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| Author(s): | Martin Weigert, Uwe Schmidt, Tobias Boothe, Andreas Müller, Alexandr Dibrov, Akanksha Jain, Benjamin Wilhelm, Deborah Schmidt, Coleman Broaddus, Sian Culley, Mauricio Rocha-Martins, Fabián Segovia-Miranda, Caren Norden, Ricardo Henriques, Marino Zerial, Michele Solimena, Jochen Rink, Pavel Tomancak, Loic Royer, Florian Jug, Eugene W. Myers |
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| Abstract: | Fluorescence microscopy is a key driver of discoveries in the life-sciences, with observable phenomena being limited by the optics of the microscope, the chemistry of the fluorophores, and the maximum photon exposure tolerated by the sample. These limits necessitate trade- offs between imaging speed, spatial resolution, light exposure, and imaging depth. In this work we show how image restoration based on deep learning extends the range of biological phenomena observable by microscopy. On seven concrete examples we demonstrate how microscopy images can be restored even if 60-fold fewer photons are used during acquisition, how near isotropic resolution can be achieved with up to 10-fold under-sampling along the axial direction, and how tubular and granular structures smaller than the diffraction limit can be resolved at 20-times higher frame-rates compared to state-of-the-art methods. All developed image restoration methods are freely available as open source software in Python, FIJI, and KNIME. |
| Journal Name: | Nature Methods |
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Denoising_Planaria.tar.gz
Planaria Denoising experiment
the corresponding low and high pairs of image patches (in python/numpy npz file format)
the test stacks used for the analysis, 3 low conditions (C1,C2,C3) and a GT condition |
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Denoising_Tribolium.tar.gz
Tribolium Denoising experiment
the corresponding low and high pairs of image patches (in python/numpy npz file format)
the test stacks used for the analysis. Each stack has 4 channels, corresponding to 3 low conditions (C1,C2,C3) and a GT condition. |
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Projection_Flywing.tar.gz
Flywing Projection experiment
[1] Blasse et al. "PreMosa: extracting 2D surfaces from 3D microscopy mosaics." Bioinformatics 2017. |
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Isotropic_Drosophila.tar.gz
Isotropic Reconstruction of Drosophila stacks
the corresponding low and high pairs of image patches (in python/numpy npz file format)
the test stack used for the analysis (from [1]) with an anisotropic pixelsizes (dx,dy,dz) = (0.39um,0.39um,1.95um). [1] Royer et al. "Adaptive light-sheet microscopy for long-term, high-resolution imaging in living organisms." Nat. Biotechnol. 2016. |
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Isotropic_Retina.tar.gz
Isotropic Reconstruction of Retina stacks
the corresponding low and high pairs of image patches (in python/numpy npz file format)
the test stacks used for the analysis. The anisotropic pixelsize is (dx,dy,dz) = (0.195um,0.195um,2um). |
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Isotropic_Liver.tar.gz
Isotropic Reconstruction of Liver stacks
the corresponding low and high pairs of image patches (in python/numpy npz file format)
the test stacks used for the analysis. A stack is provided with isotropic pixelsize ( |
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Synthetic_tubulin_granules.tar.gz
Planaria Denoising experiment
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Synthetic_tubulin_gfp.tar.gz
Synthetic Tubulin (GFP) Reconstruction
the corresponding low and high pairs of image patches (in python/numpy npz file format)
the test stacks used for the analysis.
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