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
Alcatel TITN Answare (1993), IMMUNO 4.00: User’s Guide, 1st ed., Grenoble, France.
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.
Bartels, P.H. (1992), “Computer generated diagnosis and image analysis, an overview,” Cancer, vol. 69, pp. 1636–1638.
Becker, R.L. and Usaf, M.C. (1995), “Applications of neural networks in histopathology,” Pathologica, vol. 87, no. 3, pp. 246254.
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.
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.
Birdsong, G.G. (1996), “Automated screening of cervical cytology specimens,” Human Pathology, vol. 27, pp. 468–481.
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.
Burke, H.B. (1994), “Artificial neural networks for cancer research. Outcome prediction,” Sem. Surgical Oncology, vol. 10, pp. 73–79.
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.
Cell Analysis Systems Inc. (1990), Cell Analysis Systems: Quantitative Estrogen Progesterone Users Manual,Application Version 2.0, Catalog Number 201325–00, USA.
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.
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.
Cohen, C. (1996), “Image cytometric analysis in pathology,” Human Pathology, vol. 27, no. 5, pp. 482–493.
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.
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.
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.
Demuth, H. and Beale, M. (1994), Neural Network Toolbox, The MathWorks, Inc., Natick, Mass., USA.
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.
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.
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.
Haykin, S. (1994), Neural Networks: a Comprehensive Foundation, New York, USA: Macmillan, 1994.
Hong, Z.-Q. (1991), “Algebraic feature extraction of image for recognition,” Pattern Recognition, vol. 24, no. 3, pp. 211–219.
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.
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.
Jain, A.K. (1989), Fundamentals of Digital Image Processing, Englewood Cliffs, New Jersey, USA: Prentice Hall, 1989.
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.
Kok, M.R. and Boon, M.E. (1996), “Consequences of neural network technology for cervical screening,” Cancer, vol. 78, pp. 112–117.
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.
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.
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.
Man, D. and Hildreth, E. (1980), “Theory of edge detection,” Proc. R. Soc. Lond., vol. B 207, pp. 187–217.
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.
Millot, C. and Dufer, J. (2000), “Clinical applications of image cytometry to human tumour analysis,” Histology Histopathology, vol. 15, no. 4, pp. 1185–200.
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.
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.
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.
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.
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.
Press, W.H., Flattery, B.P., Teukovsky, S.A., and Vetterling, W.T. (1988), Numerical Recipes in C, Cambridge, UK: University Press.
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.
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.
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.
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.
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.
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.
Taylor, C.R. (1993), “An exaltation of experts: concerted efforts in the standardization of immunohistochemistry,” Applied Immunohistochemistry, vol. 1, pp. 232–243.
True, L.D. (1996), “Morphometric applications in anatomic pathology,” Human Pathology, vol. 27, pp. 450–467.
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.
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.
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.
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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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