Computational imaging in cell biology
Eils et al. (2003): Computational imaging in cell biology
This paper deals with "computational methods that (semi-) automatically quantify objects, distances, concentrations, and velocities of cells and subcellular structures" and thus generate quantitative data that "provide the basis for mathematical modeling of protein kinetics and biochemical signaling networks".
In the introduction, the authors write:
Fluorescent dyes such as fluorescein and rhodamine, together with recombinant fluorescent protein technology and voltage- and pH-sensitive dyes allow virtually any cellular structure to be tagged. In combination with techniques in live cells like FRAP and fluorescence resonance energy transfer, it is now possible to obtain spatio-temporal, biochemical, and biophysical information about the cell in a manner not imaginable before.
This is continued by an elaboration on "methods for segmentation and tracking of cells".
Nowadays, techniques for fully automated analysis and time space visualization of time series from living cells involve either segmentation and tracking of individual structures, or continuous motion estimation. For tracking a large number of small particles that move individually and independently from each other, single particle tracking approaches are most appropriate.
Several paragraphs follow that explain how these methods work. The paper also mentions computer vision, visualization and quantitative image analysis.
A great advantage of the combination of segmentation and surface reconstruction is the immediate access to quantitative information that corresponds to visual data. These approaches were designed to deal particularly with the high degree of anisotropy typical for 4-D live-cell recordings and to directly estimate quantitative parameters, e.g., the gray values in the segmented area of corresponding images can be measured to determine the amount and concentration of fluorescently labeled proteins in the segmented cellular compartments.
Finally, the paper mentions a couple of applications and concludes:
In combination with models of biochemical processes and regulatory networks, computational imaging as part of the emerging field of systems biology will lead to the identification of novel principles of cellular regulation derived from the huge amount of experimental data that are currently generated.