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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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