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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Artículo científico |
| Language: | en |
| Published: |
Nature communications
2025
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| Subjects: | |
| Online Access: | https://pubmed.ncbi.nlm.nih.gov/39747824/ |
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| _version_ | 1868266260630863872 |
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| author | Guo, Min Wu, Yicong Hobson, Chad M Su, Yijun Qian, Shuhao Krueger, Eric Christensen, Ryan Kroeschell, Grant Bui, Johnny Chaw, Matthew Zhang, Lixia Liu, Jiamin Hou, Xuekai Han, Xiaofei Lu, Zhiye Ma, Xuefei Zhovmer, Alexander Combs, Christian Moyle, Mark Yemini, Eviatar Liu, Huafeng Liu, Zhiyi Benedetto, Alexandre La Riviere, Patrick Colón-Ramos, Daniel Shroff, Hari |
| author_facet | Guo, Min Wu, Yicong Hobson, Chad M Su, Yijun Qian, Shuhao Krueger, Eric Christensen, Ryan Kroeschell, Grant Bui, Johnny Chaw, Matthew Zhang, Lixia Liu, Jiamin Hou, Xuekai Han, Xiaofei Lu, Zhiye Ma, Xuefei Zhovmer, Alexander Combs, Christian Moyle, Mark Yemini, Eviatar Liu, Huafeng Liu, Zhiyi Benedetto, Alexandre La Riviere, Patrick Colón-Ramos, Daniel Shroff, Hari Guo, Min Wu, Yicong Hobson, Chad M Su, Yijun Qian, Shuhao Krueger, Eric Christensen, Ryan Kroeschell, Grant Bui, Johnny Chaw, Matthew Zhang, Lixia Liu, Jiamin Hou, Xuekai Han, Xiaofei Lu, Zhiye Ma, Xuefei Zhovmer, Alexander Combs, Christian Moyle, Mark Yemini, Eviatar Liu, Huafeng Liu, Zhiyi Benedetto, Alexandre La Riviere, Patrick Colón-Ramos, Daniel Shroff, Hari |
| collection | PubMed - marine biology |
| contents | Deep learning-based aberration compensation improves contrast and resolution in fluorescence microscopy. Guo, Min Wu, Yicong Hobson, Chad M Su, Yijun Qian, Shuhao Krueger, Eric Christensen, Ryan Kroeschell, Grant Bui, Johnny Chaw, Matthew Zhang, Lixia Liu, Jiamin Hou, Xuekai Han, Xiaofei Lu, Zhiye Ma, Xuefei Zhovmer, Alexander Combs, Christian Moyle, Mark Yemini, Eviatar Liu, Huafeng Liu, Zhiyi Benedetto, Alexandre La Riviere, Patrick Colón-Ramos, Daniel Shroff, Hari Deep Learning Animals Caenorhabditis elegans Microscopy, Fluorescence Mice Image Processing, Computer-Assisted Neural Networks, Computer Microscopy, Confocal Optical aberrations hinder fluorescence microscopy of thick samples, reducing image signal, contrast, and resolution. Here we introduce a deep learning-based strategy for aberration compensation, improving image quality without slowing image acquisition, applying additional dose, or introducing more optics. Our method (i) introduces synthetic aberrations to images acquired on the shallow side of image stacks, making them resemble those acquired deeper into the volume and (ii) trains neural networks to reverse the effect of these aberrations. We use simulations and experiments to show that applying the trained 'de-aberration' networks outperforms alternative methods, providing restoration on par with adaptive optics techniques; and subsequently apply the networks to diverse datasets captured with confocal, light-sheet, multi-photon, and super-resolution microscopy. In all cases, the improved quality of the restored data facilitates qualitative image inspection and improves downstream image quantitation, including orientational analysis of blood vessels in mouse tissue and improved membrane and nuclear segmentation in C. elegans embryos. |
| format | Artículo científico |
| id | pubmed_39747824 |
| institution | PubMed |
| language | en |
| publishDate | 2025 |
| publisher | Nature communications |
| record_format | pubmed |
| spellingShingle | Deep learning-based aberration compensation improves contrast and resolution in fluorescence microscopy. Guo, Min Wu, Yicong Hobson, Chad M Su, Yijun Qian, Shuhao Krueger, Eric Christensen, Ryan Kroeschell, Grant Bui, Johnny Chaw, Matthew Zhang, Lixia Liu, Jiamin Hou, Xuekai Han, Xiaofei Lu, Zhiye Ma, Xuefei Zhovmer, Alexander Combs, Christian Moyle, Mark Yemini, Eviatar Liu, Huafeng Liu, Zhiyi Benedetto, Alexandre La Riviere, Patrick Colón-Ramos, Daniel Shroff, Hari Deep Learning Animals Caenorhabditis elegans Microscopy, Fluorescence Mice Image Processing, Computer-Assisted Neural Networks, Computer Microscopy, Confocal Deep learning-based aberration compensation improves contrast and resolution in fluorescence microscopy. Guo, Min Wu, Yicong Hobson, Chad M Su, Yijun Qian, Shuhao Krueger, Eric Christensen, Ryan Kroeschell, Grant Bui, Johnny Chaw, Matthew Zhang, Lixia Liu, Jiamin Hou, Xuekai Han, Xiaofei Lu, Zhiye Ma, Xuefei Zhovmer, Alexander Combs, Christian Moyle, Mark Yemini, Eviatar Liu, Huafeng Liu, Zhiyi Benedetto, Alexandre La Riviere, Patrick Colón-Ramos, Daniel Shroff, Hari Deep Learning Animals Caenorhabditis elegans Microscopy, Fluorescence Mice Image Processing, Computer-Assisted Neural Networks, Computer Microscopy, Confocal Optical aberrations hinder fluorescence microscopy of thick samples, reducing image signal, contrast, and resolution. Here we introduce a deep learning-based strategy for aberration compensation, improving image quality without slowing image acquisition, applying additional dose, or introducing more optics. Our method (i) introduces synthetic aberrations to images acquired on the shallow side of image stacks, making them resemble those acquired deeper into the volume and (ii) trains neural networks to reverse the effect of these aberrations. We use simulations and experiments to show that applying the trained 'de-aberration' networks outperforms alternative methods, providing restoration on par with adaptive optics techniques; and subsequently apply the networks to diverse datasets captured with confocal, light-sheet, multi-photon, and super-resolution microscopy. In all cases, the improved quality of the restored data facilitates qualitative image inspection and improves downstream image quantitation, including orientational analysis of blood vessels in mouse tissue and improved membrane and nuclear segmentation in C. elegans embryos. |
| title | Deep learning-based aberration compensation improves contrast and resolution in fluorescence microscopy. |
| topic | Deep Learning Animals Caenorhabditis elegans Microscopy, Fluorescence Mice Image Processing, Computer-Assisted Neural Networks, Computer Microscopy, Confocal |
| url | https://pubmed.ncbi.nlm.nih.gov/39747824/ |