Abstract
Renal cell carcinoma (RCC) can be diagnosed by histological tissue analysis where exact counts of cancerous cell nuclei are required. We propose a completely automated image analysis pipeline to predict the survival of RCC patients based on the analysis of immunohistochemical staining of MIB-1 on tissue microarrays. A random forest classifier detects cell nuclei of cancerous cells and predicts their staining. The classifier training is achieved by expert annotations of 2300 nuclei gathered from tissues of 9 different RCC patients. The application to a test set of 133 patients clearly demonstrates that our computational pathology analysis matches the prognostic performance of expert pathologists.
Chapter PDF
Similar content being viewed by others
Keywords
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.
References
Kononen, J., Bubendorf, L., et al.: Tissue microarrays for high-throughput molecular profiling of tumor specimens. Nat. Med. 4(7), 844–847 (1998)
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)
Yang, L., Chen, W., Meer, P., Salaru, G., Feldman, M.D., Foran, D.J.: High throughput analysis of breast cancer specimens on the grid. Med. Image Comput. 10 (Pt 1), 617–625 (2007)
Rabinovich, A., Agarwal, S., Laris, C.A., Price, J., Belongie, S.: Unsupervised color decomposition of histologically stained tissue samples
Hall, B., Chen, W., Reiss, M., Foran, D.J.: A clinically motivated 2-fold framework for quantifying and classifying immunohistochemically stained specimens, pp. 287–294 (2007)
Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features (2001)
Ahonen, T., Hadid, A., Pietikainen, M.: Face recognition with local binary patterns. In: Pajdla, T., Matas, J(G.) (eds.) ECCV 2004. LNCS, vol. 3021, pp. 469–481. Springer, Heidelberg (2004)
Hadid, A., Pietikainen, M., Ahonen, T.: A discriminative feature space for detecting and recognizing faces. In: CVPR 2004, June 27– July 2, 2004, vol. 2, II–797–II–804 (2004)
Breiman, L.: Random forests (1999)
Freund, Y., Schapire, R.: Experiments with a new boosting algorithm. In: Machine Learning: Proceedings of the Thirteenth International Conference, pp. 148–156 (1996)
Bosch, A., Zisserman, A., Munoz, X.: Image classification using random forests and ferns. In: ICCV 2007, October 14-21, 2007, pp. 1–8 (2007)
Author information
Authors and Affiliations
Editor information
Electronic Supplementary Material
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Fuchs, T.J., Wild, P.J., Moch, H., Buhmann, J.M. (2008). Computational Pathology Analysis of Tissue Microarrays Predicts Survival of Renal Clear Cell Carcinoma Patients. In: Metaxas, D., Axel, L., Fichtinger, G., Székely, G. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2008. MICCAI 2008. Lecture Notes in Computer Science, vol 5242. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85990-1_1
Download citation
DOI: https://doi.org/10.1007/978-3-540-85990-1_1
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-85989-5
Online ISBN: 978-3-540-85990-1
eBook Packages: Computer ScienceComputer Science (R0)