Deep learning enables structured illumination microscopy with low light levels and enhanced speed
Deep learning enables structured illumination microscopy with low light levels and enhanced speed
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DOI：
10.1038/s41467-020-15784-x
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发表时间：
2020-04-22
影响因子：
16.6
通讯作者：
Hahn, Klaus M.
中科院分区：
文献类型：
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作者：
Jin, Luhong;Liu, Bei;Hahn, Klaus M.
结构照明显微镜（SIM）超越了光学衍射极限，并提供了一个两倍的分辨率增强衍射极限显微镜。然而，它需要强烈的照明和多次采集来产生单个高分辨率图像。使用深度学习来增强SIM，我们将超分辨率SIM所需的原始图像数量减少了五倍，并在极低光照条件下生成图像（至少少100倍的光子）。我们验证了深度神经网络在不同细胞结构上的性能，并实现了多色活细胞超分辨率成像，大大减少了光漂白。超分辨率显微镜通常需要高的激光功率，这会引起光漂白并降低图像质量。在这里，作者通过深度学习增强了结构照明显微镜（SIM），以减少所需的原始图像数量，并提高其在弱光条件下的性能。
Structured illumination microscopy (SIM) surpasses the optical diffraction limit and offers a two-fold enhancement in resolution over diffraction limited microscopy. However, it requires both intense illumination and multiple acquisitions to produce a single high-resolution image. Using deep learning to augment SIM, we obtain a five-fold reduction in the number of raw images required for super-resolution SIM, and generate images under extreme low light conditions (at least 100x fewer photons). We validate the performance of deep neural networks on different cellular structures and achieve multi-color, live-cell super-resolution imaging with greatly reduced photobleaching. Super-resolution microscopy typically requires high laser powers which can induce photobleaching and degrade image quality. Here the authors augment structured illumination microscopy (SIM) with deep learning to reduce the number of raw images required and boost its performance under low light conditions.