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Naji, M. |
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Motta, Antonella |
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Aletan, Dirar |
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Mohamed, Tarek |
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Ertürk, Emre |
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Taccardi, Nicola |
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Kononenko, Denys |
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Petrov, R. H. | Madrid |
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Alshaaer, Mazen | Brussels |
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Bih, L. |
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Casati, R. |
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Muller, Hermance |
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Kočí, Jan | Prague |
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Šuljagić, Marija |
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Kalteremidou, Kalliopi-Artemi | Brussels |
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Azam, Siraj |
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Ospanova, Alyiya |
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Blanpain, Bart |
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Ali, M. A. |
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Popa, V. |
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Rančić, M. |
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Ollier, Nadège |
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Azevedo, Nuno Monteiro |
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Landes, Michael |
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Rignanese, Gian-Marco |
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Shelton, Richard
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (8/8 displayed)
- 2021Biofilm viability checkercitations
- 2021Novel chitosan-silica hybrid hydrogels for cell encapsulation and drug deliverycitations
- 2018Automated non-invasive cell counting in phase contrast microscopy with automated image analysis parameter selectioncitations
- 2015Automated optimisation of cell segmentation parameters in phase contrast using discrete mereotopology
- 2014Semi-automated cell counting in phase contrast images of epithelial monolayers
- 2010Oral Keratinocyte Responses to Nickel-based Dental Casting Alloys In Vitrocitations
- 2007Corrosion of nickel-based dental casting alloyscitations
- 2001The influence of mixing ratio on the toughening mechanisms of a hand-mixed zinc phosphate dental cementcitations
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article
Automated non-invasive cell counting in phase contrast microscopy with automated image analysis parameter selection
Abstract
Cell counting is commonly used to determine proliferation rates in cell cultures and for adherent cells it is often a ‘destructive’ process requiring disruption of the cell monolayer resulting in the inability to follow cell growth longitudinally. This process is time consuming and utilises significant resource. In this study a relatively inexpensive, rapid and widely applicable phase contrast microscopy based technique has been developed that emulates the contrast changes taking place when bright field microscope images of epithelial cell cultures are defocused. Processing of the resulting images produces an image that can be segmented using a global threshold; the number of cells is then deduced from the number of segmented regions and these cell counts can be used to generate growth curves. The parameters of this method were tuned using the discrete mereotopological relations between ground truth and processed images. Cell count accuracy was improved using linear discriminant analysis to identify spurious noise regions for removal.<br/>The proposed cell counting technique was validated by comparing the results with a manual count of cells in images, and subsequently applied to generate growth curves for oral keratinocyte cultures supplemented with a range of concentrations of foetal calf serum. The approach developed has broad applicability and utility for researchers with standard laboratory imaging equipment.