Structural anisotropy quantification improves the final superresolution image of localization microscopy.
Yina Wang,Zhen-Li Huang +1 more
TL;DR: With simulated as well as experimental data of a biological specimen, it is demonstrated that exploring structural anisotropy has allowed us to obtain superresolution images with a much cleaner background.
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Abstract: Superresolution localization microscopy initially produces a dataset of fluorophore coordinates instead of a conventional digital image. Therefore, superresolution localization microscopy requires additional data analysis to present a final superresolution image. However, methods of employing the structural information within the localization dataset to improve the data analysis performance remain poorly developed. Here, we quantify the structural information in a localization dataset using structural anisotropy, and propose to use it as a figure of merit for localization event filtering. With simulated as well as experimental data of a biological specimen, we demonstrate that exploring structural anisotropy has allowed us to obtain superresolution images with a much cleaner background.
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References
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