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A Modular Neural Network System for the Analysis of Nuclei in Histopathological Sections

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Computational Intelligence Processing in Medical Diagnosis

Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 96))

Abstract

The evaluation of immunocytochemically stained histopathological sections presents a complex problem due to many variations that are inherent in the methodology. This chapter describes a modular neural network system which is being used for the detection and classification of breast cancer nuclei named Biopsy Analysis Support System (BASS). The system is based on a modular architecture where the detection and classification stages are independent. Two different methods for the detection of nuclei are being used: the one approach is based on a feed forward neural network (FNN) which uses a block-based singular value decomposition (SVD) of the image, to signal the likelihood of occurrence of nuclei.

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References

  1. Alcatel TITN Answare (1993), IMMUNO 4.00: User’s Guide, 1st ed., Grenoble, France.

    Google Scholar 

  2. Bacus, S. and Flowers, J.L. (1988), “The evaluation of estrogen receptor in primary breast carcinoma by computer-assisted image analysis,” Am. J. of Clinical Pathology, vol. 90, pp. 233–239.

    CAS  Google Scholar 

  3. Bartels, P.H. (1992), “Computer generated diagnosis and image analysis, an overview,” Cancer, vol. 69, pp. 1636–1638.

    Article  PubMed  CAS  Google Scholar 

  4. Becker, R.L. and Usaf, M.C. (1995), “Applications of neural networks in histopathology,” Pathologica, vol. 87, no. 3, pp. 246254.

    Google Scholar 

  5. Bibbo, M., Bartels, P.H., Pfeifer, T., Thompson, D., Minimo, C., and Galera Davidson, H. (1993), “Belief network for grading prostate lesions,” Anal. Quant. Cytol. Histol., vol. 15, pp. 124–135.

    PubMed  CAS  Google Scholar 

  6. Biesterfeld, S., Kluppel, D., Koch, R., Schneider, S., Steinhagen, G., Mihalcea, A.M., and Schroder, W. (1998), “Rapid and prognostically valid quantification of immunohistochemical reactions by immunohistometry of the most positive tumour focus,” Journal of Pathology, vol. 185, no. 1, pp. 25–31.

    Article  PubMed  CAS  Google Scholar 

  7. Birdsong, G.G. (1996), “Automated screening of cervical cytology specimens,” Human Pathology, vol. 27, pp. 468–481.

    Article  PubMed  CAS  Google Scholar 

  8. Brugal, G. (1985), “Color processing in automated image analysis for cytology,” in Mary, J.Y. and Rigaut, J.P. (Eds.), Quant. Image Analysis in Cancer Cytology and Histology, Amsterdam: Elsevier, pp. 19–33.

    Google Scholar 

  9. Burke, H.B. (1994), “Artificial neural networks for cancer research. Outcome prediction,” Sem. Surgical Oncology, vol. 10, pp. 73–79.

    Article  CAS  Google Scholar 

  10. Carter, C.L., Allen, C., and Henson, D.E. (1989), “Relation of tumour size, lymph mode status and survival in 24,740 breast cancer cases,” Cancer, vol. 63, pp. 181–187.

    Article  PubMed  CAS  Google Scholar 

  11. Cell Analysis Systems Inc. (1990), Cell Analysis Systems: Quantitative Estrogen Progesterone Users Manual,Application Version 2.0, Catalog Number 201325–00, USA.

    Google Scholar 

  12. Charpin, C., Martin, P.M., DeVictor, B., Lavaut, M.N., Habib, M.C., Andrac, L., and Toga, M. (1988), “Multiparametric study (SAMBA 200) of estrogen receptor immunocytochemical assay in 400 human breast carcinomas,” Cancer Research, vol. 48, pp. 1578–1586.

    PubMed  CAS  Google Scholar 

  13. Chen, S., Cowan, C.F.N., and Grant, P.M. (1991), “Orthogonal least squares learning algorithm for radial basis function networks,” IEEE Trans. Neural Networks, vol. 2, no. 2, pp. 302309.

    Google Scholar 

  14. Cohen, C. (1996), “Image cytometric analysis in pathology,” Human Pathology, vol. 27, no. 5, pp. 482–493.

    Article  PubMed  CAS  Google Scholar 

  15. Dawson, A.E., Austin Jr., R.E., and Weinberg, D.S. (1991), “Nuclear grading of breast carcinoma by image analysis,” American Journal of Clinical Pathology, vol. 95 (Suppl. 1), pp. S29 - S37.

    PubMed  CAS  Google Scholar 

  16. De Laurentiis, M., De Placido, S., Bianco, AR., Clark, G.M., and Ravdin, P.M. (1999), “A prognostic model that makes quantitative estimates of probability of relapse for breast cancer patients,” Clinical Cancer Research, vol. 5, no. 12, pp. 4133–4139.

    PubMed  Google Scholar 

  17. Deligdisch, L., Einstein, A.J., Guera, D., and Gil, J. (1995), “Ovarian dysplasia in epithelial inclusion cysts. A morphometric approach using neural networks,” Cancer, vol. 76, no. 6, pp. 10271034.

    Google Scholar 

  18. Demuth, H. and Beale, M. (1994), Neural Network Toolbox, The MathWorks, Inc., Natick, Mass., USA.

    Google Scholar 

  19. Furness, P.N., Levesley, J., Luo, Z., Taub, N., Kazi, J.I., Bates, W.D., and Nicholson, M.L. (1999), “A neural network approach to the biospy diagnosis of early acute renal transplant rejection,” Histopathology, vol. 35, pp. 461–467.

    Article  PubMed  CAS  Google Scholar 

  20. Garfinkel, L., Boring, C.C., and Heath, C.W. Jr. (1994), “Changing trends. An overview of breast cancer incidence and mortality,” Cancer, vol. 74, pp. 222–227.

    Article  PubMed  CAS  Google Scholar 

  21. Goldschmidt, D., Decaestecker, C., Berthe, J.V., Gordower, L., Remmelink, M., Danguy, A., Pasteels, J.L., Salmon, I., and Kiss, R. (1996), “The contribution of image cytometry and artificial intelligence-related methods of numerical data analysis for adipose tumor histopathologic classification,” Laboratory Investigation, vol. 75, no. 3, pp. 295–306.

    PubMed  CAS  Google Scholar 

  22. Haykin, S. (1994), Neural Networks: a Comprehensive Foundation, New York, USA: Macmillan, 1994.

    Google Scholar 

  23. Hong, Z.-Q. (1991), “Algebraic feature extraction of image for recognition,” Pattern Recognition, vol. 24, no. 3, pp. 211–219.

    Article  Google Scholar 

  24. Hubel, D.H. and Wiesel, T.N. (1962), “Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex,” J. Physiol., Lond., vol. 160, pp. 106–154.

    CAS  Google Scholar 

  25. Jagoe, R., Steele, J.H., Vucicevic, V., Alexander, N., van Noorden, S., Wooton, R., and Polak, J.M. (1991), “Observer variation in quantification of immunocytochemistry by image analysis,” Histochemical Journal, vol. 23, pp. 541–547.

    Article  PubMed  CAS  Google Scholar 

  26. Jain, A.K. (1989), Fundamentals of Digital Image Processing, Englewood Cliffs, New Jersey, USA: Prentice Hall, 1989.

    Google Scholar 

  27. Kelsey, J.L. and Horn-Ross, P.L. (1993), “Breast cancer: magnitude of the problem and descriptive epidemiology,” Epidemiological Reviews, vol. 15, no. 1, pp. 7–16.

    CAS  Google Scholar 

  28. Kok, M.R. and Boon, M.E. (1996), “Consequences of neural network technology for cervical screening,” Cancer, vol. 78, pp. 112–117.

    Article  PubMed  CAS  Google Scholar 

  29. Koss, L.G. (2000), “The Application of PAPNET to Diagnostic Cytology,” in Lisboa, P.J.G., Ifeachor, C., and Szczepaniak, P.S. (Eds.), Artificial Neural Networks in Biomedicine, Springer-Verlag, London, pp. 51–67.

    Chapter  Google Scholar 

  30. Lundin, M., Lundin, J., Burke, H.B., Toikkanen, S., Pylkkanen, L., and Joensuu, H. (1999), “Artificial neural networks applied to survival prediction in breast cancer,” Oncology, vol. 57, pp. 281286.

    Google Scholar 

  31. Mangasarian, O.L., Street, W.N., and Wolberg, W.H. (1995), “Breast cancer diagnosis and prognosis via linear programming,” Operations Research, vol. 43, no. 4, pp. 570–577.

    Article  Google Scholar 

  32. Man, D. and Hildreth, E. (1980), “Theory of edge detection,” Proc. R. Soc. Lond., vol. B 207, pp. 187–217.

    Google Scholar 

  33. McCarty Jr., K.S., Miller, L.S., Cox, E.B., Konrath, J., and McCarty Sr., K.S. (1985), “Estrogen receptor analyses. Correlation of biochemical and immunohistochemical methods using monoclonal antireceptor antibodies,” Arch. Pathol. Lab. Med., vol. 109, pp. 716–721.

    PubMed  Google Scholar 

  34. Millot, C. and Dufer, J. (2000), “Clinical applications of image cytometry to human tumour analysis,” Histology Histopathology, vol. 15, no. 4, pp. 1185–200.

    PubMed  CAS  Google Scholar 

  35. Naguib, R.N., Sakim, H.A., Lakshmi, M.S., Wadehra, V., Lennard, T.W., Bhatavdekar, J., and Sherbet, G.V. (1999), “DNA ploidy and cell cycle distribution of breast cancer aspirate cells measured by image cytometry and analyzed by artificial neural networks for their prognostic significance,” IEEE Trans Information Technology Biomedicine, vol. 3, no. 1, pp. 61–69.

    Article  CAS  Google Scholar 

  36. Newcomb, P.A. and Lantz, P.M. (1993), “Recent trends in breast cancer incidence, mortality, and mammography,” Breast Cancer Research and Treatment, vol. 28, pp. 97–106.

    Article  PubMed  CAS  Google Scholar 

  37. O’Brien, M.J. and Sotnikov, A.V. (1996), “Digital imaging in anatomic pathology,” American Journal of Clinical Pathology, vol. 106, no. 4, suppl. 1, pp. S25 - S32.

    Google Scholar 

  38. Pantazopoulos, D., Karakitsos, P., Iokim-Liossi, A., Pouliakis, A., Botsoli-Stergiou, E., and Dimopoulos, C. (1998), “Back propagation neural network in the discrimination of benign from malignant lower urinary tract lesions,” Journal of Urology, vol. 159, no. 5, pp. 1619–1623.

    Article  PubMed  CAS  Google Scholar 

  39. Pisani, P., Parkin, D.M., Bray, F., and Ferlay, J. (1999), “Estimates of the world mortality from 25 cancers in 1990,” International Journal of Cancer, vol. 83, pp. 18–29.

    Article  CAS  Google Scholar 

  40. Press, W.H., Flattery, B.P., Teukovsky, S.A., and Vetterling, W.T. (1988), Numerical Recipes in C, Cambridge, UK: University Press.

    Google Scholar 

  41. Ravdin, P.M. and Clark, G.M. (1992), “A practical application of neural network analysis for predicting outcome of individual breast cancer patients,” Breast Cancer Research and Treatment, vol. 22, pp. 285–293.

    Article  PubMed  CAS  Google Scholar 

  42. Schnorrenberg, F., Pattichis, C.S., Kyriacou, K., Vassiliou, M., and Schizas, C.N. (1996), “Computer-aided classification of breast cancer nuclei,” Technology and Health Care, vol. 4, no. 2, pp. 147–161.

    PubMed  CAS  Google Scholar 

  43. Schnorrenberg, F., Pattichis, C.S., Kyriacou, K., and Schizas, C.N. (1997), “Computer-aided detection of breast cancer nuclei,” IEEE Trans. Information Technology in Biomedicine, vol. 1, no. 2, pp. 128–140.

    Article  CAS  Google Scholar 

  44. Schnorrenberg, F., Tsapatsoulis, N., Pattichis, C.S., Schizas, C.N., Kollias, S., Vassiliou, M., Adamou, A., and Kyriacou, K. (2000), “Improved detection of breast cancer nuclei using modular neural networks,” IEEE Engineering in Medicine and Biology Magazine, Special Issue on Classifying Patterns with Neural Networks, vol. 19, no. 1, pp. 48–63.

    Google Scholar 

  45. Schnorrenberg, F., Pattichis, C.S., Kyriacou, K., and Schizas, C.N. (2000), “Content-based retrieval of breast cancer biopsy slides,” Technology and Health Care, vol. 8, to appear in Dec.

    Google Scholar 

  46. Störkel, S., Reichert, T., Reiffen, K.A., and Wagner, W. (1993), “EGFR and PCNA expression in oral squamous cell carcinomas: a valuable tool in estimating the patients prognosis,” European Journal of Cancer, vol. 29B, pp. 273–277.

    Google Scholar 

  47. Taylor, C.R. (1993), “An exaltation of experts: concerted efforts in the standardization of immunohistochemistry,” Applied Immunohistochemistry, vol. 1, pp. 232–243.

    Google Scholar 

  48. True, L.D. (1996), “Morphometric applications in anatomic pathology,” Human Pathology, vol. 27, pp. 450–467.

    Article  PubMed  CAS  Google Scholar 

  49. Weinberg, D.S. (1994), “Quantitative immunocytochemistry in pathology,” in: Marchevsky, A.M. and Bartels, P.H. (Eds.), Image Analysis: a Primer for Pathologists, New York, USA: Raven Press Ltd., pp. 235–260.

    Google Scholar 

  50. Willemse, F., Nap, M., Henzen-Logmans, S.C., and Eggink, H.F. (1994), “Quantification of area percentage of immunohistochemical staining by true color image analysis with application of fixed thresholds,” Analytical and Quantitative Cytology and Histology, vol. 16, no. 5, pp. 357–364.

    PubMed  CAS  Google Scholar 

  51. Wolberg, W.H., Street, W.N., and Mangassarian, O.L. (1999), “Importance of nuclear morphology in breast cancer prognosis,” Clinical Cancer Research, vol. 11, pp. 3542–3548.

    Google Scholar 

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Authors
  1. C. S. Pattichis
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  2. F. Schnorrenberg
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  3. C. N. Schizas
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  4. M. S. Pattichis
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  5. K. Kyriacou
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Editor information

Editors and Affiliations

  1. Technical University of Munich, Ismaninger Straße 22, 81675, München, Germany

    Manfred Schmitt

  2. Romanian Academy, Calea Victoriei 125, Bucharest, Romania

    Horia-Nicolai Teodorescu

  3. The Queens Elizabeth Hospital, Woodville Road, 5011, Woodville, Adelaide, South Australia, Australia

    Ashlesha Jain

  4. Bellevue Residential Care Centre, 5050, Bellevue Heights, Adelaide, South Australia, Australia

    Ajita Jain

  5. Julia Farr Services, Fullarton Road, 5063, Adelaide, South Australia, Australia

    Sandyha Jain

  6. Knowledge-Based Intelligent, Engineering Systems Centre, University of South Australia, 5095, Mawson Lakes, Adelaide, South Australia, Australia

    Lakhmi C. Jain

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© 2002 Springer-Verlag Berlin Heidelberg

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Pattichis, C.S., Schnorrenberg, F., Schizas, C.N., Pattichis, M.S., Kyriacou, K. (2002). A Modular Neural Network System for the Analysis of Nuclei in Histopathological Sections. In: Schmitt, M., Teodorescu, HN., Jain, A., Jain, A., Jain, S., Jain, L.C. (eds) Computational Intelligence Processing in Medical Diagnosis. Studies in Fuzziness and Soft Computing, vol 96. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1788-1_11

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  • DOI: https://doi.org/10.1007/978-3-7908-1788-1_11

  • Publisher Name: Physica, Heidelberg

  • Print ISBN: 978-3-7908-2509-1

  • Online ISBN: 978-3-7908-1788-1

  • eBook Packages: Springer Book Archive

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