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Biomedical Engineering

, Volume 52, Issue 6, pp 387–390 | Cite as

Biotechnological Systems for Automated Microscopy of Cytology Specimens

  • A. V. SamorodovEmail author
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We present here a brief review of the history of the development of automated microscopy systems. Aspects of design methodology and results of studies in this direction conducted at the Bauman Moscow State Technical University are considered. We describe an approach to determining the size of the cohort of cells required for determining the quality of the work of image analysis algorithms and the properties of the distribution of cells on the surface of the specimen. The key directions in the development of automated microscopy are discussed.

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References

  1. 1.
    Fisher, C., “The new Quantimet 720,” The Microscope, 19, 1-20 (1971).Google Scholar
  2. 2.
    Preston, K. and Duff, M. J. B., Modern Cellular Automata: Theory and Applications, Plenum Press, New York, London (1984).CrossRefzbMATHGoogle Scholar
  3. 3.
    Bradbury, S., “Commercial image analyzers and the characterization of microscopical images,” J. Microsc., 131, No. 2, 203-210 (1983).CrossRefGoogle Scholar
  4. 4.
    Lur’e, O. B., Bykov, R. E., and Popechitelev, E. P., “Color as a criterion for automating determination of the blood leukocyte formula,” Trudy SZPI, No. 3, 24-28 (1968).Google Scholar
  5. 5.
    Bacus, J. W., Belanger, M. G., Aggarwal, R. K., and Trobaugh, F. E., “Image processing for automated erythrocyte classification,” J. Histochem. Cytochem., 24, 195-201 (1976).CrossRefGoogle Scholar
  6. 6.
    Green, J. E., “A practical application of computer pattern recognition research: The Abbott ADC-500 differential classifier,” J. Histochem. Cytochem., 27, No. 1, 160-173 (1979).Google Scholar
  7. 7.
    Wielders, J. P. M., Beunis, M. H., and van Wersch, J. W. J., “A comparison of the screening ability of two automated leukocyte differential counters,” J. Clin. Chem. Clin. Biochem., 24, 471-480 (1986).Google Scholar
  8. 8.
    Farahani, N., Parwani, A., and Pantanowitz, L., “Whole slide imaging in pathology: Advantages, limitations, and emerging perspectives,” Pathol. Lab. Med. Int., 7, 23-33 (2015).Google Scholar
  9. 9.
    Pantanowitz, L. et al., “Review of the current state of whole slide imaging in pathology,” J. Pathol. Inform., 2, No. 1, 1-36 (2011).CrossRefGoogle Scholar
  10. 10.
    Boyce, B. F., “An update on the validation of whole slide imaging systems following FDA approval of a system for a routine pathology diagnostic service in the United States,” Biotech. Histochem., 92, No. 6, 381-389 (2017).CrossRefGoogle Scholar
  11. 11.
    Ghaznavi, F. et al., “Digital imaging in pathology: Whole-slide imaging and beyond,” Annu. Rev. Pathol. Mech. Dis., 8, 331-359(2013).CrossRefGoogle Scholar
  12. 12.
    Kitchener, H. C. et al., “Automation-assisted versus manual reading of cervical cytology (MAVARIC): A randomized controlled trial,” Lancet Oncol, 12, No. 1, 56-64 (2011).CrossRefGoogle Scholar
  13. 13.
    Feldman, M. D., “Whole slide imaging in pathology: What is holding us back?” Pathol. Lab. Med. Int., 7, 35-38 (2015).CrossRefGoogle Scholar
  14. 14.
    Buttner, J., “Laboratory findings: Structure, validity and significance for medical cognitive processes,” Eur. J. Clin. Chem. Clin. Biochem., 29, 507-519 (1991).Google Scholar
  15. 15.
    Spiridonov, I. N., Apollonova, I. A., and Safonova, L. P., “Basic principles of creating laser analyzers for medical images with complex structure,” Konversiya, No. 10, 55-57 (1997).Google Scholar
  16. 16.
    Samorodov, A. V., “Building intelligent systems for the analysis of microscopic images in medicine and biology,” Patt. Recog. Image. Anal., 23, No. 4, 508-511 (2013).CrossRefGoogle Scholar
  17. 17.
    Safonova, L. P., Samorodov, A. V., and Spiridonov, I. N., “Quantitative estimation of poikilocytosis by the coherent optical method,” Proc. SPIE, 3923, 170-174 (2000).CrossRefGoogle Scholar
  18. 18.
    Samorodov, A. V., Kosorukov, A. E., Samorodova, O. A., Dobrolyubova, D. A., and Voinova, N. A., “Automated optical microscope using a standard object table,” Inzh. Vest. (an electronic scientific-technical journal), No. 5, 508-515 (2016); http://engsi.ru/doc/842850.html
  19. 19.
    Artyukhova, O. A. and Samorodov, A. V., “A comparative study of the sharpness characteristics of microscopic images of biomedical specimens,” Med. Tekh., No. 1, 15-22 (2011).Google Scholar
  20. 20.
    Agapova, E. A., Dobrolyubova, D. A., and Samorodov, A. V., “A telemedicine system for remote online consultations on the microscopy of biomedical specimens,” Biotekhnosfera, No. 6, 2-7 (2016).Google Scholar
  21. 21.
    Samorodov, A. V., Frolova, A. V., Semikina, E. L., and Spiridonov, I. N., “Automation of morphometric analysis of blood cells in smears,” Klin. Lab. Diagnost., No. 9, 43b-43 (2004).Google Scholar
  22. 22.
    Dobrolyubova, D. A., Kravtsova, T. A., Samorodova, O. A., Samorodov, A. V., Slavnova, E. N., and Volchenko, N. N., “Automatic image analysis algorithm for quantitative assessment of breast cancer estrogen receptor status in immunocytochemistry,” Patt. Recog. Image. Anal., 26, No. 3, 552-557 (2016).CrossRefGoogle Scholar
  23. 23.
    Parpulov, D., Samorodov, A., Makhov, D., Slavnova, E., Volchenko, N., and Iglovikov, V., “Convolutional neural network application for cell segmentation in immunocytochemical study,” in: Proc. 2018 Ural Symposium on Biomedical Engineering, Radioelectronics and Information Technology (USBEREIT 2018), (2018), pp. 87-90.Google Scholar
  24. 24.
    Volchenko, N. N., Spiridonov, I. N., Slavnova, E. N., Samorodov, A. V., Polyanskaya, M. G., and Borisova, O. V., “Computer analysis of the texture of images of nuclei in determining the level of differentiation of invasive ductal breast cancer,” Ross. Onkol. Zh., No. 1, 13-18 (2008).Google Scholar
  25. 25.
    Volchenko, N. N., Mel’nikova, V. Yu., Spiridonov, I. N., Samorodov, A. V., and Slavnova, E. N., “The significance of argentophilic proteins in the area of nucleolar organizers in the cytological diagnosis of renal cancer,” Ross. Onkol. Zh., No. 5, 37-39 (2007).Google Scholar
  26. 26.
    Castleman, K. R., Price, K. H., and White, B. S., “Effect of random abnormal cell proportion on specimen classifier performance,” Cytometry, 14, 1-8 (1993).CrossRefGoogle Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Bauman Moscow State Technical UniversityMoscowRussia

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