Parameter-free image resolution estimation based on decorrelation analysis

An Addendum to this article was published on 08 September 2020

Abstract

Super-resolution microscopy opened diverse new avenues of research by overcoming the resolution limit imposed by diffraction. Exploitation of the fluorescent emission of individual fluorophores made it possible to reveal structures beyond the diffraction limit. To accurately determine the resolution achieved during imaging is challenging with existing metrics. Here, we propose a method for assessing the resolution of individual super-resolved images based on image partial phase autocorrelation. The algorithm is model-free and does not require any user-defined parameters. We demonstrate its performance on a wide variety of imaging modalities, including diffraction-limited techniques. Finally, we show how our method can be used to optimize image acquisition and post-processing in super-resolution microscopy.

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Fig. 1: Image decorrelation analysis workflow.
Fig. 2: Confocal and STED.
Fig. 3: Widefield imaging and structured illumination microscopy.
Fig. 4: Localization microscopy.

Data availability

All data needed to evaluate the conclusions in the paper are present in the paper and/or the Supplementary Results and Notes. Additional data related to this paper may be requested from the authors.

Code availability

The MATLAB soruce code, the ImageJ plugin and the source Java code are publicly available on https://github.com/Ades91/ImDecorr.git or may be requested from the authors.

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Acknowledgements

The authors would like to thank T. Lukes and T. Laser for insightful discussions. We also thank H. Deschout, M. Muller, T. Huser and M. Sauer for sharing SOFI and SIM data. We also thank J. Schmied and P. Tinnefeld for sharing GATTAquant nanoruler data and N. Bantherle and A. Planchette for proofreading. This project has been funded in part by the Horizon 2020 research and innovation program of the European Union via grant 686271/SEFRI 16.0047. K.S.G. acknowledges support from the Horizon 2020 Framework Program of the European Union under the Marie Skłodowska-Curie grant agreement no. 750528 and thanks the NVIDIA Corporation for the donation of a Titan Xp GPU. A.D. and A.R. acknowledge support from the Zeiss IDEAS center. We would like to thank the EPFL BioImaging & Optics Core Facility (EPFL-BIOP) for access to confocal and STED microscopes.

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Contributions

A.D. proposed and developed the method, processed all of the presented data, and wrote the MATLAB and Java code. K.S.G. prepared all of the cells and performed measurements. A.R. supervised the research. A.D. wrote the manuscript with comments of all co-authors at all stages.

Corresponding authors

Correspondence to A. Descloux or A. Radenovic.

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The authors declare no competing interests.

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Peer review information: Rita Strack was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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Supplementary information

Supplementary Information

Supplementary Tables 1–2, Supplementary Notes 1–5 and Supplementary Results 1–7.

Reporting Summary

Supplementary Software

Image J Plugin: Image Decorrelation Analysis plugin that provides image resolution estimate.

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Descloux, A., Grußmayer, K.S. & Radenovic, A. Parameter-free image resolution estimation based on decorrelation analysis. Nat Methods 16, 918–924 (2019). https://doi.org/10.1038/s41592-019-0515-7

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