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Correction of out-of-focus microscopic images by deep learning
MOTIVATION: Microscopic images are widely used in basic biomedical research, disease diagnosis and medical discovery. Obtaining high-quality in-focus microscopy images has been a cornerstone of the microscopy. However, images obtained by microscopes are often out-of-focus, resulting in poor performa...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Research Network of Computational and Structural Biotechnology
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9062264/ https://www.ncbi.nlm.nih.gov/pubmed/35521557 http://dx.doi.org/10.1016/j.csbj.2022.04.003 |
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author | Zhang, Chi Jiang, Hao Liu, Weihuang Li, Junyi Tang, Shiming Juhas, Mario Zhang, Yang |
author_facet | Zhang, Chi Jiang, Hao Liu, Weihuang Li, Junyi Tang, Shiming Juhas, Mario Zhang, Yang |
author_sort | Zhang, Chi |
collection | PubMed |
description | MOTIVATION: Microscopic images are widely used in basic biomedical research, disease diagnosis and medical discovery. Obtaining high-quality in-focus microscopy images has been a cornerstone of the microscopy. However, images obtained by microscopes are often out-of-focus, resulting in poor performance in research and diagnosis. RESULTS: To solve the out-of-focus issue in microscopy, we developed a Cycle Generative Adversarial Network (CycleGAN) based model and a multi-component weighted loss function. We train and test our network in two self-collected datasets, namely Leishmania parasite dataset captured by a bright-field microscope, and bovine pulmonary artery endothelial cells (BPAEC) captured by a confocal fluorescence microscope. In comparison to other GAN-based deblurring methods, the proposed model reached state-of-the-art performance in correction. Another publicly available dataset, human cells dataset from the Broad Bioimage Benchmark Collection is used for evaluating the generalization abilities of the model. Our model showed excellent generalization capability, which could transfer to different types of microscopic image datasets. AVAILABILITY AND IMPLEMENTATION: Code and dataset are publicly available at: https://github.com/jiangdat/COMI. |
format | Online Article Text |
id | pubmed-9062264 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Research Network of Computational and Structural Biotechnology |
record_format | MEDLINE/PubMed |
spelling | pubmed-90622642022-05-04 Correction of out-of-focus microscopic images by deep learning Zhang, Chi Jiang, Hao Liu, Weihuang Li, Junyi Tang, Shiming Juhas, Mario Zhang, Yang Comput Struct Biotechnol J Research Article MOTIVATION: Microscopic images are widely used in basic biomedical research, disease diagnosis and medical discovery. Obtaining high-quality in-focus microscopy images has been a cornerstone of the microscopy. However, images obtained by microscopes are often out-of-focus, resulting in poor performance in research and diagnosis. RESULTS: To solve the out-of-focus issue in microscopy, we developed a Cycle Generative Adversarial Network (CycleGAN) based model and a multi-component weighted loss function. We train and test our network in two self-collected datasets, namely Leishmania parasite dataset captured by a bright-field microscope, and bovine pulmonary artery endothelial cells (BPAEC) captured by a confocal fluorescence microscope. In comparison to other GAN-based deblurring methods, the proposed model reached state-of-the-art performance in correction. Another publicly available dataset, human cells dataset from the Broad Bioimage Benchmark Collection is used for evaluating the generalization abilities of the model. Our model showed excellent generalization capability, which could transfer to different types of microscopic image datasets. AVAILABILITY AND IMPLEMENTATION: Code and dataset are publicly available at: https://github.com/jiangdat/COMI. Research Network of Computational and Structural Biotechnology 2022-04-20 /pmc/articles/PMC9062264/ /pubmed/35521557 http://dx.doi.org/10.1016/j.csbj.2022.04.003 Text en © 2022 The Author(s) https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Research Article Zhang, Chi Jiang, Hao Liu, Weihuang Li, Junyi Tang, Shiming Juhas, Mario Zhang, Yang Correction of out-of-focus microscopic images by deep learning |
title | Correction of out-of-focus microscopic images by deep learning |
title_full | Correction of out-of-focus microscopic images by deep learning |
title_fullStr | Correction of out-of-focus microscopic images by deep learning |
title_full_unstemmed | Correction of out-of-focus microscopic images by deep learning |
title_short | Correction of out-of-focus microscopic images by deep learning |
title_sort | correction of out-of-focus microscopic images by deep learning |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9062264/ https://www.ncbi.nlm.nih.gov/pubmed/35521557 http://dx.doi.org/10.1016/j.csbj.2022.04.003 |
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