@article {68385,
	title = {Rationalized deep learning super-resolution microscopy for sustained live imaging of rapid subcellular processes.},
	journal = {Nature Biotechnology},
	volume = {41},
	year = {2023},
	month = {2023 Mar 01},
	pages = {367-77},
	abstract = {<p>The goal when imaging bioprocesses with optical microscopy is to acquire the most spatiotemporal information with the least invasiveness. Deep neural networks have substantially improved optical microscopy, including image super-resolution and restoration, but still have substantial potential for artifacts. In this study, we developed rationalized deep learning (rDL) for structured illumination microscopy and lattice light sheet microscopy (LLSM) by incorporating prior knowledge of illumination patterns and, thereby, rationally guiding the network to denoise raw images. Here we demonstrate that rDL structured illumination microscopy eliminates spectral bias-induced resolution degradation and reduces model uncertainty by five-fold, improving the super-resolution information by more than ten-fold over other computational approaches. Moreover, rDL applied to LLSM enables self-supervised training by using the spatial or temporal continuity of noisy data itself, yielding results similar to those of supervised methods. We demonstrate the utility of rDL by imaging the rapid kinetics of motile cilia, nucleolar protein condensation during light-sensitive mitosis and long-term interactions between membranous and membrane-less organelles.</p>
},
	issn = {1546-1696},
	doi = {10.1038/s41587-022-01471-3},
	author = {Qiao, Chang and Li, Di and Liu, Yong and Zhang, Siwei and Liu, Kan and Liu, Chong and Guo, Yuting and Jiang, Tao and Fang, Chuyu and Li, Nan and Zeng, Yunmin and He, Kangmin and Zhu, Xueliang and Lippincott-Schwartz, Jennifer and Dai, Qionghai and Li, Dong}
}
