Abstract
Renal cell carcinoma (RCC) is one of the ten most frequent malignancies in Western societies and can be diagnosed by histological tissue analysis. Current diagnostic rules rely on exact counts of cancerous cell nuclei which are manually counted by pathologists.
We propose a complete imaging pipeline for the automated analysis of tissue microarrays of renal cell cancer. At its core, the analysis system consists of a novel weakly supervised classification method, which is based on an iterative morphological algorithm and a soft-margin support vector machine. The lack of objective ground truth labels to validate the system requires the combination of expert knowledge of pathologists. Human expert annotations of more than 2000 cell nuclei from 9 different RCC patients are used to demonstrate the superior performance of the proposed algorithm over existing cell nuclei detection approaches.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Grignon, D.J., Eble, J.N., Bonsib, S.M., Moch, H.: Clear cell renal cell carcinoma. In: World Health Organization Classification of Tumours. Pathology and Genetics of Tumours of the Urinary System and Male Genital Organs, IARC Press (2004)
Kononen, J., Bubendorf, L., et al.: Tissue microarrays for high-throughput molecular profiling of tumor specimens. Nat. Med. 4(7), 844–847 (1998)
Takahashi, M., Rhodes, D.R., et al.: Gene expression profiling of clear cell renal cell carcinoma: gene identification and prognostic classification. Proc. Natl. Acad. Sci. U S A. 98(17), 9754–9759 (2001)
Moch, H., Schraml, P., et al.: High-throughput tissue microarray analysis to evaluate genes uncovered by cdna microarray screening in renal cell carcinoma. Am. J. Pathol. 154(4), 981–986 (1999)
Young, A.N., Amin, M.B., et al.: Expression profiling of renal epithelial neoplasms: a method for tumor classification and discovery of diagnostic molecular markers. Am. J. Pathol. 158(5), 1639–1651 (2001)
Tannapfel, A., Hahn, H.A., et al.: Prognostic value of ploidy and proliferation markers in renal cell carcinoma. Cancer 77(1), 164–171 (1996)
Nocito, A., Bubendorf, L., et al.: Microarrays of bladder cancer tissue are highly representative of proliferation index and histological grade. J. Pathol. 194(3), 349–357 (2001)
Yang, L., Meer, P., Foran, D.J.: Unsupervised segmentation based on robust estimation and color active contour models. IEEE Transactions on Information Technology in Biomedicine 9(3), 475–486 (2005)
Mertz, K.D., Demichelis, F., Kim, R., Schraml, P., Storz, M., Diener, P.A., Moch, H., Rubin, M.A.: Automated immunofluorescence analysis defines microvessel area as a prognostic parameter in clear cell renal cell cancer. Human Pathology 38(10), 1454–1462 (2007)
Soille, P.: Morphological Image Analysis: Principles and Applications. Springer, New York (2003)
Sonka, M., Hlavac, V., Boyle, R.: Image Processing, Analysis, and Machine Vision. Thomson-Engineering (2007)
Canny, J.: A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. 8(6), 679–698 (1986)
Vapnik, V.: Statistical Learning Theory. Wiley, New York (forthcoming, 1998)
Hall, B., Chen, W., Reiss, M., Foran, D.J.: A clinically motivated 2-fold framework for quantifying and classifying immunohistochemically stained specimens. In: Ayache, N., Ourselin, S., Maeder, A. (eds.) MICCAI 2007, Part II. LNCS, vol. 4792, pp. 287–294. Springer, Heidelberg (2007)
Yang, L., Chen, W., Meer, P., Salaru, G., Feldman, M.D., Foran, D.J.: High throughput analysis of breast cancer specimens on the grid. In: Med. Image Comput. Comput. Assist. Interv. Int. Conf. Med. Image Comput. Comput. Assist. Interv., vol. 10(pt. 1), pp. 617–25 (2007)
Kuhn, H.W.: The hungarian method for the assignment problem: Naval Research Logistic Quarterly 2, 83–97 (1955)
Jain, A.K., Dubes, R.C.: Algorithms for clustering data. Prentice-Hall, Inc., Upper Saddle River (1988)
Glotsos, D.: An image-analysis system based on support vector machines for automatic grade diagnosis of brain-tumour astrocytomas in clinical routine. Medical Informatics and the Internet in Medicine 30(3), 179–193 (2005)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Fuchs, T.J., Lange, T., Wild, P.J., Moch, H., Buhmann, J.M. (2008). Weakly Supervised Cell Nuclei Detection and Segmentation on Tissue Microarrays of Renal Clear Cell Carcinoma. In: Rigoll, G. (eds) Pattern Recognition. DAGM 2008. Lecture Notes in Computer Science, vol 5096. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69321-5_18
Download citation
DOI: https://doi.org/10.1007/978-3-540-69321-5_18
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-69320-8
Online ISBN: 978-3-540-69321-5
eBook Packages: Computer ScienceComputer Science (R0)