Hostname: page-component-78c5997874-s2hrs Total loading time: 0 Render date: 2024-11-05T17:16:54.416Z Has data issue: false hasContentIssue false
  • English
  • Français

Segmentation and Quantitative Analysis of Apoptosis of Chinese Hamster Ovary Cells from Fluorescence Microscopy Images

Published online by Cambridge University Press:  03 April 2017

Yuncheng Du ,
Hector M. Budman  and
Thomas A. Duever
Yuncheng Du*
Affiliation:
Chemical Engineering, Clarkson University, 8 Clarkson Ave, Potsdam, NY 13699-5805, USA
Hector M. Budman
Affiliation:
Chemical Engineering, University of Waterloo, 200 University Ave, Waterloo, ON N2L 3G1, Canada
Thomas A. Duever
Affiliation:
Chemical Engineering, Ryerson University, 350 Victoria Street. Toronto, ON M5B 2K3, Canada
*
*Corresponding author. [email protected]
Get access

Abstract

Accurate and fast quantitative analysis of living cells from fluorescence microscopy images is useful for evaluating experimental outcomes and cell culture protocols. An algorithm is developed in this work to automatically segment and distinguish apoptotic cells from normal cells. The algorithm involves three steps consisting of two segmentation steps and a classification step. The segmentation steps are: (i) a coarsesegmentation, combining a range filter with a marching square method, is used as a prefiltering step to provide the approximate positions of cells within a two-dimensional matrix used to store cells’ images and the count of the number of cells for a given image; and (ii) a fine segmentation step using the Active Contours Without Edges method is applied to the boundaries of cells identified in the coarse segmentation step. Although this basic two-step approach provides accurate edges when the cells in a given image are sparsely distributed, the occurrence of clusters of cells in high cell density samples requires further processing. Hence, a novel algorithm for clusters is developed to identify the edges of cells within clusters and to approximate their morphological features. Based on the segmentation results, a support vector machine classifier that uses three morphological features: the mean value of pixel intensities in the cellular regions, the variance of pixel intensities in the vicinity of cell boundaries, and the lengths of the boundaries, is developed for distinguishing apoptotic cells from normal cells. The algorithm is shown to be efficient in terms of computational time, quantitative analysis, and differentiation accuracy, as compared with the use of the active contours method without the proposed preliminary coarse segmentation step.

Keywords

cell morphologyliving-cells imaginglevel set functionsupervised machine learningclusters of cells
Type
Biological Science Applications
Information
Microscopy and Microanalysis , Volume 23 , Issue 3 , June 2017 , pp. 569 - 583
Copyright
© Microscopy Society of America 2017 

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

Aurum, A. Wohlin, C. (2003). The fundamental nature of requirements engineering activities as a decision making process. Inform Softw Technol 45(14), 945–954.CrossRefGoogle Scholar
Burgers, C.J. (1998). A tutorial on support vector machines for pattern recognition. Data Min Knowl Disc 2(2), 121–167.CrossRefGoogle Scholar
Carpenter, A.E., Jones, T.R., Lamprecht, M.R., Clarke, C., Kang, H., Friman, O., Guertin, D.A., Chang, J.H., Lindquist, R.A., Moffat, J., Golland, P. Sabatini, D.M. (2006). CellProfiler: Image analysis software for identifying and quantifying cell phenotypes. Genome Biol 7(10), R100.CrossRefGoogle ScholarPubMed
Chan, T.F. Shen, J. (2005). Image Processing and Analysis: Variational, PDE, Wavelet, and Stochastic Methods. Philadelphia, PA: The Society for Industrial and Applied Mathematics.CrossRefGoogle Scholar
Chan, T.F. Vese, L.A. (2001). Active contours without edges. IEEE Trans Image Proceess 10(2), 266–277.CrossRefGoogle ScholarPubMed
Du, Y., Budman, H. Duever, T. (2016a). Classification of normal and apoptotic cells from fluorescence microscopy images using generalized polynomial chaos and level set function. Microsc Microanal 22(3), 475–486.CrossRefGoogle ScholarPubMed
Du, Y., Budman, H. Duever, T. (2016b). Segmentation and quantitative analysis of normal and apoptotic cells from fluorescence microscopy images. The 11th IFAC Symposium on Dynamics and Control of Process Systems, Including Biosystems, Trondheim, Norway.CrossRefGoogle Scholar
Getreuer, P. (2012). Chan-Vese segmentation. IPOL 2(1), 214–224.CrossRefGoogle Scholar
Gosselin, R., Rodrigue, D. Duchesne, C. (2011). A hyperspectral imaging sensor for online quality control of extruded polymer composite products. Comput Chem Eng 35, 296–306.CrossRefGoogle Scholar
Henry, C.M., Hollville, E. Martin, S.J. (2013). Measuring apoptosis by microscopy and flow cytometry. Methods 61(2), 90–97.CrossRefGoogle ScholarPubMed
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(4), 856–866.CrossRefGoogle Scholar
Juneau, P.-M., Garnier, A. Duchesne, C. (2017). Monitoring of adherent live cells morphology using the undecimated wavelet transform multivariate image analysis (UWT-MIA). Biotechnol Bioeng 114(1), 141–154.CrossRefGoogle ScholarPubMed
Li, C., Kao, C.-Y., Gore, J.C. Ding, Z. (2008). Minimization of region scalable fitting energy for image segmentation. IEEE Trans Image Process 17(10), 1940–1949.Google ScholarPubMed
Meijering, E. (2012). Cell segmentation: 50 years down the road. IEEE Signal Proc Mag 29(5), 140–145.CrossRefGoogle Scholar
Meshram, M., Naderi, S., McConkey, B., Budman, H., Scharer, J. Ingalls, B. (2011). Population based modeling of the progression of apoptosis in mammalian cell culture. Biotechnol Bioeng 109(5), 1193–1204.CrossRefGoogle ScholarPubMed
Osher, S. Sethian, J.A. (1988). Fronts propagating with curvature dependent speed: Algorithms based on Hamilton Jacobi formulation. J Comput Phys 79(1), 12–49.CrossRefGoogle Scholar
Rulter, M., Spearman, M. Braasch, K. (2014). Monitoring cell growth, viability and apoptosis. In Animal Cell Biotechnology, Method and Protocols, R. Portner (Ed.), pp. 169–192. Hamburg: Springer.Google Scholar
Schneider, C.A., Rasband, W.S. Eliceiri, K.W. (2012). NIH image to ImageJ: 25 years of image analysis. Nat Methods 9(1), 671–675.CrossRefGoogle ScholarPubMed
Taatjes, D.J., Sobel, B.E. Budd, R.C. (2008). Morphological and cytochemical determination of cell death by apoptosis. Histochem Cell Biol 129(1), 33–43.CrossRefGoogle ScholarPubMed
Theriault, D.H., Walker, M.L., Wong, J.Y. Betke, M. (2012). Cell morphology classification and clutter mitigation in phase contrast microscopy images using machine learning. Mach Vision Appl 23(4), 659–673.CrossRefGoogle Scholar
Veredas, F., Mesa, H. Morente, L. (2010). Binary tissue classification on wound images with neural networks and Baysian classifiers. IEEE Trans Med Imag 29(2), 410–427.CrossRefGoogle Scholar
Waters, J.C. (2009). Accuracy and precision in quantitative fluorescence microscopy. J Cell Biol 185(7), 1135–1148.CrossRefGoogle ScholarPubMed
Yin, Z., Bise, R., Chen, M. Kanade, T. (2010). Cell Segmentation in Microscopy Imagery Using a Bag of Local Bayesian Classifiers, pp. 125–128. Rotterdam, The Netherlands: IEEE.Google Scholar
Zhao, H., Chan, T., Merriman, B. Osher, S. (1996). A variational level set approach to multiphase motion. J Comput Phys 127(1), 179–195.CrossRefGoogle Scholar
Ziadi, A., Maldague, X., Saucier, L., Duchesne, C. Gosselin, R. (2012). Visible and near-infrared light transmission: A hybrid imaging method for non-destructive meat quality evaluation. Infrared Phys Technol 55(5), 412–420.CrossRefGoogle Scholar