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

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Similarity-Based Pattern Analysis and Recognition

Part of the book series: Advances in Computer Vision and Pattern Recognition ((ACVPR))

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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.

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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.

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Authors and Affiliations

  1. Swiss Federal Institute of Technology Zurich, Zurich, Switzerland

    Peter J. Schüffler & Joachim M. Buhmann

  2. California Institute of Technology, Pasadena, CA, 91125, USA

    Thomas J. Fuchs

  3. National ICT Australia, Melbourne, Australia

    Cheng Soon Ong

  4. Computer Science Department, University of Basel, Basel, Switzerland

    Volker Roth

Authors
  1. Peter J. Schüffler
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  2. Thomas J. Fuchs
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  3. Cheng Soon Ong
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  4. Volker Roth
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  5. Joachim M. Buhmann
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Corresponding author

Correspondence to Peter J. Schüffler .

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Editors and Affiliations

  1. DAIS, Ca' Foscari University of Venice, Venezia Mestre, Italy

    Marcello Pelillo

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Schüffler, P.J., Fuchs, T.J., Ong, C.S., Roth, V., Buhmann, J.M. (2013). Automated Analysis of Tissue Micro-Array Images on the Example of Renal Cell Carcinoma. In: Pelillo, M. (eds) Similarity-Based Pattern Analysis and Recognition. Advances in Computer Vision and Pattern Recognition. Springer, London. https://doi.org/10.1007/978-1-4471-5628-4_9

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  • DOI: https://doi.org/10.1007/978-1-4471-5628-4_9

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-4471-5627-7

  • Online ISBN: 978-1-4471-5628-4

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