Abstract
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.
Keywords
This is a preview of subscription content, log in via an institution.
References
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)
Tavassoli, F.A., Devilee, P.: Pathology and Genetics of Tumours of the Breast and Female Genital Organs. IARCPress, Lyon (2003)
Ghaznavi, F., Evans, A., Madabhushi, A., Feldman, M.: Digital imaging in pathology: Whole-slide imaging and beyond. In: Annual Review of Pathology (2013)
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)
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)
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)
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)
Sertel, O., Kong, J., Shimada, H., Catalyurek, U.: Computer-aided prognosis of neuroblastoma on whole-slide images: classification of stromal development. Pattern Recogn. (2009)
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)
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)
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)
Montanari, U.: On the optimal detection of curves in noisy pictures. Commun. ACM (1971)
Ojala, T., Pietikäinen, M., Harwood, D.: A comparative study of texture measures with classification based on featured distributions. Pattern Recogn. (1996)
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)
Fawcett, T.: An introduction to roc analysis. Pattern Recogn. Lett. (2006)
Haindl, M., Mikes̆, S.: Texture segmentation benchmark. In: Proceedings of the 19th International Conference on Pattern Recognition (2008)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Apou, G., Naegel, B., Forestier, G., Feuerhake, F., Wemmert, C. (2015). Efficient Region-based Classification for Whole Slide Images. In: Battiato, S., Coquillart, S., Pettré, J., Laramee, R., Kerren, A., Braz, J. (eds) Computer Vision, Imaging and Computer Graphics - Theory and Applications. VISIGRAPP 2014. Communications in Computer and Information Science, vol 550. Springer, Cham. https://doi.org/10.1007/978-3-319-25117-2_15
Download citation
DOI: https://doi.org/10.1007/978-3-319-25117-2_15
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-25116-5
Online ISBN: 978-3-319-25117-2
eBook Packages: Computer ScienceComputer Science (R0)