Skip to main content
SpringerLink
Log in

Automatic segmentation of adherent biological cell boundaries and nuclei from brightfield microscopy images

  • Special Issue Paper
  • Published:
Machine Vision and Applications Aims and scope Submit manuscript

Abstract

The detection and segmentation of adherent eukaryotic cells from brightfield microscopy images represent challenging tasks in the image analysis field. This paper presents a free and open-source image analysis package which fully automates the tasks of cell detection, cell boundary segmentation, and nucleus segmentation in brightfield images. The package also performs image registration between brightfield and fluorescence images. The algorithms were evaluated on a variety of biological cell lines and compared against manual and fluorescence-based ground truths. When tested on HT1080 and HeLa cells, the cell detection step was able to correctly identify over 80% of cells, whilst the cell boundary segmentation step was able to segment over 75% of the cell body pixels, and the nucleus segmentation step was able to correctly identify nuclei in over 75% of the cells. The algorithms for cell detection and nucleus segmentation are novel to the field, whilst the cell boundary segmentation algorithm is contrast-invariant, which makes it more robust on these low-contrast images. Together, this suite of algorithms permit brightfield microscopy image processing without the need for additional fluorescence images. Finally our sephaCe application, which is available at http://www.sephace.com, provides a novel method for integrating these methods with any motorised microscope, thus facilitating the adoption of these techniques in biological research labs.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Ali, R.: Applications of microscopy image analysis and modelling in characterising the mechanisms of hypoxia-mediated drug resistance. Ph.D thesis, University of Oxford, Oxford (2009)

  2. Ali, R., Gooding, M., Christlieb, M., Brady, M.: Phase-based segmentation of cells from brightfield microscopy. In: Proceedings of the International Symposium Biomedical Imaging (ISBI), pp 57–60 (2007)

  3. Ali, R., Gooding, M., Christlieb, M., Brady, M.: Advanced phase-based segmentation of multiple cells from brightfield microscopy images. In: Proceedings of the International Symposium Biomedical Imaging (ISBI), pp 181–184 (2008)

  4. Ali, R., Szilagyi, T., Gooding, M., Christlieb, M., Brady, M.: On the use of lowpass filters for image processing with inverse laplacian models. J. Math. Imag. Vis. Under Rev. (2010)

  5. Barber, P.R., Locke, R.J., Pierce, G.P., Rothkamm, K., Vojnovic, B.: Gamma-H2AX focicounting: image processing and control software for high-content screening. In: Proceedings of SPIE, San Jose, CA,USA, pp 64, 411M–64, 411M–10 (2007)

  6. Boukerroui M., Noble D., Brady A.: On the choice of band-pass quadraturefilters. J. Math. Imag. Vis. 21, 53–80 (2004)

    Article  MathSciNet  Google Scholar 

  7. Bradbury, L.: Segmentation of bright-field cell images. Ph.D thesis, University of Waterloo, Ontario, Canada (2009)

  8. Carpenter A.E., Jones T.R., Lamprecht M.R., Clarke C., Kang I.H., Friman O., Guertin D.A., Chang J.H., Lindquist R.A., Moffat J., Golland P., Sabatini D.M.: CellProfiler: image analysis software for identifying and quantifying cell phenotypes. Genome Biol. 7(10), R100 (2006)

    Article  Google Scholar 

  9. Cates J., Lefohn A., Whitaker R.: GIST: an interactive, GPU-Based level set segmentation tool for 3D medical images. Med. Imag. Anal. 8, 217–231 (2004)

    Article  Google Scholar 

  10. Curl C., Harris T., Harris P., Allman B., Bellair C., Stewart A., Delbridge L.: Quantitative phase microscopy: a new tool for measurement of cell culture growth and confluency in situ. Eur. J. Physiol. 448, 462–468 (2004)

    Article  Google Scholar 

  11. Dice, L. (1945) Measures of the amount of ecologic association between species. Ecology 26:297–302

    Google Scholar 

  12. Felsberg M., Sommer G.: The monogenic signal. IEEE Trans. Sig. Proc. 49(12), 3136–3144 (2001)

    Article  MathSciNet  Google Scholar 

  13. Felsberg M., Kalkan S., Krüger N.: Continuous dimensionality characterization of image structures. Imag. Vis. Comput. 27(6), 628–636 (2009)

    Article  Google Scholar 

  14. Folkard M., Prise K.M., Grime G., Kirkby K., Vojnovic B.: The use of microbeamsto investigate radiation damage in living cells. Appl. Rad. Isot. 67(3), 436–439 (2009)

    Article  Google Scholar 

  15. Gooding M., Kennedy S., Noble J.: Volume segmentation and reconstruction from freehand 3D ultrasound data with application to ovarian follicle measurement. Ultrasound Med. Biol. 34(2), 183–195 (2008)

    Article  Google Scholar 

  16. Gordon A., Colman-Lerner A., Chin T., Benjamin K., Yu R., Brent R.: Single-cell quantification of molecules and rates using open-source microscope-based cytometry. Nat. Methods 4, 175–181 (2007)

    Article  Google Scholar 

  17. Korzynska A., Stronjy W., Hoppe A., Wertheim D., Hoser P.: Segmentation of microscope images of living cells. Pattern Anal. Appl. 10(4), 301–319 (2007)

    Article  MathSciNet  Google Scholar 

  18. Mellor M., Brady M.: Phase mutual information as a similarity measure for registration. Med. Image Anal. 9(4), 330–343 (2005)

    Article  Google Scholar 

  19. Nilsson B., Heyden A.: A fast algorithm for level set-like active contours. Pattern Rec. Lett. 24, 1331–1337 (2003)

    Article  MATH  Google Scholar 

  20. Otsu N.: A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 9, 62–66 (1979)

    Article  Google Scholar 

  21. Paganin D., Nugent K.: Noninterferometric phase imaging with partially coherent light. Phys. Rev. Lett. 80(12), 2586–2589 (1998)

    Article  Google Scholar 

  22. Paganin D., Barty A., McMahon P., Nugent K.: Quantitative phase-amplitude microscopy. III. The effects of noise. J. Microsc. 214, 51–61 (2004)

    Article  MathSciNet  Google Scholar 

  23. Rittscher J., Machiraju R., Wong S.T.C.: Microscopic Image Analysis for Life Science Applications, 1st edn. Artech House Publishers, USA (2008)

    Google Scholar 

  24. Rote G.: Computing the minimum Hausdorff distance between two point sets on a line under translation. Inf. Process. Lett. 38, 123–127 (1991)

    Article  MathSciNet  MATH  Google Scholar 

  25. Selinummi, J., Ruusuvuori, P., Podolsky, I., Ozinsky, A., Gold, E., Yli-Harja, O., Aderem, A., Shmulevich, I.: Bright field microscopy as an alternative to whole cell fluorescence in automated analysis of macrophage images 4(10):e7497 (2009). doi:10.1371/journal.pone.0007497

  26. Styner M., Brehbuhler C., Szekely G., Gerig G.: Parametric estimate of intensity homogeneities applied to MRI. IEEE Trans. Med. Imag. 19(3), 153–165 (2000)

    Article  Google Scholar 

  27. Teague M.: Deterministic phase retrieval: a green’s function solution. J. Opt. Soc. Am. 73, 1434–1441 (1983)

    Article  Google Scholar 

  28. Tscherepanow M., Zollner F., Kummert F.: Classification of segmented regions in brightfield microscope images. ICPR 06(3), 972–975 (2006)

    Google Scholar 

  29. Tscherepanow M., Nickels N., Kummert F.: Recognition of unstained live drosophila cells in microscope image, pp. 169–176. IMVIP, Los Alamitos (2007)

    Google Scholar 

  30. Veselov O., Polak W., Ugenskiene R., Lebed K., Lekki J., Stachura Z., Styczen J.: Development of the IFJ single ion hit facility for cell irradiation. Radiat. Prot. Dosim. 122, 316–319 (2006)

    Article  Google Scholar 

  31. Volkov V., Zhu Y., Graef M.D.: A new symmetrized solution for phase retrieval using the transport of intensity equation. Micron. 33(5), 411–416 (2002)

    Article  Google Scholar 

  32. Wang K., Chang A., Kale L., Dantzig A.: Parallelization of a level set method for simulating dendritic growth. J. Parallel Distrib. Comput. 11, 1379–1386 (2006)

    Article  Google Scholar 

  33. Wu K., Gauthier D., Levine M.: Live cell image segmentation. IEEE Trans. Biomed. Eng. 42(1), 1–12 (1995)

    Article  Google Scholar 

  34. Xu C., Prince J.: Snakes, shapes and gradient vector flow. IEEE Trans. Image. Proc. 7, 359–369 (1998)

    Article  MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Radiation Physics, Stanford University, 875 Blake Wilbur Drive, CC-G206, Stanford, CA, 94305, USA

    Rehan Ali

  2. Mirada Medical Ltd, Innovation House, Mill Street, Oxford, OX2 0JX, UK

    Mark Gooding

  3. Department of Engineering Science, FRS FREng FMedSci Wolfson Medical Vision Lab, University of Oxford, Parks Road, Oxford, OX1 3PJ, UK

    Tünde Szilágyi & Michael Brady

  4. Gray Institute for Radiation Oncology and Biology, University of Oxford, Old Road Campus Research Building, Oxford, OX3 7QD, UK

    Borivoj Vojnovic & Martin Christlieb

Authors
  1. Rehan Ali
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mark Gooding
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Tünde Szilágyi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Borivoj Vojnovic
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Martin Christlieb
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Michael Brady
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Rehan Ali.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Ali, R., Gooding, M., Szilágyi, T. et al. Automatic segmentation of adherent biological cell boundaries and nuclei from brightfield microscopy images. Machine Vision and Applications 23, 607–621 (2012). https://doi.org/10.1007/s00138-011-0337-9

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00138-011-0337-9

Keywords

Search

Navigation