Super-Resolution Reconstruction of Cytoskeleton Image Based on A-Net Deep Learning Network
To date, live-cell imaging at the nanometer scale remains challenging. Even though super-resolution microscopy methods have enabled visualization of sub-cellular structures below the optical resolution limit, the spatial resolution is still far from enough for the structural reconstruction of biomol...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-09-01
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| Series: | Micromachines |
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| Online Access: | https://www.mdpi.com/2072-666X/13/9/1515 |
| _version_ | 1827658667275583488 |
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| author | Qian Chen Haoxin Bai Bingchen Che Tianyun Zhao Ce Zhang Kaige Wang Jintao Bai Wei Zhao |
| author_facet | Qian Chen Haoxin Bai Bingchen Che Tianyun Zhao Ce Zhang Kaige Wang Jintao Bai Wei Zhao |
| author_sort | Qian Chen |
| collection | DOAJ |
| description | To date, live-cell imaging at the nanometer scale remains challenging. Even though super-resolution microscopy methods have enabled visualization of sub-cellular structures below the optical resolution limit, the spatial resolution is still far from enough for the structural reconstruction of biomolecules in vivo (i.e., ~24 nm thickness of microtubule fiber). In this study, a deep learning network named A-net was developed and shows that the resolution of cytoskeleton images captured by a confocal microscope can be significantly improved by combining the A-net deep learning network with the DWDC algorithm based on a degradation model. Utilizing the DWDC algorithm to construct new datasets and taking advantage of A-net neural network’s features (i.e., considerably fewer layers and relatively small dataset), the noise and flocculent structures which originally interfere with the cellular structure in the raw image are significantly removed, with the spatial resolution improved by a factor of 10. The investigation shows a universal approach for exacting structural details of biomolecules, cells and organs from low-resolution images. |
| first_indexed | 2024-03-09T23:07:53Z |
| format | Article |
| id | doaj.art-71e48551b50a46279e952375cbd0dff2 |
| institution | Directory Open Access Journal |
| issn | 2072-666X |
| language | English |
| last_indexed | 2024-03-09T23:07:53Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Micromachines |
| spelling | doaj.art-71e48551b50a46279e952375cbd0dff22023-11-23T17:50:48ZengMDPI AGMicromachines2072-666X2022-09-01139151510.3390/mi13091515Super-Resolution Reconstruction of Cytoskeleton Image Based on A-Net Deep Learning NetworkQian Chen0Haoxin Bai1Bingchen Che2Tianyun Zhao3Ce Zhang4Kaige Wang5Jintao Bai6Wei Zhao7School of Automation, Northwestern Polytechnical University, Xi’an 710129, ChinaState Key Laboratory of Photon-Technology in Western China Energy, International Collaborative Center on Photoelectric Technology and Nano Functional Materials, Institute of Photonics & Photon Technology, Northwestern University, Xi’an 710127, ChinaState Key Laboratory of Photon-Technology in Western China Energy, International Collaborative Center on Photoelectric Technology and Nano Functional Materials, Institute of Photonics & Photon Technology, Northwestern University, Xi’an 710127, ChinaSchool of Automation, Northwestern Polytechnical University, Xi’an 710129, ChinaState Key Laboratory of Photon-Technology in Western China Energy, International Collaborative Center on Photoelectric Technology and Nano Functional Materials, Institute of Photonics & Photon Technology, Northwestern University, Xi’an 710127, ChinaState Key Laboratory of Photon-Technology in Western China Energy, International Collaborative Center on Photoelectric Technology and Nano Functional Materials, Institute of Photonics & Photon Technology, Northwestern University, Xi’an 710127, ChinaState Key Laboratory of Photon-Technology in Western China Energy, International Collaborative Center on Photoelectric Technology and Nano Functional Materials, Institute of Photonics & Photon Technology, Northwestern University, Xi’an 710127, ChinaState Key Laboratory of Photon-Technology in Western China Energy, International Collaborative Center on Photoelectric Technology and Nano Functional Materials, Institute of Photonics & Photon Technology, Northwestern University, Xi’an 710127, ChinaTo date, live-cell imaging at the nanometer scale remains challenging. Even though super-resolution microscopy methods have enabled visualization of sub-cellular structures below the optical resolution limit, the spatial resolution is still far from enough for the structural reconstruction of biomolecules in vivo (i.e., ~24 nm thickness of microtubule fiber). In this study, a deep learning network named A-net was developed and shows that the resolution of cytoskeleton images captured by a confocal microscope can be significantly improved by combining the A-net deep learning network with the DWDC algorithm based on a degradation model. Utilizing the DWDC algorithm to construct new datasets and taking advantage of A-net neural network’s features (i.e., considerably fewer layers and relatively small dataset), the noise and flocculent structures which originally interfere with the cellular structure in the raw image are significantly removed, with the spatial resolution improved by a factor of 10. The investigation shows a universal approach for exacting structural details of biomolecules, cells and organs from low-resolution images.https://www.mdpi.com/2072-666X/13/9/1515super-resolution reconstructionA-netdeep learning networkcytoskeleton |
| spellingShingle | Qian Chen Haoxin Bai Bingchen Che Tianyun Zhao Ce Zhang Kaige Wang Jintao Bai Wei Zhao Super-Resolution Reconstruction of Cytoskeleton Image Based on A-Net Deep Learning Network Micromachines super-resolution reconstruction A-net deep learning network cytoskeleton |
| title | Super-Resolution Reconstruction of Cytoskeleton Image Based on A-Net Deep Learning Network |
| title_full | Super-Resolution Reconstruction of Cytoskeleton Image Based on A-Net Deep Learning Network |
| title_fullStr | Super-Resolution Reconstruction of Cytoskeleton Image Based on A-Net Deep Learning Network |
| title_full_unstemmed | Super-Resolution Reconstruction of Cytoskeleton Image Based on A-Net Deep Learning Network |
| title_short | Super-Resolution Reconstruction of Cytoskeleton Image Based on A-Net Deep Learning Network |
| title_sort | super resolution reconstruction of cytoskeleton image based on a net deep learning network |
| topic | super-resolution reconstruction A-net deep learning network cytoskeleton |
| url | https://www.mdpi.com/2072-666X/13/9/1515 |
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