Cargando…
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...
Autores principales: | , |
---|---|
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 |
_version_ | 1784784956923314176 |
---|---|
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. |
format | Online Article Text |
id | pubmed-9452654 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT fanousmichaeljohn ganscancontinuousscanningmicroscopyusingdeeplearningdeblurring AT popescugabriel ganscancontinuousscanningmicroscopyusingdeeplearningdeblurring |