Deep learning-based aberration compensation improves contrast and resolution in fluorescence microscopy
Abstract 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 introdu...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-024-55267-x |
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| author | Min Guo Yicong Wu Chad M. Hobson Yijun Su Shuhao Qian Eric Krueger Ryan Christensen Grant Kroeschell Johnny Bui Matthew Chaw Lixia Zhang Jiamin Liu Xuekai Hou Xiaofei Han Zhiye Lu Xuefei Ma Alexander Zhovmer Christian Combs Mark Moyle Eviatar Yemini Huafeng Liu Zhiyi Liu Alexandre Benedetto Patrick La Riviere Daniel Colón-Ramos Hari Shroff |
| author_facet | Min Guo Yicong Wu Chad M. Hobson Yijun Su Shuhao Qian Eric Krueger Ryan Christensen Grant Kroeschell Johnny Bui Matthew Chaw Lixia Zhang Jiamin Liu Xuekai Hou Xiaofei Han Zhiye Lu Xuefei Ma Alexander Zhovmer Christian Combs Mark Moyle Eviatar Yemini Huafeng Liu Zhiyi Liu Alexandre Benedetto Patrick La Riviere Daniel Colón-Ramos Hari Shroff |
| author_sort | Min Guo |
| collection | DOAJ |
| description | Abstract 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 | Article |
| id | doaj-art-766d67edf8ea4d09aca345b0dd87a934 |
| institution | Kabale University |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-766d67edf8ea4d09aca345b0dd87a9342025-01-05T12:37:26ZengNature PortfolioNature Communications2041-17232025-01-0116111910.1038/s41467-024-55267-xDeep learning-based aberration compensation improves contrast and resolution in fluorescence microscopyMin Guo0Yicong Wu1Chad M. Hobson2Yijun Su3Shuhao Qian4Eric Krueger5Ryan Christensen6Grant Kroeschell7Johnny Bui8Matthew Chaw9Lixia Zhang10Jiamin Liu11Xuekai Hou12Xiaofei Han13Zhiye Lu14Xuefei Ma15Alexander Zhovmer16Christian Combs17Mark Moyle18Eviatar Yemini19Huafeng Liu20Zhiyi Liu21Alexandre Benedetto22Patrick La Riviere23Daniel Colón-Ramos24Hari Shroff25State Key Laboratory of Extreme Photonics and Instrumentation, College of Optical Science and Engineering, Zhejiang UniversityLaboratory of High Resolution Optical Imaging, National Institute of Biomedical Imaging and Bioengineering, National Institutes of HealthJanelia Research Campus, Howard Hughes Medical Institute (HHMI)Laboratory of High Resolution Optical Imaging, National Institute of Biomedical Imaging and Bioengineering, National Institutes of HealthState Key Laboratory of Extreme Photonics and Instrumentation, College of Optical Science and Engineering, Zhejiang UniversityLaboratory of High Resolution Optical Imaging, National Institute of Biomedical Imaging and Bioengineering, National Institutes of HealthLaboratory of High Resolution Optical Imaging, National Institute of Biomedical Imaging and Bioengineering, National Institutes of HealthLaboratory of High Resolution Optical Imaging, National Institute of Biomedical Imaging and Bioengineering, National Institutes of HealthLaboratory of High Resolution Optical Imaging, National Institute of Biomedical Imaging and Bioengineering, National Institutes of HealthLaboratory of High Resolution Optical Imaging, National Institute of Biomedical Imaging and Bioengineering, National Institutes of HealthAdvanced Imaging and Microscopy Resource, National Institutes of HealthAdvanced Imaging and Microscopy Resource, National Institutes of HealthState Key Laboratory of Extreme Photonics and Instrumentation, College of Optical Science and Engineering, Zhejiang UniversityLaboratory of High Resolution Optical Imaging, National Institute of Biomedical Imaging and Bioengineering, National Institutes of HealthLaboratory of Molecular Cardiology, National Heart, Lung, and Blood Institute, National Institutes of HealthLaboratory of Molecular Cardiology, National Heart, Lung, and Blood Institute, National Institutes of HealthCenter for Biologics Evaluation and Research, U.S. Food and Drug AdministrationNHLBI Light Microscopy Facility, National Institutes of HealthDepartment of Biology, Brigham Young University-IdahoDepartment of Neurobiology, UMass Chan Medical SchoolState Key Laboratory of Extreme Photonics and Instrumentation, College of Optical Science and Engineering, Zhejiang UniversityState Key Laboratory of Extreme Photonics and Instrumentation, College of Optical Science and Engineering, Zhejiang UniversityFaculty of Health and Medicine, Division of Biomedical and Life Sciences, Lancaster UniversityDepartment of Radiology, University of ChicagoMBL Fellows Program, Marine Biological LaboratoryLaboratory of High Resolution Optical Imaging, National Institute of Biomedical Imaging and Bioengineering, National Institutes of HealthAbstract 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.https://doi.org/10.1038/s41467-024-55267-x |
| spellingShingle | Min Guo Yicong Wu Chad M. Hobson Yijun Su Shuhao Qian Eric Krueger Ryan Christensen Grant Kroeschell Johnny Bui Matthew Chaw Lixia Zhang Jiamin Liu Xuekai Hou Xiaofei Han Zhiye Lu Xuefei Ma Alexander Zhovmer Christian Combs Mark Moyle Eviatar Yemini Huafeng Liu Zhiyi Liu Alexandre Benedetto Patrick La Riviere Daniel Colón-Ramos Hari Shroff Deep learning-based aberration compensation improves contrast and resolution in fluorescence microscopy Nature Communications |
| title | Deep learning-based aberration compensation improves contrast and resolution in fluorescence microscopy |
| title_full | Deep learning-based aberration compensation improves contrast and resolution in fluorescence microscopy |
| title_fullStr | Deep learning-based aberration compensation improves contrast and resolution in fluorescence microscopy |
| title_full_unstemmed | Deep learning-based aberration compensation improves contrast and resolution in fluorescence microscopy |
| title_short | Deep learning-based aberration compensation improves contrast and resolution in fluorescence microscopy |
| title_sort | deep learning based aberration compensation improves contrast and resolution in fluorescence microscopy |
| url | https://doi.org/10.1038/s41467-024-55267-x |
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