@article{EndesfelderMalkuschFlottmannetal.2011,
  author    = {Endesfelder, Ulrike and Malkusch, Sebastian and Flottmann, Benjamin and Mondry, Justine and Liguzinski, Piotr and Verveer, Peter J. and Heilemann, Mike},
  title     = {Chemically Induced Photoswitching of Fluorescent Probes - A General Concept for Super-Resolution Microscopy},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-74896},
  year      = {2011},
  abstract  = {We review fluorescent probes that can be photoswitched or photoactivated and are suited for single-molecule localization based super-resolution microscopy. We exploit the underlying photochemical mechanisms that allow photoswitching of many synthetic organic fluorophores in the presence of reducing agents, and study the impact of these on the photoswitching properties of various photoactivatable or photoconvertible fluorescent proteins. We have identified mEos2 as a fluorescent protein that exhibits reversible photoswitching under various imaging buffer conditions and present strategies to characterize reversible photoswitching. Finally, we discuss opportunities to combine fluorescent proteins with organic fluorophores for dual-color photoswitching microscopy.},
  subject      = {Super-Resolution Microscopy},
  language  = {en}
}
@article{EndesfelderMalkuschFlottmannetal.2011,
  author    = {Endesfelder, Ulrike and Malkusch, Sebastian and Flottmann, Benjamin and Mondry, Justine and Liguzinski, Piotr and Verveer, Peter J. and Heilemann, Mike},
  title     = {Chemically Induced Photoswitching of Fluorescent Probes - A General Concept for Super-Resolution Microscopy},
  series = {Molecules},
  volume    = {16},
  journal   = {Molecules},
  number    = {4},
  doi       = {10.3390/molecules16043106},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-134080},
  pages     = {3106-3118},
  year      = {2011},
  abstract  = {We review fluorescent probes that can be photoswitched or photoactivated and are suited for single-molecule localization based super-resolution microscopy. We exploit the underlying photochemical mechanisms that allow photoswitching of many synthetic organic fluorophores in the presence of reducing agents, and study the impact of these on the photoswitching properties of various photoactivatable or photoconvertible fluorescent proteins. We have identified mEos2 as a fluorescent protein that exhibits reversible photoswitching under various imaging buffer conditions and present strategies to characterize reversible photoswitching. Finally, we discuss opportunities to combine fluorescent proteins with organic fluorophores for dual-color photoswitching microscopy.},
  language  = {en}
}
@article{BerberichKurzReinhardetal.2021,
  author    = {Berberich, Andreas and Kurz, Andreas and Reinhard, Sebastian and Paul, Torsten Johann and Burd, Paul Ray and Sauer, Markus and Kollmannsberger, Philip},
  title     = {Fourier Ring Correlation and anisotropic kernel density estimation improve deep learning based SMLM reconstruction of microtubules},
  series = {Frontiers in Bioinformatics},
  volume    = {1},
  journal   = {Frontiers in Bioinformatics},
  doi       = {10.3389/fbinf.2021.752788},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-261686},
  year      = {2021},
  abstract  = {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 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.},
  language  = {en}
}