Hostname: page-component-586b7cd67f-t8hqh Total loading time: 0 Render date: 2024-11-28T01:01:22.152Z Has data issue: false hasContentIssue false
  • English
  • Français

Selection and Tuning of a Fast and Simple Phase-Contrast Microscopy Image Segmentation Algorithm for Measuring Myoblast Growth Kinetics in an Automated Manner

Published online by Cambridge University Press:  30 May 2013

Pierre-Marc Juneau ,
Alain Garnier  and
Carl Duchesne
Pierre-Marc Juneau
Affiliation:
Department of Chemical Engineering, Université Laval, Pavillon Adrien-Pouliot, 1065 ave. de la Médecine, Québec City, Québec G1V 0A6, Canada
Alain Garnier
Affiliation:
Department of Chemical Engineering, Université Laval, Pavillon Adrien-Pouliot, 1065 ave. de la Médecine, Québec City, Québec G1V 0A6, Canada
Carl Duchesne*
Affiliation:
Department of Chemical Engineering, Université Laval, Pavillon Adrien-Pouliot, 1065 ave. de la Médecine, Québec City, Québec G1V 0A6, Canada
*
*Corresponding author. E-mail: [email protected]
Get access

Abstract

Acquiring and processing phase-contrast microscopy images in wide-field long-term live-cell imaging and high-throughput screening applications is still a challenge as the methodology and algorithms used must be fast, simple to use and tune, and as minimally intrusive as possible. In this paper, we developed a simple and fast algorithm to compute the cell-covered surface (degree of confluence) in phase-contrast microscopy images. This segmentation algorithm is based on a range filter of a specified size, a minimum range threshold, and a minimum object size threshold. These parameters were adjusted in order to maximize the F-measure function on a calibration set of 200 hand-segmented images, and its performance was compared with other algorithms proposed in the literature. A set of one million images from 37 myoblast cell cultures under different conditions were processed to obtain their cell-covered surface against time. The data were used to fit exponential and logistic models, and the analysis showed a linear relationship between the kinetic parameters and passage number and highlighted the effect of culture medium quality on cell growth kinetics. This algorithm could be used for real-time monitoring of cell cultures and for high-throughput screening experiments upon adequate tuning.

Keywords

phase-contrast microscopylive-cell imagingsegmentationcell-covered surfacedegree of confluencemyoblastsgrowth kineticspassage numberalgorithm tuningalgorithm selection
Type
Biological Applications
Information
Microscopy and Microanalysis , Volume 19 , Issue 4 , August 2013 , pp. 855 - 866
Copyright
Copyright © Microscopy Society of America 2013 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Adams, R. & Bischof, L. (1994). Seeded region growing. IEEE Trans Pattern Anal Mach Intell 16(6), 641–647.CrossRefGoogle Scholar
Al-Muhairi, H., Fleury, M. & Clark, A.F. (2011). Parameter-orientated segmentation algorithm evaluation. 2011 IEEE GCC Conference and Exhibition (GCC), Dubai, United Arab Emirates, February 19–22, 2011. CrossRefGoogle Scholar
Athelogou, M., Eblenkamp, M., Schmidt, G., Novotny, F., Wintermantel, E. & Binnig, G. (2010). Single-cell detection and classification in phase contrast images. 10th IEEE International Conference on Information Technology and Applications in Biomedicine (ITAB), Corfu, Greece, November 3–5, 2010. Google Scholar
Boudreault, P., Tremblay, J.P., Pépin, M.-F. & Garnier, A. (2001). Scale-up of a myoblast culture process. J Biotechnol 91(1), 63–74.Google ScholarPubMed
Bradbury, L. & Wan, J.W.L. (2010). A spectral k-means approach to bright-field cell image segmentation. Annual International Conference of the IEEE on Engineering in Medicine and Biology Society (EMBC), Buenos Aires, Argentina, August 31–September 4, 2010. CrossRefGoogle Scholar
Bradhurst, C.J., Boles, W. & Yin, X (2008). Segmentation of bone marrow stromal cells in phase contrast microscopy images. 23rd International Conference on Image and Vision Computing New Zealand (IVCNZ), Christchurch, New Zealand, November 26–28, 2008. CrossRefGoogle Scholar
Chowdhury, M.H. & Little, W.D. (1995). Image thresholding techniques. Proceedings, IEEE Pacific Rim Conference on Communications, Computers, and Signal Processing, Victoria, BC, Canada, May 17–19, 1995. CrossRefGoogle Scholar
Cox, G. (2007). Optical Imaging Techniques in Cell Biology. Boca Raton, FL: CRC/Taylor & Francis.Google Scholar
Deasy, B.M., Jankowski, R.J., Payne, T.R., Cao, B., Goff, J.P., Greenberger, J.S. & Huard, J. (2003). Modeling stem cell population growth: Incorporating terms for proliferative heterogeneity. Stem Cells 21(5), 536–545.CrossRefGoogle ScholarPubMed
Debeir, O., Adanja, I., Warzee, N., Van Ham, P. & Decaestecker, C. (2008a). Phase contrast image segmentation by weak watershed transform assembly. 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Paris, France, May 14–17, 2008. CrossRefGoogle Scholar
Debeir, O., Megalizzi, V., Warzee, N., Kiss, R. & Decaestecker, C. (2008b). Videomicroscopic extraction of specific information on cell proliferation and migration in vitro . Exp Cell Res 314(16), 2985–2998.CrossRefGoogle ScholarPubMed
Dewan, M.A.A., Ahmad, M.O. & Swamy, M.N.S. (2011). Tracking biological cells in time-lapse microscopy: An adaptive technique combining motion and topological features. IEEE Trans Biomed Eng 58(6), 1637–1647.CrossRefGoogle ScholarPubMed
Freshney, R.I. (2010). Culture of Animal Cells: A Manual of Basic Technique and Specialized Applications. Hoboken, NJ: John Wiley & Sons.CrossRefGoogle Scholar
Frigault, M.M., Lacoste, J., Swift, J.L. & Brown, C.M. (2009). Live-cell microscopy—Tips and tools. J Cell Sci 122(6), 753–767.CrossRefGoogle ScholarPubMed
Goldman, R.D. & Spector, D.L. (2005). Live Cell Imaging: A Laboratory Manual. Cold Spring Harbor, NY: Cold Spring Harbor Laboratory Press.Google Scholar
Gonzalez, R.C. & Woods, R.E. (2008). Digital Image Processing. Upper Saddle River, NJ: Pearson/Prentice Hall.Google Scholar
Ham, R.G. & Blau, H.M. (1988). Improved media for normal human muscle satellite cells: Serum-free clonal growth and enhanced growth with low serum. In Vitro Cell Dev Biol 24(8), 833–844.CrossRefGoogle ScholarPubMed
He, W.J., Wang, X.X., Metaxas, D., Mathew, R. & White, E. (2007). Cell segmentation for division rate estimation in computerized video time-lapse microscopy. Conference on Multimodal Biomedical Imaging II, San Jose, CA, January 20–23, 2007. CrossRefGoogle Scholar
Huth, J., Buchholz, M., Kraus, J.M., Schmucker, M., von Wichert, G., Krndija, D., Seufferlein, T., Gress, T.M. & Kestler, H.A. (2010). Significantly improved precision of cell migration analysis in time-lapse video microscopy through use of a fully automated tracking system. BMC Cell Biol 11, 11–24.CrossRefGoogle ScholarPubMed
Kang, L., Miller, E.D., Weiss, L.E., Campbell, P.G. & Kanade, T. (2006). Online tracking of migrating and proliferating cells imaged with phase-contrast microscopy. Conference on Computer Vision and Pattern Recognition Workshop (CVPRW), New York, June 17–22, 2006. CrossRefGoogle Scholar
Kapur, J.N., Sahoo, P.K. & Wong, A.K.C. (1985). A new method for gray-level picture thresholding using the entropy of the histogram. Comput Vision Graph Image Process 29(3), 273–285.CrossRefGoogle Scholar
Kass, M., Witkin, A. & Terzopoulos, D. (1988). Snakes: Active contour models. Int J Comput Vision 1(4), 321–331.Google Scholar
Kenong, W., Gauthier, D. & Levine, M.D. (1995). Live cell image segmentation. IEEE Trans Biomed Eng 42(1), 1–12.CrossRefGoogle Scholar
Ker, D.F.E., Weiss, L.E., Junkers, S.N., Chen, M., Yin, Z.Z., Sandbothe, M.F., Huh, S.I., Eom, S., Bise, R., Osuna-Highley, E., Kanade, T. & Campbell, P.G. (2011). An engineered approach to stem cell culture: Automating the decision process for real-time adaptive subculture of stem cells. Plos One 6(11), e27672. doi:10.1371/journal.pone.0027672. CrossRefGoogle ScholarPubMed
Kittler, J. & Illingworth, J. (1986). Minimum error thresholding. Pattern Recogn 19(1), 41–47.CrossRefGoogle Scholar
Kreyszig, E. (2000). Advanced Engineering Mathematics, Maple Computer Guide. New York: John Wiley & Sons.Google Scholar
Leysi-Derilou, Y., Duchesne, C., Garnier, A. & Pineault, N. (2012). Single-cell level analysis of megakaryocyte growth and development. Differentiation 83(4), 200–209.CrossRefGoogle ScholarPubMed
Liu, A., Li, K. & Kanade, T. (2011). A semi-Markov model for mitosis segmentation in time-lapse phase contrast microscopy image sequences of stem cell populations. IEEE Trans Med Imaging 31, 359–369.Google ScholarPubMed
Mõlder, A., Sebesta, M., Gustafsson, M., Gisselson, L., Wingren, A.G. & Alm, K. (2008). Non-invasive, label-free cell counting and quantitative analysis of adherent cells using digital holography. J Microsc 232(2), 240–247.CrossRefGoogle ScholarPubMed
Moogk, D., Stewart, M., Gamble, D., Bhatia, M. & Jervis, E. (2010). Human ESC colony formation is dependent on interplay between self-renewing hESCs and unique precursors responsible for niche generation. Cytometry A 77A(4), 321–327.CrossRefGoogle Scholar
Nattkemper, T.W., Twellmann, T., Ritter, H. & Schubert, W. (2003). Human vs. machine: Evaluation of fluorescence micrographs. Comput Biol Med 33(1), 31–43.CrossRefGoogle ScholarPubMed
Nielsen, L.K., Smyth, G.K. & Greenfield, P.F. (1991). Hemacytometer cell count distributions—Implications of non-Poisson behavior. Biotechnol Prog 7(6), 560–563.CrossRefGoogle Scholar
Orikawa, J. & Tanaka, T. (2010). Cell segmentation from phase-contrast images using hybrid watershed and region growing algorithm for genomic drug discovery. Proceedings of SICE Annual Conference 2010, Tapei, Taiwan, August 18–21, 2010. Google Scholar
Otsu, N. (1979). A threshold selection method from gray-level histograms. IEEE T Syst Man Cyb 9(1), 62–66.CrossRefGoogle Scholar
Padfield, D., Rittscher, J. & Roysam, B. (2011). Coupled minimum-cost flow cell tracking for high-throughput quantitative analysis. Med Image Anal 15(4), 650–668.CrossRefGoogle ScholarPubMed
Paduano, V., Sepe, L., Cantarella, C., Sansone, C., Paolella, G. & Ceccarelli, M. (2010). Time-lapse phase-contrast microscopy fibroblast automated tracking. 2010 IEEE International Conference on Imaging Systems and Techniques (IST), Thessaloniki, Greece, July 1–2, 2010. CrossRefGoogle Scholar
Parent, V.A., Garnier, A. & Tremblay, J.P. (2009). Culture medium for myoblasts, precursors thereof and derivatives thereof. Canada. Patent PCT/CA2009/001342. Google Scholar
Rapoport, D.H., Becker, T., Mamlouk, A.M., Schicktanz, S. & Kruse, C. (2011). A novel validation algorithm allows for automated cell tracking and the extraction of biologically meaningful parameters. PLos One 6(11), e27315. doi:10.1371/journal.pone.0027315. Google ScholarPubMed
Ridler, T.W. & Calvard, S. (1978). Picture thresholding using an iterative selection method. IEEE T Syst Man Cyb 8(8), 630–632.Google Scholar
Rimon, N. & Schuldiner, M. (2011). Getting the whole picture: Combining throughput with content in microscopy. J Cell Sci 124(22), 3743–3751.CrossRefGoogle ScholarPubMed
Russell, C., Metaxas, D., Restif, C. & Torr, P. (2007). Using the Pn Potts model with learning methods to segment live cell images. IEEE 11th International Conference on Computer Vision (ICCV), Rio de Janeiro, Brazil, October 14–21, 2007. CrossRefGoogle Scholar
Sethian, J.A. & Adalsteinsson, D. (1997). An overview of level set methods for etching, deposition, and lithography development. IEEE T Semiconduct M 10(1), 167–184.CrossRefGoogle Scholar
Shuler, M.L. & Kargi, F. (1992). Bioprocess Engineering: Basic Concepts. Englewood Cliffs, NJ: Prentice Hall PTR.Google Scholar
Tabatabai, M., Williams, D. & Bursac, Z. (2005). Hyperbolastic growth models: Theory and application. Theor Biol Med Model 2(1), 14.CrossRefGoogle Scholar
Topman, G., Sharabani-Yosef, O. & Gefen, A. (2011). A method for quick, low-cost automated confluency measurements. Microsc Microanal 17(6), 915–922.CrossRefGoogle ScholarPubMed
Vincent, L. & Soille, P. (1991). Watersheds in digital spaces: An efficient algorithm based on immersion simulations. IEEE Trans Pattern Anal Mach Intell 13(6), 583–598.CrossRefGoogle Scholar
Vogels, M., Zoeckler, R., Stasiw, D. & Cerny, L. (1975). P. F. Verhulst's “notice sur la loi que la populations suit dans son accroissement” from correspondence mathematique et physique. Ghent, vol. X, 1838. J Biol Phys 3(4), 183–192.CrossRefGoogle Scholar
Walck, C. (1996). Hand-Book on Statistical Distributions for Experimentalists. Stockholm, Sweden: Universitet Stockholms.Google Scholar
Yin, Z.Z., Li, K., Kanade, T. & Chen, M. (2010). Understanding the optics to aid microscopy image segmentation. In Medical Image Computing and Computer-Assisted Intervention—Miccai 2010, Pt I, Jiang, T., Navab, N., Pluim, J.P.W. & Viegever, M.A. (Eds.), pp. 209–217. Berlin: Springer-Verlag Berlin.Google Scholar