Advertisement

Automated Analysis of Tissue Micro-Array Images on the Example of Renal Cell Carcinoma

  • Peter J. SchüfflerEmail author
  • Thomas J. Fuchs
  • Cheng Soon Ong
  • Volker Roth
  • Joachim M. Buhmann
Part of the Advances in Computer Vision and Pattern Recognition book series (ACVPR)

Abstract

Automated tissue micro-array analysis forms a challenging problem in computational pathology. The detection of cell nuclei, the classification into malignant and benign as well as the evaluation of their protein expression pattern by immunohistochemical staining are crucial routine steps for human cancer research and oncology. Computational assistance in this field can extremely accelerate the high throughput of the upcoming patient data as well as facilitate the reproducibility and objectivity of qualitative and quantitative measures. In this chapter, we describe an automated pipeline for staining estimation of tissue micro-array images, which comprises nucleus detection, nucleus segmentation, nucleus classification and staining estimation among cancerous nuclei. This pipeline is a practical example for the importance of non-metric effects in this kind of image analysis, e.g., the use of shape information and non-Euclidean kernels improve the nucleus classification performance significantly. The pipeline is explained and validated on a renal clear cell carcinoma dataset with MIB-1 stained tissue micro-array images and survival data of 133 patients. Further, the pipeline is implemented for medical use and research purpose in the free program TMARKER.

Keywords

Support Vector Machine Renal Cell Carcinoma Local Binary Pattern Renal Clear Cell Carcinoma Multiple Kernel 
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.

Notes

Acknowledgement

We thank Aydın Ulaş, Umberto Castellani, Vittorio Murino, Mehmet Gönen, Manuele Bicego, Pasquale Mirtuono, André Martins, Pedro M.Q. Aguiar and Mário A.T. Figueiredo for successful collaborations and inspiring ideas. We want to thank all our co-workers and SIMBAD partners for fruitful discussions.

References

  1. 1.
    Grignon, D.J., Eble, J.N., Bonsib, S.M., Moch, H.: Clear Cell Renal Cell Carcinoma. World Health Organization Classification of Tumours. Pathology and Genetics of Tumours of the Urinary System and Male Genital Organs. IARC Press, Lyon (2004) Google Scholar
  2. 2.
    Bubendorf Juha Kononen, L., Bärlund Anne Kallionimeni, M., Leighton Peter Schraml, S., Mihatsch, M.J., Torhorst, J., Kallionimeni, O.-P., Sauter, G.: 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., Furge, K.A., Kanayama, H.-o., Kagawa, S., Haab, B.B., Tean Teh, B.: Gene expression profiling of clear cell renal cell carcinoma: gene identification and prognostic classification. Proc. Natl. Acad. Sci. USA 98(17), 9754–9759 (2001) CrossRefGoogle Scholar
  4. 4.
    Schraml Holger Moch, P., Mirlacher Lukas Bubendorf, M., Gasser Juha Kononen, T., Kallioniemi, O.P., Mihatsch, M.J., Sauter, G.: 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) CrossRefGoogle Scholar
  5. 5.
    Amin, M.B., Young, A.N., Lim, S.D., Moreno, C.S., Petros, J.A. Cohen, C., Neish, A.S., Marshall, F.F.: 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) CrossRefGoogle Scholar
  6. 6.
    Tannapfel, A., Hahn, H.A., Katalinic, A., Fietkau, R.J., Kühn, R., Wittekind, C.W.: 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., Maria Tinner, E., Süess, K., Wagner, U., Forster, T., Kononen, J., Fijan, A., Bruderer, J., Schmid, U., Ackermann, D., Maurer, R., Alund, G., Knönagel, H., Rist, M., Anabitarte, M., Hering, F., Hardmeier, T., Schoenenberger, A.J., Flury, R., Jäger, P., Luc Fehr, J., Schraml, P., Moch, H., Mihatsch, M.J., Gasser, T., Sauter, G.: 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 Trans. Inf. Technol. Biomed. 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. Hum. Pathol. 38(10), 1454–1462 (2007) CrossRefGoogle Scholar
  10. 10.
    Fuchs, T.J., Wild, P.J., Schüffler, P.J.: Labeled IHC images of RCC (2012). doi: 10.5881/LABELED-IHC-IMAGES-OF-RCC
  11. 11.
    Fuchs, T.J., Haybaeck, J., Wild, P.J., Heikenwalder, M., Moch, H., Aguzzi, A., Buhmann, J.M.: Randomized tree ensembles for object detection in computational pathology. In: ISVC (1). Lecture Notes in Computer Science, vol. 5875, pp. 367–378. Springer, Berlin (2009) Google Scholar
  12. 12.
    Breiman, L.: Random forests. Mach. Learn. 45, 5–32 (2001) CrossRefzbMATHGoogle Scholar
  13. 13.
    Strobl, C., Boulesteix, A.-L., Augustin, T.: Unbiased split selection for classification trees based on the Gini index. Comput. Stat. Data Anal. 52(1), 483–501 (2007) MathSciNetCrossRefzbMATHGoogle Scholar
  14. 14.
    Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: CVPR (2001) Google Scholar
  15. 15.
    Kuhn, H.W.: The Hungarian method for the assignment problem:. Nav. Res. Logist. Q. 2, 83–97 (1955) CrossRefGoogle Scholar
  16. 16.
    R Development Core Team: R: a Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna (2009). ISBN 3-900051-07-0 Google Scholar
  17. 17.
    Glotsos, D., Spyridonos, P., Cavouras, D., Ravazoula, P., Arapantoni Dadioti, P., Nikiforidis, G.: An image-analysis system based on support vector machines for automatic grade diagnosis of brain-tumour astrocytomas in clinical routine. Med. Inform. Internet Med. 30(3), 179–193 (2005) CrossRefGoogle Scholar
  18. 18.
    Fuchs, T.J., Lange, T., Wild, P.J., Moch, H., Buhmann, J.M.: Weakly supervised cell nuclei detection and segmentation on tissue microarrays of renal cell carcinoma. In: Pattern Recognition. DAGM 2008. Lecture Notes in Computer Science, vol. 5096, pp. 173–182. Springer, Berlin (2008) CrossRefGoogle Scholar
  19. 19.
    Soille, P.: Morphological Image Analysis: Principles and Applications. Springer, New York (2003) Google Scholar
  20. 20.
    Boykov, Y., Kolmogorov, V.: An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision. IEEE Trans. Pattern Anal. Mach. Intell. 26(9), 1124–1137 (2004) CrossRefGoogle Scholar
  21. 21.
    Boykov, Y., Veksler, O., Zabih, R.: Efficient approximate energy minimization via graph cuts. IEEE Trans. Pattern Anal. Mach. Intell. 20(12), 1222–1239 (2001) CrossRefGoogle Scholar
  22. 22.
    Kolmogorov, V., Zabih, R.: What energy functions can be minimized via graph cuts? IEEE Trans. Pattern Anal. Mach. Intell. 26(2), 147–159 (2004) CrossRefGoogle Scholar
  23. 23.
    Bagon, S.: Matlab wrapper for graph cut (2006) Google Scholar
  24. 24.
    Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., Suesstrunk, S.: SLIC Superpixels. Technical report, EPFL, EPFL (2010) Google Scholar
  25. 25.
    Schüffler, P.J., Fuchs, T.J., Soon Ong, C., Roth, V., Buhmann, J.M.: Computational TMA analysis and cell nucleus classification of renal cell carcinoma. In: Proceedings of the 32nd DAGM Conference on Pattern Recognition, pp. 202–211. Springer, Berlin (2010) Google Scholar
  26. 26.
    Gonzalez, R.C., Woods, R.E., Eddins, S.L.: Digital image processing using Matlab. 993475 (2003) Google Scholar
  27. 27.
    Bosch, A., Zisserman, A., Munoz, X.: Representing shape with a spatial pyramid kernel. In: CIVR’07: Proceedings of the 6th ACM International Conference on Image and Video Retrieval, pp. 401–408. ACM, New York (2007) CrossRefGoogle Scholar
  28. 28.
    Schüffler, P.J., Ulaş, A., Castellani, U., Murino, V.: A multiple kernel learning algorithm for cell nucleus classification of renal cell carcinoma. In: Proceedings of the International Conference on Image Analysis and Processing, ICIAP’11 (2011). Page accepted Google Scholar
  29. 29.
    Gönen, M., Ulaş, A., Schüffler, P.J., Castellani, U., Murino, V.: Combining data sources nonlinearly for cell nucleus classification of renal cell carcinoma. In: Pelillo, M., Hancock, E.R. (eds.) Proceedings of the International Workshop on Similarity-Based Pattern Analysis, SIMBAD’11. Lecture Notes in Computer Science, vol. 7005, pp. 250–260. Springer, Berlin (2011) CrossRefGoogle Scholar
  30. 30.
    Bach, F.R., Lanckriet, G.R.G., Jordan, M.I.: Multiple kernel learning, conic duality, and the SMO algorithm. In: Proceedings of the 21st International Conference on Machine Learning, pp. 41–48 (2004) Google Scholar
  31. 31.
    Cortes, C., Mohri, M., Rostamizadeh, A.: Learning non-linear combinations of kernels. In: Advances in Neural Information Processing Systems, vol. 22, pp. 396–404 (2010) Google Scholar
  32. 32.
    Ulaş, A., Schüffler, P.J., Bicego, M., Castellani, U., Murino, V.: Hybrid generative–discriminative nucleus classification of renal cell carcinoma. In: Pelillo, M., Hancock, E.R. (eds.) Proceedings of the International Workshop on Similarity-Based Pattern Analysis, SIMBAD’11. Lecture Notes in Computer Science, vol. 7005, pp. 77–88. Springer, Berlin (2011) Google Scholar
  33. 33.
    Bicego, M., Ulaş, A., Schüffler, P.J., Castellani, U., Mirtuono, P., Murino, V., Aguiar, P.M.Q., Martins, A., Figueiredo, M.A.T.: Renal cancer cell classification using generative embeddings and information theoretic kernels. In: Loog, M. (ed.) IAPR International Conference on Pattern Recognition in Bioinformatics, PRIB’11. Lecture Notes in Bioinformatics (accepted), vol. 7036. Springer, Berlin (2011) Google Scholar
  34. 34.
    Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software: an update. ACM SIGKDD Explor. Newsl. 11, 10–18 (2009) CrossRefGoogle Scholar
  35. 35.
    Fuchs, T.J., Buhmann, J.M.: Computational pathology: challenges and promises for tissue analysis. Comput. Med. Imaging Graph. 35(7–8), 515–530 (2011) CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London 2013

Authors and Affiliations

  • Peter J. Schüffler
    • 1
    Email author
  • Thomas J. Fuchs
    • 2
  • Cheng Soon Ong
    • 3
  • Volker Roth
    • 4
  • Joachim M. Buhmann
    • 1
  1. 1.Swiss Federal Institute of Technology ZurichZurichSwitzerland
  2. 2.California Institute of TechnologyPasadenaUSA
  3. 3.National ICT AustraliaMelbourneAustralia
  4. 4.Computer Science DepartmentUniversity of BaselBaselSwitzerland

Personalised recommendations