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Rationalized deep learning super-resolution microscopy for sustained live imaging of rapid subcellular processes. | LitMetric

AI Article Synopsis

  • - The study focuses on using deep learning techniques to enhance optical microscopy in a way that captures high-resolution images with minimal interference in biological processes.
  • - A new method called rationalized deep learning (rDL) was developed, which leverages knowledge of illumination patterns to improve image quality and significantly reduce artifacts and uncertainties found in traditional methods.
  • - The rDL approach was successfully applied to various biological imaging scenarios, demonstrating its ability to effectively capture fast-moving cellular dynamics and interactions, yielding superior results compared to existing computational techniques.

Article Abstract

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.

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Source
http://dx.doi.org/10.1038/s41587-022-01471-3DOI Listing

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