the new millennium (pp. 84–87). London: Institute of Physics and Engineering in Medicine.Google Scholar
  6. 6.
    Doi, K. (2000). Computer-aided diagnosis in radiology: Basic concept, current status and future potential. In: N. -Z. Xie (Ed.), Medical imaging and precision radiotherapy (pp. 125–138). Guangzhou: Foundation of International Scientific Exchange.Google Scholar
  7. 7.
    Giger, M. L., Huo, Z., Kupinski, M. A., & Vyborny, C. J. (2000). Computer aided diagnosis in mammography. In: J. M. Fitzpatrick & M. Sonka (Eds.), The handbook of medical imaging, vol. 2, Medical imaging processing and analysis (pp. 915–1004). Bellingham: SPIE.Google Scholar
  8. 8.
    Li, Q., Li, F., Armato, S. G., 3rd, Suzuki, K., Shiraishi, J., Abe, H., et al. (2005). Computer-aided diagnosis in thoracic CT. Seminars in Ultrasound. CT MRI, 26, 357–363.Google Scholar
  9. 9.
    Yoshida, H., & Dachman, A. H. (2004). Computer-aided diagnosis for CT colonography. Seminars in Ultrasound. CT MRI, 25, 404–410.Google Scholar
  10. 10.
    Chan, H. P., Doi, K., Vyborny, C. J., Schmidt, R. A., Metz, C. E., Lam, K. L., et al. (1990). Improvement in radiologists’ detection of clustered microcalcifications on mammograms: The potential of computer-aided diagnosis. Investigative Radiology, 25, 1102–1110.Google Scholar
  11. 11.
    Moberg, K., Bjurstam, N., Wilczek, B., Rostgard, L., Egge, E., & Muren, C. (2001). Computer assisted detection of interval breast cancers. European Journal of Radiology, 39, 104–110.Google Scholar
  12. 12.
    Shiraishi, J., Abe, H., Engelmann, R., Aoyama, M., MacMahon, H., & Doi, K. (2003). Computer-aided diagnosis to distinguish benign from malignant solitary pulmonary nodules on radiographs: ROC analysis of radiologists’ performance-initial experience. Radiology, 227, 469–474.Google Scholar
  13. 13.
    Arimura, H., Katsuragawa, S., Suzuki, K., Li, F., Shiraishi, J., & Doi, K. (2004). Computerized scheme for automated detection of lung nodules in low-dose CT images for lung cancer screening. Academic Radiology, 11, 617–629.Google Scholar
  14. 14.
    Summers, R. M., et al. (2001). Automated polyp detection at CT colonography: Feasibility assessment in the human population. Radiology, 219, 51–59.Google Scholar
  15. 15.
    Wardlaw, J. M., & White, P. M. (2000). The detection and management of unruptured intracranial aneurysms. Brain, 123, 205–221.Google Scholar
  16. 16.
    White, P. M., Teasdale, E. M., Wardlaw, J. M., & Easton, V. (2001). Intracranial aneurysms: CT angiography and MR angiography for detection-prospective blinded comparison in a large patient cohort. Radiology, 219, 739–749.CrossRefGoogle Scholar
  17. 17.
    Kasai, S., Li, F., Shiraishi, J., Li, Q., Nie, Y., Doi, K. (2006). Development of computerized method for detection of vertebral fractures on lateral chest radiographs: Proceedings of SPIE 6144:61445D1-11. Bellingham: SPIE Press.Google Scholar
  18. 18.
  19. 19.
    Barbieri, F., Parascandolo, P., Vosilla, L., Cesario, L., Viano, G., Cimmino, M. A. (2012). Assessing MRI erosions in the rheumatoid wrist: A comparison between RAMRIS and a semiautomated segmentation software. Annals of the Rheumatic Diseases, 71(3), 296.Google Scholar
  20. 20.
    OSH in figures (2010). Work-related musculoskeletal disorders in the EU—Facts and figures. European Agency for Safety and Health at Work.Google Scholar
  21. 21.
    Managing musculoskeletal disorders. European foundation for the improvement of living and working conditions. www.eurofound.europa.eu
  22. 22.
    Green paper (2008). On the European workforce for health. Commission of the European Communities.Google Scholar
  23. 23.
    Giger, M. L. (2002). Computer-aided diagnosis in radiology. Academic Radiology, 9(1), 1–3.CrossRefGoogle Scholar
  24. 24.
    Lodwick, G. S., Haun, C. L., Smith, W. E., et al. (1963). Computer diagnosis of primary bone tumor. Radiology, 80, 273–275.Google Scholar
  25. 25.
    Myers, P. H., Nice, C. M., Becker, H. C., et al. (1964). Automated computer analysis of radiographic images. Radiology, 83, 1029–1033.Google Scholar
  26. 26.
    Winsberg, F., Elkin, M., May, J., et al. (1967). Detection of radiographic abnormalities in mammograms by means of optical scanning and computer analysis. Radiology, 89, 211–215.Google Scholar
  27. 27.
    Kruger, R. P., Towns, J. R., Hall, D. L., et al. (1972). Automated radiographic diagnosis via feature extraction and classification of cardiac size and shape descriptors. IEEE Transactions on Biomedical Engineering, 19, 174–186.CrossRefGoogle Scholar
  28. 28.
    Kruger, R. P., Thompson, W. B., Turner, A. F. (1974). Computer diagnosis of pneumoconiosis. IEEE Transactions on Systems, Man, and Cybernetics, 4(1), 40–49.Google Scholar
  29. 29.
    Toriwaki, J., Suenaga, Y., Negoro, T., et al. (1973). Pattern recognition of chest X-ray images. Computer Graphics and Image Processing, 2, 252–271.CrossRefGoogle Scholar
  30. 30.
    Ackerman, L. V., & Gose, E. E. (1972). Breast lesion classification by computer and xeroradiograph. Cancer, 30(4), 1025–1035.CrossRefGoogle Scholar
  31. 31.
    Engle, R. L. (1992). Attempt to use computers as diagnostic aids in medical decision making: A thirty-year experience. Perspectives in Biology and Medicine, 35, 207–219.Google Scholar
  32. 32.
    Nishikawa, R., Haldemann, M., Papaioannou, R. C., Giger, J., Lu, M. L., Schmidt, P. et al. (1995). Initial experience with a prototype clinical intelligent mammography workstation for computer-aided diagnosis: Proceedings of SPIE, (vol. 2434, pp. 65–71). Medical Imaging 1995: Image Processing.Google Scholar
  33. 33.
    Arimura, H., & Li, Q. (2004). Automated computerized scheme for detection of unruptured intracranial aneurysms in three-dimensional MRA. Academic Radiology, 11, 1093–1104.CrossRefGoogle Scholar
  34. 34.
    Arimura, H., Li, Q., Korogi, Y., Hirai, T., Katsuragawa, S., Yamashita, Y., et al. (2006). Computerized detection of intracranial aneurysms for 3D MR angiography: Feature extraction of small protrusions based on a shape-based difference image technique. Medical Physics, 33, 394–401.CrossRefGoogle Scholar
  35. 35.
    Kasai, S., Li, F., Shiraishi, J., Li, Q., Doi, K. (2006). Computerized detection of vertebral compression fractures on lateral chest radiographs: Preliminary results of a tool for early detection of osteoporosis. Medical Physics, 33, 4664–4676.Google Scholar
  36. 36.
    Chesnut, C. H., 3rd, Silverman, S., Andriano, K., Genant, H., Gimona, A., Harris, S., et al. (2000). A randomized trial of nasal spray salmon calcitonin in post-menopausal women with established osteoporosis. American Journal of Medicine, 109, 267–276.Google Scholar
  37. 37.
    Huang, D., & Wang, C. (2009). Optimal multi-level thresholding using a two-stage Otsu optimization approach. Pattern Recognition Letters, 30, 275–284.Google Scholar
  38. 38.
    Selle, D., Preim, B., Schenk, A., & Peitgen, H.-O. (2002). Analysis of vasculature for liver surgery planning. IEEE Transactions on Medical Imaging, 21(11), 1344–1357.Google Scholar
  39. 39.
    Kass, M., Witkin, A., & Terzopoulos, D. (1988). Snakes: Active contour models. International Journal of Computer Vision, 1(4), 321–331.Google Scholar
  40. 40.
    Osher, S., & Sethian, J. A. (1988). Fronts propagating with curvature dependent speed: Algorithms based on Hamilton-Jacobi formulation. Journal of Computational Physics, 79, 12–49.CrossRefzbMATHMathSciNetGoogle Scholar
  41. 41.
    Caselles, V., Kimmel, R., & Sapiro, G. (1995). Geodesic active contours. ICCV, 69, 4–699.Google Scholar
  42. 42.
    Chan, T., & Vese, L. (2001). Active contours without edges. IEEE TIP, 10(2), 266–277.Google Scholar
  43. 43.
    Ghuneim, A. G. (2005). Contour tracing, tutorial in image processing place. www.imageprocess-ingplace.com/DIP/dip
  44. 44.
    Chen, L. S., Herman, G. T., Reynolds, R. A., & Udupa, J. K. (1985). Surface shading in the cuberille environment. Computer Graphics and Applications, 5(12), 33–43.CrossRefzbMATHGoogle Scholar
  45. 45.
    Lorensen, W. E., & Cline H. E. (1987). Marching Cubes.A high resolution 3D surface construction algorithm: Proceedings of ACM SIGGRAPH (pp. 163–169).Google Scholar
  46. 46.
    Sobierajski, L., & Kaufman, A. (1994). Volumetric ray tracing. Proceedings of IEEE/ACM Symposium on Volume Visualization (pp. 11–18).Google Scholar
  47. 47.
    Schulze, J., Niemeier, R., & Lang, U. (2001). The perspective shear-warp algorithm in a virtual environment. Proceedings of IEEE Visualization (pp. 207–214).Google Scholar
  48. 48.
    Bartz, D., Fischer, J., del Rio, A. Hoffmann, J., & Freudenstein D. (2003a). VIRTUE: A navigated virtual endoscopy system for maxillo-facial and neurosurgery. Proceedings of 3D Modelling, Paris.Google Scholar
  49. 49.
    Preim, B., Tietjen, C., Spindler, W., Peitgen, H. O. (2002b). Integration of measurement tools in medical visualizations. Proceedings of IEEE Visualization (pp. 21–28).Google Scholar
  50. 50.
    Luft, A., Skalej, M., Welte, D., Kolb, R., Burk, K., Schulz, J., et al. (1998). A new semiautomated, three-dimensional technique allowing precise quantification of total and regional cerebellar volume using MRI. Magnetic Resonance in Medicine, 40(1), 143–151.CrossRefGoogle Scholar
  51. 51.
    Bartz, D., Orman, J., & Gurvit, O. (2004). Accurate volumetric measurements of anatomical cavities. Methods of Information in Medicine, 43(4), 331–335.Google Scholar
  52. 52.
    Hong, L., Muraki, S., Kaufman, A., Bartz, D., & He, T. (1997). Virtual voyage: Interactive navigation in the human colon. Proceedings of ACM SIGGRAPH (pp. 27–34).Google Scholar
  53. 53.
    Galyean, T. (1995). Guided navigation of virtual environments. Proceedings of ACM Symposium on Interactive 3D Graphics (pp. 103–104).Google Scholar
  54. 54.
    van Hulst, L. T., Fransen, J., den Broeder, A. A., Grol, R., van Riel, P. L., & Hulscher, M. E. (2009). Development of quality indicators for monitoring of the disease course in rheumatoid arthritis. Annals of the Rheumatic Diseases, 68, 1805–1810.CrossRefGoogle Scholar
  55. 55.
    Grigor, C., Capell, H., Stirling, A., McMahon, A. D., Lock, P., Vallance, R., et al. (2004). Effect of a treatment strategy of tight control for rheumatoid arthritis (the TICORA study): A single-blind randomised controlled trial. Lancet, 364, 263–269.CrossRefGoogle Scholar
  56. 56.
    Kuntz, R. D., & Minier, D. (2006). Building and using a medical ontology for knowledge management and cooperative work in a health care network. Computers in Biology and Medicine, 36, 871–892.CrossRefGoogle Scholar
  57. 57.
    Markenson, J. A. (1991). Worldwide trends in the socio economic impact and long-term prognosis of rheumatoid arthritis. Seminars in Arthritis and Rheumatism, 21, 4–12.CrossRefGoogle Scholar
  58. 58.
    Weinblatt, M. E. (1996). Rheumatoid arthritis: Treat now, not later (editorial). Annals of Internal Medicine, 124, 773–774.CrossRefGoogle Scholar
  59. 59.
    Østergaard, M., Hansen, M., Stoltenberg, M., Jensen, K. E., Szkudlarek, M., Pedersen-Zbinden, B., et al. (2003). 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 and Rheumatism, 48, 2128–2131.CrossRefGoogle Scholar
  60. 60.
    Benton, N., Stewart, N., Crabbe, J., Robinson, E., Yeoman, S., & McQueen, F. M. (2004). MRI of the wrist in early rheumatoid arthritis can be used to predict functional outcome at 6 years. Annals of the Rheumatic Diseases, 63, 555–561.CrossRefGoogle Scholar
  61. 61.
    McQueen, F. M., Benton, N., Perry, D., Crabbe, J., Robinson, E., Yeoman, S., et al. (2003). 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 and Rheumatism, 48, 1814–1827.CrossRefGoogle Scholar
  62. 62.
    Østergaard, M., Hansen, M., Stoltenberg, M., Jensen, K.E., Szkudlarek, M., Klarlund, M., et al. (2002). 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 and Rheumatism, 46, S526–S527.Google Scholar
  63. 63.
    Savnik, A., Malmskov, H., Thomsen, H. S., et al. (2002). MRI of the wrist and finger joints in inflammatory joint diseases at 1-year interval: MRI features to predict bone erosions. European Journal of Radiology, 12, 1203–1210.Google Scholar
  64. 64.
    Ejbjerg, B., McQueen, F., Lassere, M., Haavardsholm, E., Conaghan, P., O’Connor, P., et al. (2005). The EULAR-OMERACT rheumatoid arthritis MRI reference image atlas: The wrist joint. Annals of the Rheumatic Diseases, 64(1), 23–47.Google Scholar
  65. 65.
    Catalano, C. E., Robbiano, F., Parascandolo, P., Cesario, L., Vosilla, L., Barbieri, F., et al. (2012). Exploiting 3D part-based analysis, description and indexing to support medical applications. Proceedings of Workshop on Medical Content-Based Retrieval for Clinical Decision Support, in conjunction with MICCAI. France: Nice.Google Scholar

Copyright information

© Springer-Verlag London 2014

Authors and Affiliations

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

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