Deep Learning-Based Point-Scanning Super-Resolution Imaging | bioRxiv

Deep Learning-Based Point-Scanning Super-Resolution Imaging | bioRxiv

Point scanning imaging systems (e.g. scanning electron or laser scanning confocal microscopes) are perhaps the most widely used tools for high resolution cellular and tissue imaging. Like all other imaging modalities, the resolution, speed, sample preservation, and signal-to-noise ratio (SNR) of point scanning systems are difficult to optimize simultaneously. In particular, point scanning systems are uniquely constrained by an inverse relationship between imaging speed and pixel resolution. Here we show these limitations can be mitigated via the use of deep learning-based super-sampling of undersampled images acquired on a point-scanning system, which we termed point-scanning super-resolution (PSSR) imaging. Oversampled, high SNR ground truth images acquired on scanning electron or Airyscan laser scanning confocal microscopes were ‘crappified’ to generate semi-synthetic training data for PSSR models that were then used to restore real-world undersampled images. Remarkably, our EM PSSR

1 mentions: @jeremyphoward
Keywords: deep learning
Date: 2020/01/15 20:21

Referring Tweets

@jeremyphoward Heard that neural nets can't generalize? Not always true! For instance, in work we did @AiWamri with @manorlaboratory and many great collaborators, a model trained on one dataset generalized to many other labs, species, microscopes, and modalities! t.co/pmCSQSlCUK t.co/0KJjVlpz66

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