Continuous scanning microscopy using deep learning deblurring

Michael J. Fanous; Gabriel Popescu

doi:10.1117/12.2623763

30 May 2022 Continuous scanning microscopy using deep learning deblurring

Michael J. Fanous, Gabriel Popescu

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Proceedings Volume PC12136, Unconventional Optical Imaging III; PC121360G (2022) https://doi.org/10.1117/12.2623763
Event: SPIE Photonics Europe, 2022, Strasbourg, France

Abstract

Using deep neural networks, we demonstrate an ultra-fast scanning approach that extracts sharp pictures from motion blurred videos. This strategy allows us to complete image acquisitions at up to 58x the frame rate of similar stop-and-stare systems. This method is implemented on a Zeiss AXIO Observer Z1 microscope in both brightfield and phase contrast modes at stage speeds of 5,000 μm/s to 10,000 μm/s. We validate the proposed method by imaging tissue slides and live cell cultures over time. These results comprise the first application of recurrent neural networks for microscopy motion deblurring. The presented approach not only retrieves the clear image, but also corrects any defocusing that occurs over scanning operations due to height irregularities in samples. This technique will significantly accelerate microcopy measurements and will be particularly advantageous for imaging extensive sample types.

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Michael J. Fanous and Gabriel Popescu "Continuous scanning microscopy using deep learning deblurring", Proc. SPIE PC12136, Unconventional Optical Imaging III, PC121360G (30 May 2022); https://doi.org/10.1117/12.2623763

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KEYWORDS

Microscopy

Neural networks

Image acquisition

Image retrieval

Live cell imaging

Microscopes

Phase contrast

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