Computational TMA Analysis and Cell Nucleus Classification of Renal Cell Carcinoma

  • Peter J. Schüffler
  • Thomas J. Fuchs
  • Cheng Soon Ong
  • Volker Roth
  • Joachim M. Buhmann
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6376)

Abstract

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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Peter J. Schüffler
    • 1
  • Thomas J. Fuchs
    • 1
  • Cheng Soon Ong
    • 1
  • Volker Roth
    • 1
  • Joachim M. Buhmann
    • 1
  1. 1.Department of Computer ScienceETH ZurichSwitzerland

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