Deep learning enables structured illumination microscopy with low light levels and enhanced speed

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 100× 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.


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Software and code
Policy information about availability of computer code Data collection Data was collected on the Nikon N-SIM system.

Data analysis
Conventional SIM reconstruction was done with the NIS-Elements software (Nikon Elements AR 4.51.01 64bit). Fiji/ImageJ Version: 2.0.0-rc-69/1.52t. NanoJ SQUIRREL version:2.1-rc. All other codes related to deep learning can be found at https://github.com/drbeiliu/DeepLearning . For manuscripts utilizing custom algorithms or software that are central to the research but not yet described in published literature, software must be made available to editors/reviewers. We strongly encourage code deposition in a community repository (e.g. GitHub). See the Nature Research guidelines for submitting code & software for further information.

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The sample size for training, validation and testing can be found in Supplementary Table 1. We think the size of the dataset is sufficient since (1) the training process converge after 2000 epoch; (2) the validation dataset are used to monitor the training performance, which also indicated the training dataset is large enough.
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nature research | reporting summary
October 2018 Clinical data

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Flow Cytometry
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nature research | reporting summary
October 2018 Cell population abundance Describe the abundance of the relevant cell populations within post-sort fractions, providing details on the purity of the samples and how it was determined.

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Magnetic resonance imaging
Experimental design