Advertisement

Assessment and Comparison of Colour Fidelity of Whole Slide Imaging Scanners

  • Norman ZerbeEmail author
  • Alexander Alekseychuk
  • Peter Hufnagl
Chapter
  • 92 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12090)

Abstract

The aim of this work is to develop a colourimetrically justified method for determination of colour fidelity of WSI scanners. We measure the absolute accuracy of colour reproduction and assess the scanner’s ability to resolve similar colours, which seems to be even more important for diagnostic and research tasks. Along this, some theoretical background is given which helps in understanding of common sources of failures in colour reproduction. The work was done in the framework of 2-nd and 3-rd International Scanner Contest. We hope this publication will be useful for scanner manufactures as well as for academical and clinical users.

Keywords

Whole slide imaging Quality assurance Color calibration Digital pathology Colour target 

References

  1. 1.
    Baxi, V.: Color Calibration on Whole Slide Imaging Scanners. Pathology Visions (2011)Google Scholar
  2. 2.
    Bautista, P.A., Hashimoto, N., Yagi, Y.: Color standardization in whole slide imaging using a color calibration slide. J. Pathol. Inform. 5(1), 4 (2014).  https://doi.org/10.4103/2153-3539.126153CrossRefGoogle Scholar
  3. 3.
    The International Scanner Contest. http://scanner-contest.charite.de/en/
  4. 4.
    Shrestha, P., Hulsken, B.: Color accuracy and reproducibility in whole slide imaging scanners. J. Med. Imaging (Bellingham) 1(2), 027501 (2014).  https://doi.org/10.1117/1.JMI.1.2.027501CrossRefGoogle Scholar
  5. 5.
    Clarke, E.L., Treanor, D.: Colour in digital pathology: a review. Histopathology 70(2), 153–163 (2017).  https://doi.org/10.1111/his.13079CrossRefGoogle Scholar
  6. 6.
    Stockman, A., MacLeod, D.I.A., Johnson, N.E.: Spectral sensitivities of the human cones. J. Opt. Soc. Am. A 10(12), 2491–2520 (1993)CrossRefGoogle Scholar
  7. 7.
    Fairchild, M.D.: Color Appearance Models. The Wiley-IS & T Series in Imaging Science and Technology. Wiley, New York (2005)Google Scholar
  8. 8.
    Poynton, C.: The rehabilitation of gamma. In: Human Vision and Electronic Imaging III, pp. 232–249 (1998). http://www.poynton.com/PDFs/Rehabilitation_of_gamma.pdf
  9. 9.
    Brill, M.H., Michael, H.: The relation between the color of the illuminant and the color of the illuminated object. Color Res. Appl. 20, 70–76 (1995)CrossRefGoogle Scholar
  10. 10.
    Sharma, G., Wu, W., Dalal, E.N.: The CIEDE2000 color-difference formula: implementation notes, supplementary test data, and mathematical observations. Color Res. Appl. 30, 21–30 (2005)CrossRefGoogle Scholar
  11. 11.
    Haroske, G., Zwönitzer, R., Hufnagl, P.: Leitfaden “Digitale Pathologie in der Diagnostik”. Pathologe 39(3), 216–221 (2018).  https://doi.org/10.1007/s00292-018-0433-yCrossRefGoogle Scholar
  12. 12.
    Hufnagl, P., Lohmann, S., Schlüns, K., Zerbe, N.: Implementation of the “Digital Pathology in Diagnostics” guideline: support systems and their functionality. Pathologe 39(3), 222–227 (2018).  https://doi.org/10.1007/s00292-018-0436-8CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Norman Zerbe
    • 1
    Email author
  • Alexander Alekseychuk
    • 2
  • Peter Hufnagl
    • 1
    • 3
  1. 1.Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Institute of PathologyBerlinGermany
  2. 2.Vision in X industrial imaging GmbHBerlinGermany
  3. 3.Center for Biomedical Image and Information Processing (CBMI)HTW University of Applied Sciences BerlinBerlinGermany

Personalised recommendations