English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

. 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.

Structural anisotropy quantification improves the final superresolution image of localization microscopy