Center for Systems Biology Dresden, Dresden, Germany.
Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany.
Nat Methods. 2018 Dec;15(12):1090-1097. doi: 10.1038/s41592-018-0216-7. Epub 2018 Nov 26.
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.
荧光显微镜是生命科学发现的关键驱动力，可观察到的现象受到显微镜光学、荧光染料化学和样品可耐受的最大光子暴露的限制。这些限制需要在成像速度、空间分辨率、光暴露和成像深度之间进行权衡。在这项工作中，我们展示了基于深度学习的内容感知图像恢复如何扩展显微镜可观察到的生物现象的范围。我们通过八个具体示例展示了即使在采集过程中使用的光子数量减少 60 倍，显微镜图像如何能够得到恢复，以及如何在轴向方向上以十倍的欠采样实现近乎各向同性的分辨率，以及如何以比最先进的方法高 20 倍的帧率解析比衍射极限小的管状和颗粒状结构。所有开发的图像恢复方法都以 Python、FIJI 和 KNIME 的开源软件形式免费提供。
