Efficient Region-based Classification for Whole Slide Images

  • Grégory Apou
  • Benoît Naegel
  • Germain Forestier
  • Friedrich Feuerhake
  • Cédric Wemmert
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 550)


For the past decade, new hardware able to generate very high spatial resolution digital images called Whole Slide Images (WSIs) have been challenging traditional microscopy. But the potential for automation is hindered by the large size of the files, possibly tens of billions of pixels. We propose a fast segmentation method coupled with an intuitive multiclass supervised classification that captures expert knowledge presented as morphological annotations to establish a cartography of a WSI and highlight biological regions of interest. While our primary focus has been the development of a proof of concept for the analysis of breast cancer WSIs acquired after chromogenic immunohistochemistry, this method could also be applied to more general texture-based problems.


Whole slide images Biomedical image processing Segmentation Classification 


  1. 1.
    Gurcan, M.N., Boucheron, L.E., Can, A., Madabhushi, A., Rajpoot, N.M., Yener, B.: Histopathological image analysis: a review. In: IEEE Reviews in Biomedical Engineering (2009)Google Scholar
  2. 2.
    Tavassoli, F.A., Devilee, P.: Pathology and Genetics of Tumours of the Breast and Female Genital Organs. IARCPress, Lyon (2003)Google Scholar
  3. 3.
    Ghaznavi, F., Evans, A., Madabhushi, A., Feldman, M.: Digital imaging in pathology: Whole-slide imaging and beyond. In: Annual Review of Pathology (2013)Google Scholar
  4. 4.
    Signolle, N., Plancoulaine, B., Herlin, P., Revenu, M.: Texture-based multiscale segmentation: application to stromal compartment characterization on ovarian carcinoma virtual slides. In: Elmoataz, A., Lezoray, O., Nouboud, F., Mammass, D. (eds.) ICISP 2008 2008. LNCS, vol. 5099, pp. 173–182. Springer, Heidelberg (2008) CrossRefGoogle Scholar
  5. 5.
    Huang, C.H., Veillard, A., Roux, L., Loménie, N., Racoceanu, D.: Time-efficient sparse analysis of histopathological whole slide images. Comput. Med. Imaging Graph. (2010)Google Scholar
  6. 6.
    Elston, C.W., Ellis, I.O.: Pathological prognostic factors in breast cancer. I. the value of histological grade in breast cancer: experience from a large study with long-term follow-up. Histopathology (1991)Google Scholar
  7. 7.
    Ruiz, A., Sertel, O., Ujaldon, M., Catalyurek, U., Saltz, J., Gurcan, M.: Pathological image analysis using the gpu: stroma classification for neuroblastoma. In: 2007 IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2007) (2007)Google Scholar
  8. 8.
    Sertel, O., Kong, J., Shimada, H., Catalyurek, U.: Computer-aided prognosis of neuroblastoma on whole-slide images: classification of stromal development. Pattern Recogn. (2009)Google Scholar
  9. 9.
    Roullier, V., Lézoray, O., Ta, V.T., Elmoataz, A.: Multi-resolution graph-based analysis of histopathological whole slide images: application to mitotic cell extraction and visualization. Comput. Med. Imaging Graph. (2011)Google Scholar
  10. 10.
    Homeyer, A., Schenk, A., Arlt, J., Dahmen, U., Dirsch, O., Hahn, H.K.: Practical quantification of necrosis in histological whole-slide images. Comput. Med. Imaging Graph. (2013)Google Scholar
  11. 11.
    Moore, A.P., Prince, S.J.D., Warrell, J., Mohammed, U., Jones, G.: Superpixel lattices. In: IEEE Conference on Computer Vision and Pattern Recognition (2008)Google Scholar
  12. 12.
    Montanari, U.: On the optimal detection of curves in noisy pictures. Commun. ACM (1971)Google Scholar
  13. 13.
    Ojala, T., Pietikäinen, M., Harwood, D.: A comparative study of texture measures with classification based on featured distributions. Pattern Recogn. (1996)Google Scholar
  14. 14.
    Wemmert, C., Krüger, J., Forestier, G., Sternberger, L., Feuerhake, F., Gançarski, P.: Stain unmixing in brightfield multiplexed immunohistochemistry. In: IEEE International Conference on Image Processing (2013)Google Scholar
  15. 15.
    Fawcett, T.: An introduction to roc analysis. Pattern Recogn. Lett. (2006)Google Scholar
  16. 16.
    Haindl, M., Mikes̆, S.: Texture segmentation benchmark. In: Proceedings of the 19th International Conference on Pattern Recognition (2008)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Grégory Apou
    • 1
  • Benoît Naegel
    • 1
  • Germain Forestier
    • 2
  • Friedrich Feuerhake
    • 3
  • Cédric Wemmert
    • 1
  1. 1.ICubeUniversity of StrasbourgIllkirchFrance
  2. 2.MIPSUniversity of Haute AlsaceMulhouseFrance
  3. 3.Institute for PathologyHannover Medical SchoolHannoverGermany

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