Computational TMA Analysis and Cell Nucleus Classification of Renal Cell Carcinoma
We consider an automated processing pipeline for tissue micro array analysis (TMA) of renal cell carcinoma. It consists of several consecutive tasks, which can be mapped to machine learning challenges. We investigate three of these tasks, namely nuclei segmentation, nuclei classification and staining estimation. We argue for a holistic view of the processing pipeline, as it is not obvious whether performance improvements at individual steps improve overall accuracy. The experimental results show that classification accuracy, which is comparable to trained human experts, can be achieved by using support vector machines (SVM) with appropriate kernels. Furthermore, we provide evidence that the shape of cell nuclei increases the classification performance. Most importantly, these improvements in classification accuracy result in corresponding improvements for the medically relevant estimation of immunohistochemical staining.
Unable to display preview. Download preview PDF.
- 1.Bagon, S.: Matlab wrapper for graph cut (December 2006), http://www.wisdom.weizmann.ac.il/~bagon
- 3.Bosch, A., Zisserman, A., Munoz, X.: Representing shape with a spatial pyramid kernel. In: CIVR 2007: Proceedings of the 6th ACM international conference on Image and video retrieval, pp. 401–408. ACM, New York (2007)Google Scholar
- 7.Fuchs, T.J., Wild, P.J., Moch, H., Buhmann, J.M.: Computational pathology analysis of tissue microarrays predicts survival of renal clear cell carcinoma patients. In: Metaxas, D., Axel, L., Fichtinger, G., Székely, G. (eds.) MICCAI 2008, Part II. LNCS, vol. 5242, pp. 1–8. Springer, Heidelberg (2008)CrossRefGoogle Scholar
- 8.Gonzalez, R.C., Woods, R.E., Eddins, S.L.: Digital image processing using matlab (2003), 993475Google Scholar
- 11.Schölkopf, B., Smola, A.J.: Learning with Kernels. MIT Press, Cambridge (2002)Google Scholar