nach oben

Erschienen in:

2013 | OriginalPaper | Buchkapitel

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

verfasst von : Peter J. Schüffler, Thomas J. Fuchs, Cheng Soon Ong, Volker Roth, Joachim M. Buhmann

Erschienen in: Similarity-Based Pattern Analysis and Recognition

Verlag: Springer London

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

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.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Vorheriges Kapitel A Game-Theoretic Approach to Pairwise Clustering and Matching

Nächstes Kapitel Analysis of Brain Magnetic Resonance (MR) Scans for the Diagnosis of Mental Illness

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)

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) CrossRef

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) CrossRef

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) CrossRef

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) CrossRef

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) CrossRef

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) CrossRef

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) CrossRef

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) CrossRef

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.

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)

12.

Breiman, L.: Random forests. Mach. Learn. 45, 5–32 (2001) CrossRefMATH

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) MathSciNetCrossRefMATH

14.

Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: CVPR (2001)

15.

Kuhn, H.W.: The Hungarian method for the assignment problem:. Nav. Res. Logist. Q. 2, 83–97 (1955) CrossRef

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

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) CrossRef

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) CrossRef

19.

Soille, P.: Morphological Image Analysis: Principles and Applications. Springer, New York (2003)

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) CrossRef

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) CrossRef

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) CrossRef

23.

Bagon, S.: Matlab wrapper for graph cut (2006)

24.

Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., Suesstrunk, S.: SLIC Superpixels. Technical report, EPFL, EPFL (2010)

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)

26.

Gonzalez, R.C., Woods, R.E., Eddins, S.L.: Digital image processing using Matlab. 993475 (2003)

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) CrossRef

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

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) CrossRef

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)

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)

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)

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)

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) CrossRef

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) CrossRef

Titel: Automated Analysis of Tissue Micro-Array Images on the Example of Renal Cell Carcinoma
verfasst von: Peter J. Schüffler
Thomas J. Fuchs
Cheng Soon Ong
Volker Roth
Joachim M. Buhmann
Verlag: Springer London
Buch: Similarity-Based Pattern Analysis and Recognition
Print ISBN: 978-1-4471-5627-7

Electronic ISBN: 978-1-4471-5628-4

Copyright-Jahr: 2013
DOI: https://doi.org/10.1007/978-1-4471-5628-4_9

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"