Superresolution Microscopy With Deep Learning

Superresolution microscopy – a technology that enables the acquisition of fluorescent micrographs of samples with a resolution well below the optical diffraction limit of ~250 nm – is rapidly evolving. Several methods have been developed during the past two decades that allow for this extension of conventional optical microscopy, and they have substantially contributed to the overall understanding of systems as complex as the specific arrangement of chromatin in cells during interphase, or resolving the inner structure of polymer networks in microgels.

With this new ability to visualize such biomedical systems on the nanoscale, the scientific community has developed a significantly improved understanding of the inner workings of cellular protein factories. This superresolution microscopy ability, however, comes at the expense of an ever-increasing need for storage space. To produce higher-resolution optical micrographs, hundreds to thousands of diffraction-limited images, or images at much higher pixel densities, have to be taken, which requires the substantially longer acquisition and processing times. This need has limited the implementation of these methods by sectors such as the pharmaceutical industry, where high-throughput screening methods are still the first choice.

Several recent developments, however – such as parallel image processing, smart (content-aware) data acquisition, deep learning approaches to improve the quality of image data, microscope automation, and multiplane image acquisition at higher volumetric rates – are presenting new opportunities and challenges to manufacturers, developers, and users of superresolution microscopy.

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