Segmentation
Iatia's QPI generates quantitative phase data based on relative changes in the optical thickness of a sample. This measurement of changes in optical thickness (phase) provides a significantly improved contrast mechanism enabling highly accurate cell segmentation.
Brightfield
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QPI
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As seen in the example above of rat mast cells, the sample has almost no effect on the intensity or color distirbution in the image and is almost totally transparent. However, the cells and their internal structures have an optical thickness and do affect the phase of light. QPI measures the relative change in optical thickness of the cells and that quantitative data is displayed as a high contrast grayscale image.
By comparison, conventional phase contrast techniques, such as Differential Interference Contrast (DIC) or optical phase contrast, produce qualitative images. These qualitative images often result in poor cell segmentation as a result of the shadows or the edge halo effects evidenced in optical phase contrast visualizations of optical thickness.
Case study - monitoring cell growth
In the following example, researchers from the Department of Physiology and Pharmacology at the University of Melbourne, Australia, monitored the change in growth in human airway smooth muscle cells over a period of a week1.
Cell growth was monitored with optical phase contrast as well as the QPI derived phase image. Automated cell segmentation and area calculations to monitor growth were obtained using Media Cybernetics' ImagePro Plus™ image processing software. The QPI based cell growth measurements were also compared to an independent estimate of cell growth based on haemocytometer cell counts.
Brightfield
This brightfield image, shows that the sample has little effect on the intensity/amplitude of light and is almost totally transparent. As a result, ImagePro™ was unable to reliably detect cell boundaries and provide accurate cell area measurements. Staining the sample would provide improved cellular discrimination but would kill the cells and would not allow longitudinal time studies of cell growth.
Phase contrast
The optical phase contrast image shows enhanced cellular discrimination with a "halo effect" apparent around each cell. This haloing effect resulted in inaccurate automated cell segmentation by ImagePro™ due to poor discrimination of the start of cell boundaries.
Phase contrast
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Segmentation - cell boundaries
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QPI
The QPI phase image shows clear cellular discrimination. These well defined cell boundaries allow for more robust threshold settings to be set in ImagePro™ and accurate measurements of cell area to be made.
QPI phase image
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Segmentation - cell boundaries
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Measurement over time
Utilising the QPI phase images, accurate cell area measurements were obtained of the same sample over a period of 92 hours providing data on the rate of cell growth.
The QPI based growth rate data from this study was also independently correlated with growth rates based on haemocytometer cell counts (r2=0.9933). Note that the haemocytometer based measurement entails destruction of the cell sample, making longitudinal time studies of the same cell line impossible.
Conclusion
Using QPI, transparent cells can be visualized with improved cellular boundary definition allowing precise and reproducible measurements of the cell area of a sample over time, and thus the rate of cell growth in a sample.
References
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Quantitative Phase Microscopy: a new tool for measurement of cell culture growth and confluency in situ
Claire L. Curl, Trudi Harris, Peter J. Harris, Catherine J. Bellair, Brendan E. Allman, Alastair G. Stewart, Lea M.D. Delbridge, Pflugers Archive: European J. of Physiology, 448, 462-468 (2004). -
The recognition of biological cells utilizing quantitative phase microscopy system
O. Veselov, J. Lekki, W. Polak, D. Strivay, Z. Stachura, K. Lebed, J. Styczen, Nuclear Instruments and Methods in Physics Research B 231 (2005) 212-217.