Three-dimensional residual channel attention networks denoise and sharpen fluorescence microscopy image volumes
We demonstrate residual channel attention networks (RCAN) for the restoration and enhancement of volumetric time-lapse (four-dimensional) fluorescence microscopy data. First we modify RCAN to handle image volumes, showing that our network enables denoising competitive with three other state-of-the-a...
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| Published in | Nature methods Vol. 18; no. 6; pp. 678 - 687 |
|---|---|
| Main Authors | , , , , , , , , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
New York
Nature Publishing Group US
01.06.2021
Nature Publishing Group |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1548-7091 1548-7105 1548-7105 |
| DOI | 10.1038/s41592-021-01155-x |
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| Abstract | We demonstrate residual channel attention networks (RCAN) for the restoration and enhancement of volumetric time-lapse (four-dimensional) fluorescence microscopy data. First we modify RCAN to handle image volumes, showing that our network enables denoising competitive with three other state-of-the-art neural networks. We use RCAN to restore noisy four-dimensional super-resolution data, enabling image capture of over tens of thousands of images (thousands of volumes) without apparent photobleaching. Second, using simulations we show that RCAN enables resolution enhancement equivalent to, or better than, other networks. Third, we exploit RCAN for denoising and resolution improvement in confocal microscopy, enabling ~2.5-fold lateral resolution enhancement using stimulated emission depletion microscopy ground truth. Fourth, we develop methods to improve spatial resolution in structured illumination microscopy using expansion microscopy data as ground truth, achieving improvements of ~1.9-fold laterally and ~3.6-fold axially. Finally, we characterize the limits of denoising and resolution enhancement, suggesting practical benchmarks for evaluation and further enhancement of network performance.
Three-dimensional residual channel attention networks (RCAN) enable improved image denoising and resolution enhancement on volumetric time-lapse fluorescence microscopy data, allowing longitudinal super-resolution imaging of living samples. |
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| AbstractList | We demonstrate residual channel attention networks (RCAN) for the restoration and enhancement of volumetric time-lapse (four-dimensional) fluorescence microscopy data. First we modify RCAN to handle image volumes, showing that our network enables denoising competitive with three other state-of-the-art neural networks. We use RCAN to restore noisy four-dimensional super-resolution data, enabling image capture of over tens of thousands of images (thousands of volumes) without apparent photobleaching. Second, using simulations we show that RCAN enables resolution enhancement equivalent to, or better than, other networks. Third, we exploit RCAN for denoising and resolution improvement in confocal microscopy, enabling ~2.5-fold lateral resolution enhancement using stimulated emission depletion microscopy ground truth. Fourth, we develop methods to improve spatial resolution in structured illumination microscopy using expansion microscopy data as ground truth, achieving improvements of ~1.9-fold laterally and ~3.6-fold axially. Finally, we characterize the limits of denoising and resolution enhancement, suggesting practical benchmarks for evaluation and further enhancement of network performance. Three-dimensional residual channel attention networks (RCAN) enable improved image denoising and resolution enhancement on volumetric time-lapse fluorescence microscopy data, allowing longitudinal super-resolution imaging of living samples. We demonstrate residual channel attention networks (RCAN) for the restoration and enhancement of volumetric time-lapse (four-dimensional) fluorescence microscopy data. First we modify RCAN to handle image volumes, showing that our network enables denoising competitive with three other state-of-the-art neural networks. We use RCAN to restore noisy four-dimensional super-resolution data, enabling image capture of over tens of thousands of images (thousands of volumes) without apparent photobleaching. Second, using simulations we show that RCAN enables resolution enhancement equivalent to, or better than, other networks. Third, we exploit RCAN for denoising and resolution improvement in confocal microscopy, enabling ~2.5-fold lateral resolution enhancement using stimulated emission depletion microscopy ground truth. Fourth, we develop methods to improve spatial resolution in structured illumination microscopy using expansion microscopy data as ground truth, achieving improvements of ~1.9-fold laterally and ~3.6-fold axially. Finally, we characterize the limits of denoising and resolution enhancement, suggesting practical benchmarks for evaluation and further enhancement of network performance. We demonstrate residual channel attention networks (RCAN) for the restoration and enhancement of volumetric time-lapse (four-dimensional) fluorescence microscopy data. First we modify RCAN to handle image volumes, showing that our network enables denoising competitive with three other state-of-the-art neural networks. We use RCAN to restore noisy four-dimensional super-resolution data, enabling image capture of over tens of thousands of images (thousands of volumes) without apparent photobleaching. Second, using simulations we show that RCAN enables resolution enhancement equivalent to, or better than, other networks. Third, we exploit RCAN for denoising and resolution improvement in confocal microscopy, enabling ~2.5-fold lateral resolution enhancement using stimulated emission depletion microscopy ground truth. Fourth, we develop methods to improve spatial resolution in structured illumination microscopy using expansion microscopy data as ground truth, achieving improvements of ~1.9-fold laterally and ~3.6-fold axially. Finally, we characterize the limits of denoising and resolution enhancement, suggesting practical benchmarks for evaluation and further enhancement of network performance. Three-dimensional residual channel attention networks (RCAN) enable improved image denoising and resolution enhancement on volumetric time-lapse fluorescence microscopy data, allowing longitudinal super-resolution imaging of living samples. We demonstrate residual channel attention networks (RCAN) for the restoration and enhancement of volumetric time-lapse (four-dimensional) fluorescence microscopy data. First we modify RCAN to handle image volumes, showing that our network enables denoising competitive with three other state-of-the-art neural networks. We use RCAN to restore noisy four-dimensional super-resolution data, enabling image capture of over tens of thousands of images (thousands of volumes) without apparent photobleaching. Second, using simulations we show that RCAN enables resolution enhancement equivalent to, or better than, other networks. Third, we exploit RCAN for denoising and resolution improvement in confocal microscopy, enabling ~2.5-fold lateral resolution enhancement using stimulated emission depletion microscopy ground truth. Fourth, we develop methods to improve spatial resolution in structured illumination microscopy using expansion microscopy data as ground truth, achieving improvements of ~1.9-fold laterally and ~3.6-fold axially. Finally, we characterize the limits of denoising and resolution enhancement, suggesting practical benchmarks for evaluation and further enhancement of network performance.We demonstrate residual channel attention networks (RCAN) for the restoration and enhancement of volumetric time-lapse (four-dimensional) fluorescence microscopy data. First we modify RCAN to handle image volumes, showing that our network enables denoising competitive with three other state-of-the-art neural networks. We use RCAN to restore noisy four-dimensional super-resolution data, enabling image capture of over tens of thousands of images (thousands of volumes) without apparent photobleaching. Second, using simulations we show that RCAN enables resolution enhancement equivalent to, or better than, other networks. Third, we exploit RCAN for denoising and resolution improvement in confocal microscopy, enabling ~2.5-fold lateral resolution enhancement using stimulated emission depletion microscopy ground truth. Fourth, we develop methods to improve spatial resolution in structured illumination microscopy using expansion microscopy data as ground truth, achieving improvements of ~1.9-fold laterally and ~3.6-fold axially. Finally, we characterize the limits of denoising and resolution enhancement, suggesting practical benchmarks for evaluation and further enhancement of network performance. |
| Audience | Academic |
| Author | Lai, Hoyin Rey-Suarez, Ivan Su, Yijun Chen, Jiji Zhovmer, Alexander Combs, Christian A. Wu, Yicong Lee, Shih-Jong J. Huang, Chi Chou Chang, Hung-Yu Guo, Min Li, Xuesong Nizambad, Srineil Upadhyaya, Arpita Shroff, Hari Sasaki, Hideki Lucas, Luciano A. G. Liu, Jiamin |
| Author_xml | – sequence: 1 givenname: Jiji orcidid: 0000-0002-4426-3035 surname: Chen fullname: Chen, Jiji email: jiji.chen@nih.gov organization: Advanced Imaging and Microscopy Resource, National Institutes of Health – sequence: 2 givenname: Hideki orcidid: 0000-0002-5926-3740 surname: Sasaki fullname: Sasaki, Hideki email: hideki.sasaki@aivia-software.com organization: Leica Microsystems, Inc., SVision LLC – sequence: 3 givenname: Hoyin surname: Lai fullname: Lai, Hoyin organization: Leica Microsystems, Inc., SVision LLC – sequence: 4 givenname: Yijun surname: Su fullname: Su, Yijun organization: Advanced Imaging and Microscopy Resource, National Institutes of Health, Leica Microsystems, Inc., SVision LLC, Laboratory of High Resolution Optical Imaging, National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health – sequence: 5 givenname: Jiamin surname: Liu fullname: Liu, Jiamin organization: Advanced Imaging and Microscopy Resource, National Institutes of Health – sequence: 6 givenname: Yicong surname: Wu fullname: Wu, Yicong organization: Laboratory of High Resolution Optical Imaging, National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health – sequence: 7 givenname: Alexander surname: Zhovmer fullname: Zhovmer, Alexander organization: Laboratory of Molecular Cardiology, National Heart, Lung, and Blood Institute, National Institutes of Health – sequence: 8 givenname: Christian A. surname: Combs fullname: Combs, Christian A. organization: NHLBI Light Microscopy Facility, National Institutes of Health – sequence: 9 givenname: Ivan surname: Rey-Suarez fullname: Rey-Suarez, Ivan organization: Biophysics Program, University of Maryland, Institute for Physical Science and Technology, University of Maryland – sequence: 10 givenname: Hung-Yu orcidid: 0000-0002-7666-4511 surname: Chang fullname: Chang, Hung-Yu organization: Leica Microsystems, Inc., SVision LLC – sequence: 11 givenname: Chi Chou surname: Huang fullname: Huang, Chi Chou organization: Leica Microsystems, Inc., SVision LLC – sequence: 12 givenname: Xuesong surname: Li fullname: Li, Xuesong organization: Laboratory of High Resolution Optical Imaging, National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health – sequence: 13 givenname: Min orcidid: 0000-0002-2093-8771 surname: Guo fullname: Guo, Min organization: Laboratory of High Resolution Optical Imaging, National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health – sequence: 14 givenname: Srineil surname: Nizambad fullname: Nizambad, Srineil organization: Advanced Imaging and Microscopy Resource, National Institutes of Health – sequence: 15 givenname: Arpita surname: Upadhyaya fullname: Upadhyaya, Arpita organization: Biophysics Program, University of Maryland, Institute for Physical Science and Technology, University of Maryland, Department of Physics, University of Maryland – sequence: 16 givenname: Shih-Jong J. surname: Lee fullname: Lee, Shih-Jong J. organization: Leica Microsystems, Inc., SVision LLC – sequence: 17 givenname: Luciano A. G. surname: Lucas fullname: Lucas, Luciano A. G. organization: Leica Microsystems, Inc., SVision LLC – sequence: 18 givenname: Hari surname: Shroff fullname: Shroff, Hari organization: Advanced Imaging and Microscopy Resource, National Institutes of Health, Laboratory of High Resolution Optical Imaging, National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/34059829$$D View this record in MEDLINE/PubMed |
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| Copyright | This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply 2021 COPYRIGHT 2021 Nature Publishing Group This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply 2021. |
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| Snippet | We demonstrate residual channel attention networks (RCAN) for the restoration and enhancement of volumetric time-lapse (four-dimensional) fluorescence... |
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| SubjectTerms | 631/114/1305 631/1647/245/2225 Algorithms Benchmarks Bioinformatics Biological Microscopy Biological Techniques Biomedical and Life Sciences Biomedical Engineering/Biotechnology Confocal microscopy Deep Learning Depletion Fluorescence Fluorescence microscopy Image enhancement Image Processing, Computer-Assisted Image resolution Image restoration Life Sciences Methods Microscopes Microscopy Microscopy, Fluorescence - methods Neural networks Noise reduction Photobleaching Proteomics Spatial discrimination Spatial resolution Stimulated emission |
| Title | Three-dimensional residual channel attention networks denoise and sharpen fluorescence microscopy image volumes |
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