CIEGAN: A Deep Learning Tool for Cell Image Enhancement
Long-term live-cell imaging technology has emerged in the study of cell culture and development, and it is expected to elucidate the differentiation or reprogramming morphology of cells and the dynamic process of interaction between cells. There are some advantages to this technique: it is noninvasi...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-07-01
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| Series: | Frontiers in Genetics |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fgene.2022.913372/full |
| _version_ | 1818466445084852224 |
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| author | Qiushi Sun Xiaochun Yang Jingtao Guo Yang Zhao Yi Liu |
| author_facet | Qiushi Sun Xiaochun Yang Jingtao Guo Yang Zhao Yi Liu |
| author_sort | Qiushi Sun |
| collection | DOAJ |
| description | Long-term live-cell imaging technology has emerged in the study of cell culture and development, and it is expected to elucidate the differentiation or reprogramming morphology of cells and the dynamic process of interaction between cells. There are some advantages to this technique: it is noninvasive, high-throughput, low-cost, and it can help researchers explore phenomena that are otherwise difficult to observe. Many challenges arise in the real-time process, for example, low-quality micrographs are often obtained due to unavoidable human factors or technical factors in the long-term experimental period. Moreover, some core dynamics in the developmental process are rare and fleeting in imaging observation and difficult to recapture again. Therefore, this study proposes a deep learning method for microscope cell image enhancement to reconstruct sharp images. We combine generative adversarial nets and various loss functions to make blurry images sharp again, which is much more convenient for researchers to carry out further analysis. This technology can not only make up the blurry images of critical moments of the development process through image enhancement but also allows long-term live-cell imaging to find a balance between imaging speed and image quality. Furthermore, the scalability of this technology makes the methods perform well in fluorescence image enhancement. Finally, the method is tested in long-term live-cell imaging of human-induced pluripotent stem cell-derived cardiomyocyte differentiation experiments, and it can greatly improve the image space resolution ratio. |
| first_indexed | 2024-04-13T20:58:28Z |
| format | Article |
| id | doaj.art-d22bac5306c04099bb6ce82daea0a552 |
| institution | Directory Open Access Journal |
| issn | 1664-8021 |
| language | English |
| last_indexed | 2024-04-13T20:58:28Z |
| publishDate | 2022-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Genetics |
| spelling | doaj.art-d22bac5306c04099bb6ce82daea0a5522022-12-22T02:30:14ZengFrontiers Media S.A.Frontiers in Genetics1664-80212022-07-011310.3389/fgene.2022.913372913372CIEGAN: A Deep Learning Tool for Cell Image EnhancementQiushi Sun0Xiaochun Yang1Jingtao Guo2Yang Zhao3Yi Liu4Beijing Key Lab of Traffic Data Analysis and Mining, School of Computer and Information Technology, Beijing Jiaotong University, Beijing, ChinaState Key Laboratory of Natural and Biomimetic Drugs, MOE Key Laboratory of Cell Proliferation and Differentiation, Beijing Key Laboratory of Cardiometabolic Molecular Medicine, Institute of Molecular Medicine, College of Future Technology, Peking University, Beijing, ChinaBeijing Key Lab of Traffic Data Analysis and Mining, School of Computer and Information Technology, Beijing Jiaotong University, Beijing, ChinaState Key Laboratory of Natural and Biomimetic Drugs, MOE Key Laboratory of Cell Proliferation and Differentiation, Beijing Key Laboratory of Cardiometabolic Molecular Medicine, Institute of Molecular Medicine, College of Future Technology, Peking University, Beijing, ChinaBeijing Key Lab of Traffic Data Analysis and Mining, School of Computer and Information Technology, Beijing Jiaotong University, Beijing, ChinaLong-term live-cell imaging technology has emerged in the study of cell culture and development, and it is expected to elucidate the differentiation or reprogramming morphology of cells and the dynamic process of interaction between cells. There are some advantages to this technique: it is noninvasive, high-throughput, low-cost, and it can help researchers explore phenomena that are otherwise difficult to observe. Many challenges arise in the real-time process, for example, low-quality micrographs are often obtained due to unavoidable human factors or technical factors in the long-term experimental period. Moreover, some core dynamics in the developmental process are rare and fleeting in imaging observation and difficult to recapture again. Therefore, this study proposes a deep learning method for microscope cell image enhancement to reconstruct sharp images. We combine generative adversarial nets and various loss functions to make blurry images sharp again, which is much more convenient for researchers to carry out further analysis. This technology can not only make up the blurry images of critical moments of the development process through image enhancement but also allows long-term live-cell imaging to find a balance between imaging speed and image quality. Furthermore, the scalability of this technology makes the methods perform well in fluorescence image enhancement. Finally, the method is tested in long-term live-cell imaging of human-induced pluripotent stem cell-derived cardiomyocyte differentiation experiments, and it can greatly improve the image space resolution ratio.https://www.frontiersin.org/articles/10.3389/fgene.2022.913372/fullcell imageimage enhancementlong-term imagingdeep learninggenerative adversarial network |
| spellingShingle | Qiushi Sun Xiaochun Yang Jingtao Guo Yang Zhao Yi Liu CIEGAN: A Deep Learning Tool for Cell Image Enhancement Frontiers in Genetics cell image image enhancement long-term imaging deep learning generative adversarial network |
| title | CIEGAN: A Deep Learning Tool for Cell Image Enhancement |
| title_full | CIEGAN: A Deep Learning Tool for Cell Image Enhancement |
| title_fullStr | CIEGAN: A Deep Learning Tool for Cell Image Enhancement |
| title_full_unstemmed | CIEGAN: A Deep Learning Tool for Cell Image Enhancement |
| title_short | CIEGAN: A Deep Learning Tool for Cell Image Enhancement |
| title_sort | ciegan a deep learning tool for cell image enhancement |
| topic | cell image image enhancement long-term imaging deep learning generative adversarial network |
| url | https://www.frontiersin.org/articles/10.3389/fgene.2022.913372/full |
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