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Fourier Ring Correlation and anisotropic kernel density estimation improve deep learning based SMLM reconstruction of microtubules

Please always quote using this URN: urn:nbn:de:bvb:20-opus-261686
  • Single-molecule super-resolution microscopy (SMLM) techniques like dSTORM can reveal biological structures down to the nanometer scale. The achievable resolution is not only defined by the localization precision of individual fluorescent molecules, but also by their density, which becomes a limiting factor e.g., in expansion microscopy. Artificial deep neural networks can learn to reconstruct dense super-resolved structures such as microtubules from a sparse, noisy set of data points. This approach requires a robust method to assess the qualitySingle-molecule super-resolution microscopy (SMLM) techniques like dSTORM can reveal biological structures down to the nanometer scale. The achievable resolution is not only defined by the localization precision of individual fluorescent molecules, but also by their density, which becomes a limiting factor e.g., in expansion microscopy. Artificial deep neural networks can learn to reconstruct dense super-resolved structures such as microtubules from a sparse, noisy set of data points. This approach requires a robust method to assess the quality of a predicted density image and to quantitatively compare it to a ground truth image. Such a quality measure needs to be differentiable to be applied as loss function in deep learning. We developed a new trainable quality measure based on Fourier Ring Correlation (FRC) and used it to train deep neural networks to map a small number of sampling points to an underlying density. Smooth ground truth images of microtubules were generated from localization coordinates using an anisotropic Gaussian kernel density estimator. We show that the FRC criterion ideally complements the existing state-of-the-art multiscale structural similarity index, since both are interpretable and there is no trade-off between them during optimization. The TensorFlow implementation of our FRC metric can easily be integrated into existing deep learning workflows.show moreshow less

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Metadaten
Author: Andreas Berberich, Andreas Kurz, Sebastian Reinhard, Torsten Johann Paul, Paul Ray Burd, Markus Sauer, Philip Kollmannsberger
URN:urn:nbn:de:bvb:20-opus-261686
Document Type:Journal article
Faculties:Fakultät für Physik und Astronomie / Institut für Theoretische Physik und Astrophysik
Fakultät für Biologie / Theodor-Boveri-Institut für Biowissenschaften
Fakultät für Biologie / Center for Computational and Theoretical Biology
Language:English
Parent Title (English):Frontiers in Bioinformatics
Year of Completion:2021
Volume:1
Article Number:752788
Source:Frontiers in Bioinformatics (2021) 1:752788. https://doi.org/10.3389/fbinf.2021.752788
DOI:https://doi.org/10.3389/fbinf.2021.752788
Dewey Decimal Classification:5 Naturwissenschaften und Mathematik / 57 Biowissenschaften; Biologie / 570 Biowissenschaften; Biologie
Tag:dSTORM; deep learning–artificial neural network (DL-ANN); microtubule cytoskeleton; single molecule localization microscopy; super-resolution
Release Date:2022/04/25
Collections:Open-Access-Publikationsfonds / Förderzeitraum 2021
Licence (German):License LogoCC BY: Creative-Commons-Lizenz: Namensnennung 4.0 International