Fluorescence Images
Figure 10: Overview and two regions of interest of a z-stack of cells (mitochondria in green, EB3 microtubule tips in red) acquired with a WF setup (left). Center: Background subtracted dataset from the left image. Right: 3D deconvolved dataset from the left image (constrained iterative method in ZEISS ZEN imaging software). In the overview, background subtracted and deconvolved datasets look very similar, but differences become visible in the detailed regions of interest. In Detail 2, it becomes apparent that the spatial resolution of the deconvolved dataset is much better compared to the background corrected image. In Detail 1, structures that are visible in the WF image can also be observed in the deconvolved image stack, but not with the background subtracted data, no matter how much the display contrast and brightness are increased.
Another example on a smaller scale is shown in Figure 10. Decon- volution reproduces the very crisp structures of mitochondria and microtubules while also showing the mitochondrial mem- brane stains. It also shows quite well the nucleolar-like struc- tures that are hardly visible in the cell nucleus. Tis weak signal is eroded and entirely removed by background correction, and the information is lost. In addition, all background correction methods change the signal-to-background ratio, which means that the results of an x-fold increase in a fluorescence response aſter background subtraction must not be directly compared to an x-fold response in an image without background subtraction applied. Figure 11 depicts the changes in intensities compared to a WF image aſter undergoing either background correction, deblurring (nearest-neighbors), or a full 3D deconvolution. As far as we can tell, the overall profile looks similar, but the advan- tages of reassigning photons from an entire z-stack to a 3D deconvolution over simply removing the background are quite obvious. Meanwhile, processing times are very small, even for 3D deconvolution, the most sophisticated method.
2020 November •
www.microscopy-today.com Deblurring and background subtractions (using the rolling
ball or nearest-neighbors method, or other approaches) usually do a good job in improving threshold-based segmentation of images because they render the background even and remove blur around objects. Terefore, they play a valuable part in image analysis workflows. However, when using them to reveal and extract information from images in order to draw scientific conclusions from them, they should be complemented by an approach that models the 3D context of a sample (Table 2).
Conclusion Confocal laser scanning microscopy or structured illumi-
nation methods (Apotome) use a sophisticated optical setup to allow acquisition of sharp images from single planes and of entire 3D volumes. 3D deconvolution approaches take into account as much information as possible from the imaging system and the sample in order to reconstruct the 3D volume as accurately as possible. Typical post-processing methods such as background subtraction and unsharp masking, by
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