Figure and Image Data for "BGnet: Accurate and rapid background estimation in single-molecule localization microscopy with deep neural nets," by Leonhard Möckl, Anish R. Roy, Petar N. Petrov, and W.E. Moerner, Proc. Nat. Acad. Sci. (USA) DOI: 10.1073/pnas.1916219117, published online 23 December 2019.

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Abstract/Contents

Abstract: Background fluorescence, especially when it exhibits undesired spatial features, is a primary factor for reduced image quality in optical microscopy. Structured background is particularly detrimental when analyzing single-molecule images for 3D localization microscopy or single-molecule tracking. Here, we introduce BGnet, a deep neural network with a U-net-type architecture, as a general method to rapidly estimate the background underlying the image of a point source with excellent accuracy, even when point spread function (PSF) engineering is in use to create complex PSF shapes. We trained BGnet to extract the background from images of various PSFs and show that the identification is accurate for a wide range of different interfering background structures constructed from many spatial frequencies. Furthermore, we demonstrate that the obtained background-corrected PSF images, both for simulated and experimental data, lead to a substantial improvement in localization precision. Finally, we verify that structured background estimation with BGnet results in higher quality of super-resolution reconstructions of biological structures.

Type of resource	software, multimedia
Date created	March 2019 - November 2019

Related Publication	Leonhard Möckl, Anish E. Roy, Petar N. Petrov, and W. E. Moerner, (*equal contributions), “Accurate and rapid background estimation in single-molecule localization microscopy using the deep neural network BGnet,” Proc. Nat. Acad. Sci. (USA) appearing (DOI: 10.1073/pnas.1916219117, published online 23 December 2019). https://doi.org/10.1073/pnas.1916219117
Location	https://purl.stanford.edu/pf526yt0197

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Preferred Citation: Moerner, William E. and Möckl, Leonhard and Roy, Anish R. and Petrov, Petar N. (2019). Figure and Image Data for "BGnet: Accurate and rapid background estimation in single-molecule localization microscopy with deep neural nets," by Leonhard Möckl, Anish R. Roy, Petar N. Petrov, and W.E. Moerner, Proc. Nat. Acad. Sci. (USA) DOI: 10.1073/pnas.1916219117, published online 23 December 2019. Stanford Digital Repository. Available at: https://purl.stanford.edu/pf526yt0197

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