Classification of Polyethylene Particles and the Local CD3+ Lymphocytosis in Histological Slices

  • Lara-Maria Steffes
  • Marc Aubreville
  • Stefan Sesselmann
  • Veit Krenn
  • Andreas Maier
Conference paper
Part of the Informatik aktuell book series (INFORMAT)


In 2014, about 400.000 endoprosthetic operations were performed in Germany [1]. Unfortunately, the lifespan is limited and already after 10 years 5 percent of the patients have primary complaints [2]. All the more important it is to clarify the causes for this failure. One main cause is an immune response to abrasion particles of the implant, an effect which is assumed to be correlated with occurrence and count of CD3+ immune/inflammatory cells [3]. For the further analysis of this effect, computer-aided classification and image analysis methods provide a high value for the medical research. Aim of this work was the development of an threshold-based algorithm for the segmentation of polyethylene abrasion particles and the CD3+ immune/inflammatory response of histological slice images.


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  1. 1.
    Liebs TR. EPRD-Jahresbericht 2015. EPRD Endoprothesenregister Deutschland; 2016.Google Scholar
  2. 2.
    Hopf F. Materialabhängige CD3-Response in der SLIM bei dysfunktionalen Gelenkendoprothesen. Freie Universität Berlin; 2016.Google Scholar
  3. 3.
    Hopf F, Thomas P, Sesselmann S, et al. CD3+ lymphocytosis in the peri-implant membrane of 222 loosened joint endoprostheses depends on the tribological pairing. Acta Orthopaed. 2017; p. 1–7.Google Scholar
  4. 4.
    Krenn V, Hopf F, Thomas P, et al. Supramakropartikuläres Polyethylen bei Entzündungen periprothetischer Membranen. Der Orthopäde. 2016;45(3):256–265.Google Scholar
  5. 5.
    Magee D, Treanor D, Crellin D, et al. Colour normalisation in digital histopathology images. In: Proc MICCAI Workshop on Optical Tissue Image Analysis in Microscopy, Histopathology and Endoscopy. vol. 100. Daniel Elson; 2009.Google Scholar
  6. 6.
    Khan AM, Rajpoot N, Treanor D, et al. A nonlinear mapping approach to stain normalization in digital histopathology images using image-specific color deconvolution. IEEE Trans Biomed Eng. 2014;61(6):1729–1738.Google Scholar
  7. 7.
    Macenko M, Niethammer M, Marron J, et al. A method for normalizing histology slides for quantitative analysis. Proc ISBI. 2009; p. 1107–1110.Google Scholar
  8. 8.
    Reinhard E, Adhikhmin M, Gooch B, et al. Color transfer between images. IEEE Comp Graph App. 2001;21(5):34–41.Google Scholar
  9. 9.
    Raza SeA. Stain normalisation toolbox of warwick; 2015. [Accessed 2017-09-15].
  10. 10.
    Lowe DG. Distinctive image features from scale-invariant keypoints. Int J Comput Vis. 2004;60(2):91–110.Google Scholar
  11. 11.
    Aubreville M, Knipfer C, Oetter N, et al. Automatic classification of cancerous tissue in laserendomicroscopy images of the oral cavity using deep learning. Sci Report. 2017;7(1):41598–017.Google Scholar

Copyright information

© Springer-Verlag GmbH Deutschland 2018

Authors and Affiliations

  • Lara-Maria Steffes
    • 1
  • Marc Aubreville
    • 1
  • Stefan Sesselmann
    • 2
  • Veit Krenn
    • 3
  • Andreas Maier
    • 1
  1. 1.Pattern Recognition Lab, Computer SciencesFriedrich-Alexander-Universtität Erlangen-NürnbergErlangenDeutschland
  2. 2.Professor for Innovative Concepts and Technologies in HealthcareOTH Amberg-WeidenAmbergDeutschland
  3. 3.Center for Histopathology and Molecular PathologyTrierDeutschland

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