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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. ydu@clarkson.edu
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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 

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