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Enhancing the signal-to-noise ratio and generating contrast for cryo-EM images with convolutional neural networks
In cryogenic electron microscopy (cryo-EM) of radiation-sensitive biological samples, both the signal-to-noise ratio (SNR) and the contrast of images are critically important in the image-processing pipeline. Classic methods improve low-frequency image contrast experimentally, by imaging with high d...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
International Union of Crystallography
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7642784/ https://www.ncbi.nlm.nih.gov/pubmed/33209325 http://dx.doi.org/10.1107/S2052252520013184 |
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author | Palovcak, Eugene Asarnow, Daniel Campbell, Melody G. Yu, Zanlin Cheng, Yifan |
author_facet | Palovcak, Eugene Asarnow, Daniel Campbell, Melody G. Yu, Zanlin Cheng, Yifan |
author_sort | Palovcak, Eugene |
collection | PubMed |
description | In cryogenic electron microscopy (cryo-EM) of radiation-sensitive biological samples, both the signal-to-noise ratio (SNR) and the contrast of images are critically important in the image-processing pipeline. Classic methods improve low-frequency image contrast experimentally, by imaging with high defocus, or computationally, by applying various types of low-pass filter. These contrast improvements typically come at the expense of the high-frequency SNR, which is suppressed by high-defocus imaging and removed by low-pass filtration. Recently, convolutional neural networks (CNNs) trained to denoise cryo-EM images have produced impressive gains in image contrast, but it is not clear how these algorithms affect the information content of the image. Here, a denoising CNN for cryo-EM images was implemented and a quantitative evaluation of SNR enhancement, induced bias and the effects of denoising on image processing and three-dimensional reconstructions was performed. The study suggests that besides improving the visual contrast of cryo-EM images, the enhanced SNR of denoised images may be used in other parts of the image-processing pipeline, such as classification and 3D alignment. These results lay the groundwork for the use of denoising CNNs in the cryo-EM image-processing pipeline beyond particle picking. |
format | Online Article Text |
id | pubmed-7642784 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | International Union of Crystallography |
record_format | MEDLINE/PubMed |
spelling | pubmed-76427842020-11-17 Enhancing the signal-to-noise ratio and generating contrast for cryo-EM images with convolutional neural networks Palovcak, Eugene Asarnow, Daniel Campbell, Melody G. Yu, Zanlin Cheng, Yifan IUCrJ Research Papers In cryogenic electron microscopy (cryo-EM) of radiation-sensitive biological samples, both the signal-to-noise ratio (SNR) and the contrast of images are critically important in the image-processing pipeline. Classic methods improve low-frequency image contrast experimentally, by imaging with high defocus, or computationally, by applying various types of low-pass filter. These contrast improvements typically come at the expense of the high-frequency SNR, which is suppressed by high-defocus imaging and removed by low-pass filtration. Recently, convolutional neural networks (CNNs) trained to denoise cryo-EM images have produced impressive gains in image contrast, but it is not clear how these algorithms affect the information content of the image. Here, a denoising CNN for cryo-EM images was implemented and a quantitative evaluation of SNR enhancement, induced bias and the effects of denoising on image processing and three-dimensional reconstructions was performed. The study suggests that besides improving the visual contrast of cryo-EM images, the enhanced SNR of denoised images may be used in other parts of the image-processing pipeline, such as classification and 3D alignment. These results lay the groundwork for the use of denoising CNNs in the cryo-EM image-processing pipeline beyond particle picking. International Union of Crystallography 2020-10-24 /pmc/articles/PMC7642784/ /pubmed/33209325 http://dx.doi.org/10.1107/S2052252520013184 Text en © Eugene Palovcak et al. 2020 http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution (CC-BY) Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original authors and source are cited.http://creativecommons.org/licenses/by/4.0/ |
spellingShingle | Research Papers Palovcak, Eugene Asarnow, Daniel Campbell, Melody G. Yu, Zanlin Cheng, Yifan Enhancing the signal-to-noise ratio and generating contrast for cryo-EM images with convolutional neural networks |
title | Enhancing the signal-to-noise ratio and generating contrast for cryo-EM images with convolutional neural networks |
title_full | Enhancing the signal-to-noise ratio and generating contrast for cryo-EM images with convolutional neural networks |
title_fullStr | Enhancing the signal-to-noise ratio and generating contrast for cryo-EM images with convolutional neural networks |
title_full_unstemmed | Enhancing the signal-to-noise ratio and generating contrast for cryo-EM images with convolutional neural networks |
title_short | Enhancing the signal-to-noise ratio and generating contrast for cryo-EM images with convolutional neural networks |
title_sort | enhancing the signal-to-noise ratio and generating contrast for cryo-em images with convolutional neural networks |
topic | Research Papers |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7642784/ https://www.ncbi.nlm.nih.gov/pubmed/33209325 http://dx.doi.org/10.1107/S2052252520013184 |
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