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GANscan: continuous scanning microscopy using deep learning deblurring

Most whole slide imaging (WSI) systems today rely on the “stop-and-stare” approach, where, at each field of view, the scanning stage is brought to a complete stop before the camera snaps a picture. This procedure ensures that each image is free of motion blur, which comes at the expense of long acqu...

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Autores principales: Fanous, Michael John, Popescu, Gabriel
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9452654/
https://www.ncbi.nlm.nih.gov/pubmed/36071043
http://dx.doi.org/10.1038/s41377-022-00952-z
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author Fanous, Michael John
Popescu, Gabriel
author_facet Fanous, Michael John
Popescu, Gabriel
author_sort Fanous, Michael John
collection PubMed
description Most whole slide imaging (WSI) systems today rely on the “stop-and-stare” approach, where, at each field of view, the scanning stage is brought to a complete stop before the camera snaps a picture. This procedure ensures that each image is free of motion blur, which comes at the expense of long acquisition times. In order to speed up the acquisition process, especially for large scanning areas, such as pathology slides, we developed an acquisition method in which the data is acquired continuously while the stage is moving at high speeds. Using generative adversarial networks (GANs), we demonstrate this ultra-fast imaging approach, referred to as GANscan, which restores sharp images from motion blurred videos. GANscan allows us to complete image acquisitions at 30x the throughput of stop-and-stare systems. This method is implemented on a Zeiss Axio Observer Z1 microscope, requires no specialized hardware, and accomplishes successful reconstructions at stage speeds of up to 5000 μm/s. We validate the proposed method by imaging H&E stained tissue sections. Our method not only retrieves crisp images from fast, continuous scans, but also adjusts for defocusing that occurs during scanning within +/− 5 μm. Using a consumer GPU, the inference runs at <20 ms/ image.
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spelling pubmed-94526542022-09-09 GANscan: continuous scanning microscopy using deep learning deblurring Fanous, Michael John Popescu, Gabriel Light Sci Appl Article Most whole slide imaging (WSI) systems today rely on the “stop-and-stare” approach, where, at each field of view, the scanning stage is brought to a complete stop before the camera snaps a picture. This procedure ensures that each image is free of motion blur, which comes at the expense of long acquisition times. In order to speed up the acquisition process, especially for large scanning areas, such as pathology slides, we developed an acquisition method in which the data is acquired continuously while the stage is moving at high speeds. Using generative adversarial networks (GANs), we demonstrate this ultra-fast imaging approach, referred to as GANscan, which restores sharp images from motion blurred videos. GANscan allows us to complete image acquisitions at 30x the throughput of stop-and-stare systems. This method is implemented on a Zeiss Axio Observer Z1 microscope, requires no specialized hardware, and accomplishes successful reconstructions at stage speeds of up to 5000 μm/s. We validate the proposed method by imaging H&E stained tissue sections. Our method not only retrieves crisp images from fast, continuous scans, but also adjusts for defocusing that occurs during scanning within +/− 5 μm. Using a consumer GPU, the inference runs at <20 ms/ image. Nature Publishing Group UK 2022-09-07 /pmc/articles/PMC9452654/ /pubmed/36071043 http://dx.doi.org/10.1038/s41377-022-00952-z Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Fanous, Michael John
Popescu, Gabriel
GANscan: continuous scanning microscopy using deep learning deblurring
title GANscan: continuous scanning microscopy using deep learning deblurring
title_full GANscan: continuous scanning microscopy using deep learning deblurring
title_fullStr GANscan: continuous scanning microscopy using deep learning deblurring
title_full_unstemmed GANscan: continuous scanning microscopy using deep learning deblurring
title_short GANscan: continuous scanning microscopy using deep learning deblurring
title_sort ganscan: continuous scanning microscopy using deep learning deblurring
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9452654/
https://www.ncbi.nlm.nih.gov/pubmed/36071043
http://dx.doi.org/10.1038/s41377-022-00952-z
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