Abstract
Tissue microarray (TMA) is a high throughput analysis tool to identify new diagnostic and prognostic markers in human cancers. However, standard automated method in tumour detection on routine histochemical images for TMA construction is under developed. This paper presents a MRF based Bayesian learning system for automated tumour cell detection in routine histochemical virtual slides to assist TMA construction. The experimental results show that the proposed method is able to achieve 80% accuracy on average by pixel-based quantitative performance evaluation that compares the automated segmentation outputs with the manually marked ground truth data. The presented technique greatly reduces labor-intensive workloads for pathologists, highly speeds up the process of TMA construction and allows further exploration of fully automated TMA analysis.
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References
Avninder, S., Ylaya, K., Hewitt, S.M.: Tissue microarray: a simple technology that has revolutionized research in pathology. J. Postgrad. Med. 54, 158–162 (2008)
Berthod, M., Kato, Z., Yu, S., Zerubia, J.: Bayesian image classification using Markov random fields. Image and Vision Computing 14, 285–295 (1996)
Besag, J.: On the statistical analysis of dirty pictures. IJ Roy. Statis. Soc. B (1986)
Brey, E.M., Lalani, Z., Johnston, C., Wong, M., McIntire, L.V., Duke, P.J., Patrick Jr., C.W.: Automated selection of DAB-labeled tissue for immunohistochemical quantification. J Histochem Cytochem 51(5), 575–584 (2003)
Geman, S., Geman, D.: Stochastic relaxation, Gibbs distributions and the Bayesian restoration of images. IEEE Trans Patt. Analysis and Mach. Inte. 6, 721–741 (1984)
Jawhar, N.M.: Tissue Microarray: A rapidly evolving diagnostic and research tool. Ann. Saudi Med. 29, 123–127 (2009)
Kato, Z., Zerubia, J., Berthod, M.: Satellite Image Classification Using a Modified Metropolis Dynamics. In: International Conference on Acoustics, Speech and Signal Processing, vol. 3, pp. 573–576 (1992)
Karacali, B., Tozeren, A.: Automated detection of regions of interest for tissue microarray experiments: an image texture analysis. BMC Med. Imaging 7 (2007)
Kohavi, R., Provost, F.: Glossary of Terms. Machine Learning 30, 271–274 (1998)
Law, K.W., Lamb, K.Y., Lama, F.K., Wonga, K.W., Poona, L.S., Chan, H.Y.: Image analysis system for assessment of immunohistochemically stained proliferative marker (MIB-1) in oesophageal squamous cell carcinoma. Computer Methods and Programs in Biomedicine 70(1), 37–45 (2003)
Mao, K.Z., Zhao, P., Tan, P.H.: Supervised learning-based cell image segmentation for p53 immunohistochemistry. IEEE Trans. Biomed. Eng. 53(6), 1153–1163 (2006)
Metropolis, N., Rosenbluth, A., Rosenbluth, M., Teller, A., Teller, E.: Equation of state calculations by fast computing machines. J. Chem. Physics. 21, 1087–1092 (1953)
Sauter, G., Simon, R., Hillan, K.: Tissue microarrays in drug discovery. Nature Reviews Drug Discovery 2, 962–972 (2003)
Voduc, D., Kenney, C., Nielsen, T.O.: Tissue microarrays in clinical oncology. Semin. Radiat. Oncol. 18, 89–97 (2008)
Wang, C.: Robust Auto-Classification of Adenocarcinoma and Squamous Carcinoma for Patient-targeted Therapy. Modern Pathology (under consideration)
Zhang, D.Y., et al.: Proteomics, pathway array and signaling network-based medicine in cancer. Cell. Div. 4, 20 (2009)
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Wang, CW. (2010). A Bayesian Learning Application to Automated Tumour Segmentation for Tissue Microarray Analysis. In: Wang, F., Yan, P., Suzuki, K., Shen, D. (eds) Machine Learning in Medical Imaging. MLMI 2010. Lecture Notes in Computer Science, vol 6357. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15948-0_13
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DOI: https://doi.org/10.1007/978-3-642-15948-0_13
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