Weakly Supervised Cell Nuclei Detection and Segmentation on Tissue Microarrays of Renal Clear Cell Carcinoma

  • Thomas J. Fuchs
  • Tilman Lange
  • Peter J. Wild
  • Holger Moch
  • Joachim M. Buhmann
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5096)


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.


Renal Cell Carcinoma Renal Cell Cancer Clear Cell Renal Cell Carcinoma Renal Cell Carcinoma Patient Renal Clear Cell Carcinoma 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    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)Google Scholar
  2. 2.
    Kononen, J., Bubendorf, L., et al.: Tissue microarrays for high-throughput molecular profiling of tumor specimens. Nat. Med. 4(7), 844–847 (1998)CrossRefGoogle Scholar
  3. 3.
    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)Google Scholar
  4. 4.
    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)Google Scholar
  5. 5.
    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)Google Scholar
  6. 6.
    Tannapfel, A., Hahn, H.A., et al.: Prognostic value of ploidy and proliferation markers in renal cell carcinoma. Cancer 77(1), 164–171 (1996)CrossRefGoogle Scholar
  7. 7.
    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)CrossRefGoogle Scholar
  8. 8.
    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)CrossRefGoogle Scholar
  9. 9.
    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)CrossRefGoogle Scholar
  10. 10.
    Soille, P.: Morphological Image Analysis: Principles and Applications. Springer, New York (2003)zbMATHGoogle Scholar
  11. 11.
    Sonka, M., Hlavac, V., Boyle, R.: Image Processing, Analysis, and Machine Vision. Thomson-Engineering (2007)Google Scholar
  12. 12.
    Canny, J.: A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. 8(6), 679–698 (1986)CrossRefGoogle Scholar
  13. 13.
    Vapnik, V.: Statistical Learning Theory. Wiley, New York (forthcoming, 1998)zbMATHGoogle Scholar
  14. 14.
    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)CrossRefGoogle Scholar
  15. 15.
    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)Google Scholar
  16. 16.
    Kuhn, H.W.: The hungarian method for the assignment problem: Naval Research Logistic Quarterly 2, 83–97 (1955)CrossRefMathSciNetGoogle Scholar
  17. 17.
    Jain, A.K., Dubes, R.C.: Algorithms for clustering data. Prentice-Hall, Inc., Upper Saddle River (1988)zbMATHGoogle Scholar
  18. 18.
    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)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Thomas J. Fuchs
    • 1
    • 3
  • Tilman Lange
    • 1
  • Peter J. Wild
    • 2
  • Holger Moch
    • 2
    • 3
  • Joachim M. Buhmann
    • 1
    • 3
  1. 1.Institute for Computational ScienceETH ZürichSwitzerland
  2. 2.Institute of PathologyUniversity Hospital Zürich, University ZürichSwitzerland
  3. 3.Competence Center for Systems Physiology and Metabolic DiseasesETH Zürich 

Personalised recommendations