%0 Journal Article %1 weigert2018b %A Weigert, Martin %A Schmidt, Uwe %A Boothe, Tobias %A Müller, Andreas %A Dibrov, Alexandr %A Jain, Akanksha %A Wilhelm, Benjamin %A Schmidt, Deborah %A Broaddus, Coleman %A Culley, Siân %A Rocha-Martins, Mauricio %A Segovia-Miranda, Fabián %A Norden, Caren %A Henriques, Ricardo %A Zerial, Marino %A Solimena, Michele %A Rink, Jochen %A Tomancak, Pavel %A Royer, Loic %A Jug, Florian %A Myers, Eugene W. %D 2018 %J Nature Methods %K imported %N 12 %P 1090--1097 %R 10.1038/s41592-018-0216-7 %T Content-Aware Image Restoration: Pushing the Limits of Fluorescence Microscopy %U https://doi.org/10.1038/s41592-018-0216-7 %V 15 %X 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 content-aware image restoration based on deep learning extends the range of biological phenomena observable by microscopy. We demonstrate on eight concrete examples 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 tenfold 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.

@article{weigert2018b, 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 content-aware image restoration based on deep learning extends the range of biological phenomena observable by microscopy. We demonstrate on eight concrete examples 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 tenfold 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.}, added-at = {2025-01-30T09:24:38.000+0100}, author = {Weigert, Martin and Schmidt, Uwe and Boothe, Tobias and M{\"u}ller, Andreas and Dibrov, Alexandr and Jain, Akanksha and Wilhelm, Benjamin and Schmidt, Deborah and Broaddus, Coleman and Culley, Si{\^a}n and Rocha-Martins, Mauricio and Segovia-Miranda, Fabi{\'a}n and Norden, Caren and Henriques, Ricardo and Zerial, Marino and Solimena, Michele and Rink, Jochen and Tomancak, Pavel and Royer, Loic and Jug, Florian and Myers, Eugene W.}, biburl = {https://puma.scadsai.uni-leipzig.de/bibtex/2101ac210d5d60b0825de119fe944f420/mawe985g}, doi = {10.1038/s41592-018-0216-7}, interhash = {560025a892f6887186bca338e82f9c79}, intrahash = {101ac210d5d60b0825de119fe944f420}, journal = {Nature Methods}, keywords = {imported}, number = 12, pages = {1090--1097}, timestamp = {2025-01-30T09:24:38.000+0100}, title = {Content-Aware Image Restoration: Pushing the Limits of Fluorescence Microscopy}, ty = {JOUR}, url = {https://doi.org/10.1038/s41592-018-0216-7}, volume = 15, year = 2018 }