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Whole Slide Image Registration for the Study of Tumor Heterogeneity

Part of the Lecture Notes in Computer Science book series (LNIP,volume 11039)

Abstract

Consecutive thin sections of tissue samples make it possible to study local variation in e.g. protein expression and tumor heterogeneity by staining for a new protein in each section. In order to compare and correlate patterns of different proteins, the images have to be registered with high accuracy. The problem we want to solve is registration of gigapixel whole slide images (WSI). This presents 3 challenges: (i) Images are very large; (ii) Thin sections result in artifacts that make global affine registration prone to very large local errors; (iii) Local affine registration is required to preserve correct tissue morphology (local size, shape and texture). In our approach we compare WSI registration based on automatic and manual feature selection on either the full image or natural sub-regions (as opposed to square tiles). Working with natural sub-regions, in an interactive tool makes it possible to exclude regions containing scientifically irrelevant information. We also present a new way to visualize local registration quality by a Registration Confidence Map (RCM). With this method, intra-tumor heterogeneity and characteristics of the tumor microenvironment can be observed and quantified.

Keywords

  • Whole slide image
  • Registration
  • Digital pathology

C. Wählby—European Research council for funding via ERC Consolidator grant 682810 to C. Wählby.

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References

  1. Sucaet, Y., Waelput, W.: Digital Pathology. SCS. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-319-08780-1

    CrossRef  Google Scholar 

  2. Ameisen, D.: Towards better digital pathology workflows: programming libraries for high-speed sharpness assessment of whole slide images. Diagn. Pathol. 9(Suppl. 1), S3 (2014). https://doi.org/10.1186/1746-1596-9-S1-S3

    CrossRef  Google Scholar 

  3. Spagnolo, D., et al.: Platform for quantitative evaluation of spatial intratumoral heterogeneity in multiplexed fluorescence images. Cancer Res. 77, e71–e74 (2017). American Association for Cancer Research

    CrossRef  Google Scholar 

  4. Moles Lopez, X., et al.: Registration of whole immunohistochemical slide images: an efficient way to characterize biomarker colocalization. J. Am. Med. Inform. Assoc. 22(1), 86–99 (2015)

    CrossRef  Google Scholar 

  5. Ruifrok, A.C.: Quantification of histochemical staining by color deconvolution. Anal. Quant. Cytol. Histol. 23, 291–299 (2002)

    Google Scholar 

  6. Viergever, M., et al.: A survey of medical image registration. Med. Image Anal. 33, 140–144 (2016)

    CrossRef  Google Scholar 

  7. Gurcan, M.N., et al.: Histopathological image analysis: a review. IEEE Rev. Biomed. Eng. 2, 147 (2009)

    CrossRef  Google Scholar 

  8. Cooper, L., et al.: Feature-based registration of histopathology images with different stains: an application for computerized follicular lymphoma prognosis. Comput. Methods Programs Biomed. 96(3), 182–192 (2009)

    CrossRef  Google Scholar 

  9. Trahearn, N., et al.: Hyper-stain inspector: a framework for robust registration and localised co-expression analysis of multiple whole-slide images of serial histology sections. Sci. Rep. 7, 5641 (2017)

    CrossRef  Google Scholar 

  10. Wemmert, C., et al.: Stain unmixing in brightfield multiplexed immunohistochemistry. In: 2013 IEEE International Conference on Image Processing (2013)

    Google Scholar 

  11. van Der Laak, J.A., et al.: Hue-saturation-density (HSD) model for stain recognition in digital images from transmitted light microscopy (2000)

    Google Scholar 

  12. An open-source, viewer for high-resolution zoomable images, in JavaScript. https://openseadragon.github.io. Accessed 13 May 2018

  13. Image J Feature extraction. https://imagej.net/feature_extraction. Accessed 24 Sept 2015

  14. Avron, H., et al.: Blendenpik: Supercharging LAPACK’s least-squares solver. SIAM J. Sci. Comput. 32(3), 1217–1236 (2010)

    MathSciNet  CrossRef  Google Scholar 

  15. Oheim, M., Li, D.: Quantitative colocalisation imaging: concepts, measurements, and pitfalls. In: Shorte, S.L., Frischknecht, F. (eds.) Imaging Cellular and Molecular Biological Functions. Principles and Practice, pp. 117–155. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-71331-9_5

    CrossRef  Google Scholar 

  16. Costes, S.V., et al.: Automatic and quantitative measurement of protein-protein colocalization in live cells. Biophys. J. 86, 3993–4003 (2004)

    CrossRef  Google Scholar 

  17. Rohlfing, T.: Image similarity and tissue overlaps as surrogates for image registration accuracy: widely used but unreliable. IEEE Trans. Med. Imaging 31(2), 153–163 (2012)

    CrossRef  Google Scholar 

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Correspondence to Leslie Solorzano , Gabriela M. Almeida , Bárbara Mesquita , Diana Martins , Carla Oliveira or Carolina Wählby .

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Solorzano, L., Almeida, G.M., Mesquita, B., Martins, D., Oliveira, C., Wählby, C. (2018). Whole Slide Image Registration for the Study of Tumor Heterogeneity. In: , et al. Computational Pathology and Ophthalmic Medical Image Analysis. OMIA COMPAY 2018 2018. Lecture Notes in Computer Science(), vol 11039. Springer, Cham. https://doi.org/10.1007/978-3-030-00949-6_12

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  • DOI: https://doi.org/10.1007/978-3-030-00949-6_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-00948-9

  • Online ISBN: 978-3-030-00949-6

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