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Segmentation Method of Time-Lapse Microscopy Images with the Focus on Biocompatibility Assessment

Published online by Cambridge University Press:  02 May 2016

Jindřich Soukup ,
Petr Císař  and
Filip Šroubek
Jindřich Soukup*
Affiliation:
Institute of Complex Systems FFPW, CENAKVA, University of South Bohemia, Zámek 136, CZ-373 33 Nové Hrady, Czech Republic Faculty of Mathematics and Physics, Charles University in Prague, Ke Karlovu 3, CZ-121 16 Prague 2, Czech Republic Department of Image Processing, Institute of Information Theory and Automation of the ASCR, Pod vodárenskou věží 4, CZ-182 08 Prague 8, Czech Republic
Petr Císař
Affiliation:
Institute of Complex Systems FFPW, CENAKVA, University of South Bohemia, Zámek 136, CZ-373 33 Nové Hrady, Czech Republic
Filip Šroubek
Affiliation:
Department of Image Processing, Institute of Information Theory and Automation of the ASCR, Pod vodárenskou věží 4, CZ-182 08 Prague 8, Czech Republic
*
*Corresponding author.jindra@matfyz.cz
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Abstract

Biocompatibility testing of new materials is often performed in vitro by measuring the growth rate of mammalian cancer cells in time-lapse images acquired by phase contrast microscopes. The growth rate is measured by tracking cell coverage, which requires an accurate automatic segmentation method. However, cancer cells have irregular shapes that change over time, the mottled background pattern is partially visible through the cells and the images contain artifacts such as halos. We developed a novel algorithm for cell segmentation that copes with the mentioned challenges. It is based on temporal differences of consecutive images and a combination of thresholding, blurring, and morphological operations. We tested the algorithm on images of four cell types acquired by two different microscopes, evaluated the precision of segmentation against manual segmentation performed by a human operator, and finally provided comparison with other freely available methods. We propose a new, fully automated method for measuring the cell growth rate based on fitting a coverage curve with the Verhulst population model. The algorithm is fast and shows accuracy comparable with manual segmentation. Most notably it can correctly separate live from dead cells.

Keywords

phase contrast microscopysegmentationbiocompatibility assessmenttime-lapsecytotoxicity testing
Type
Technique and Instrumentation Development
Information
Microscopy and Microanalysis , Volume 22 , Issue 3 , June 2016 , pp. 497 - 506
Copyright
Copyright © Microscopy Society of America 2016

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References

Ambühl, M.E., Brepsant, C., Meister, J.-J., Verkhovsky, A.B. & Sbalzarini, I.F. (2012). High resolution cell outline segmentation and tracking from phase-contrast microscopy images. J Microsc 245(2), 161–170.Google Scholar
Bradhurst, C.J., Boles, W. & Yin Xiao, Y.X. (2008). Segmentation of bone marrow stromal cells in phase contrast microscopy images. In 2008 23rd International Conference Image and Vision Computing New Zealand, pp. 1--6. IEEE, 2008. Available at http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=4762144.Google Scholar
Červinka, M. & Púža, V. (1990). In vitro toxicity testing of implantation materials used in medicine: Effects on cell morphology, cell proliferation and DNA synthesis. Toxicol In Vitro 4(4), 711–716.Google Scholar
Chen, C.-H. & Chen, S.C. (1981). Cell growth factor activity: New quantitative method in cell culture assay. Exp Cell Res 136(1), 43–51.Google Scholar
Ersoy, I., Bunyak, F., Mackey, M.A. & Palaniappan, K. (2008). Cell segmentation using hessian-based detection and contour evolution with directional derivatives. In ICIP 2008. 15th IEEE International Conference on Image Processing, vol. 2008, pp. 1804–1807, IEEE. San Diego, CA, USA, 12 October--15 October, 2008. Available at http://ieeexplore.ieee.org/xpls/abs/_all.jsp?arnumber=4712127.Google Scholar
Huth, J., Buchholz, M., Kraus, J.M., Mølhave, K., Gradinaru, C., v Wichert, G., Gress, T.M., Neumann, H. & Kestler, H.A. (2011). TimeLapseAnalyzer: Multi-target analysis for live-cell imaging and time-lapse microscopy. Comput Methods Programs Biomed 104(2), 227–234.Google Scholar
Huth, J., Buchholz, M., Kraus, J.M., Schmucker, M., Von Wichert, G., Krndija, D., Seufferlein, T., Gress, T. & 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(1), 24.Google Scholar
ISO 10993-5 (2009). Biological evaluation of medical devices. Tests for in vitro cytotoxicity.Google Scholar
Juneau, P.-M., Garnier, A. & Duchesne, C. (2013). Selection and tuning of a fast and simple phase-contrast microscopy image segmentation algorithm for measuring myoblast growth kinetics in an automated manner. Microsc Microanal 19, 855–866.Google Scholar
Kanade, T., Yin, Z., Bise, R., Huh, S., Eom, S., Sandbothe, M.F. & Chen, M. (2011). Cell image analysis: Algorithms, system and applications. In 2011 IEEE Workshop on Applications of Computer Vision (WACV), January, pp. 374–381. Kona, HI, USA, 5 January--7 January, 2011. Available at http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5711528.Google Scholar
Li, F., Zhou, X., Zhao, H. & Wong, S.T.C. (2009). Cell segmentation using front vector flow guided active contours. Med Image Comput Comput Assist Interv 12(Pt 2), 609–616.Google Scholar
Li, K., Miller, E.D., Weiss, L.E., Campbell, P.G. & Kanade, T. (2006). Online tracking of migrating and proliferating cells imaged with phase-contrast microscopy. In CVPRW’06. Conference on Computer Vision and Pattern Recognition Workshop, pp. 65–72, IEEE.Google Scholar
Masters, J.R. (2002). HeLa cells 50 years on: The good, the bad and the ugly. Nat Rev Cancer 2(4), 315–319.Google Scholar
Otsu, N. (1975). A threshold selection method from gray-level histograms. Automatica 20(1), 62–66.Google Scholar
Pan, J., Kanade, T. & Chen, M. (2009). Learning to detect different types of cells under phase contrast microscopy. In The forth international workshop on Microscopic Image Analysis with Applications in Biology, MIAAB, pp. 1–8.Google Scholar
Pan, J., Kanade, T. & Chen, M. (2010). Heterogeneous conditional random field: Realizing joint detection and segmentation of cell regions in microscopic images. In 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June, pp. 2940–2947, IEEE. San Francisco, CA, USA, 15 June--17 June, 2010. Available at http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5540037.Google Scholar
Peng, H., Zhou, X., Li, F., Xia, X. & Wong, S.T.C. (2009). Integrating multiscale blob/curvilinear detector techniques and multi-level sets for automated segmentation of stem cell images. In ISBI’09. IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Boston, MA, USA, 28 June--1 July, 2009. pp. 1362–1365.Google Scholar
Seroussi, I, Veikherman, D, Ofer, N, Yehudai-Resheff, S & Keren, K. (2012). Segmentation and tracking of live cells in phase-contrast images using directional gradient vector flow for snakes. J Microsc 247(2), 137–146.Google Scholar
Soukup, J., Cisar, P & Sroubek, F. (2013). Segmentation of time-lapse images with focus on microscopy images of cells. In 17th International Conference on Image Analysis and Processing – ICIAP 2013, vol. 8157, Part II of LNCS, Alfredo Petrosino (Ed.), pp. 71–80. Berlin, Heidelberg: Springer-Verlag.Google Scholar
Verhulst, P.F. (1838). Notice sur la loi que la population suit dans son accroissement. Corr Math et Phys 10(10), 113–121.Google Scholar
Yin, Z., Bise, R., Chen, M. & Kanade, T. (2010). Cell segmentation in microscopy imagery using a bag of local Bayesian classifiers. In 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Beijing, China, 20 September--24 September, 2010. pp. 125–128, IEEE.Google Scholar
Yin, Z. & Kanade, T. (2011). Restoring artifact-free microscopy image sequences. In 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Chicago, IL, USA, 30 March--2 April, 2011. pp. 909–913, IEEE.Google Scholar
Yin, Z., Kanade, T. & Chen, M. (2012). Understanding the phase contrast optics to restore artifact-free microscopy images for segmentation. Med Image Anal 16(5), 1047–1062.Google Scholar
Zimmer, C., Labruyère, E., Meas-Yedid, V., Guillén, N. & Olivo-Marin, J.-C. (2002). Segmentation and tracking of migrating cells in videomicroscopy with parametric active contours: A tool for cell-based drug testing. IEEE Trans Med Imaging 21(10), 1212–1221.Google Scholar