Advanced Imaging Integration: Multi-Modal Raman Light Sheet Microscopy Combined with Zero-Shot Learning for Denoising and Super-Resolution
This study presents an advanced integration of Multi-modal Raman Light Sheet Microscopy with zero-shot learning-based computational methods to significantly enhance the resolution and analysis of complex three-dimensional biological structures, such as 3D cell cultures and spheroids. The Multi-modal...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-11-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/24/21/7083 |
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| author | Pooja Kumari Shaun Keck Emma Sohn Johann Kern Matthias Raedle |
| author_facet | Pooja Kumari Shaun Keck Emma Sohn Johann Kern Matthias Raedle |
| author_sort | Pooja Kumari |
| collection | DOAJ |
| description | This study presents an advanced integration of Multi-modal Raman Light Sheet Microscopy with zero-shot learning-based computational methods to significantly enhance the resolution and analysis of complex three-dimensional biological structures, such as 3D cell cultures and spheroids. The Multi-modal Raman Light Sheet Microscopy system incorporates Rayleigh scattering, Raman scattering, and fluorescence detection, enabling comprehensive, marker-free imaging of cellular architecture. These diverse modalities offer detailed spatial and molecular insights into cellular organization and interactions, critical for applications in biomedical research, drug discovery, and histological studies. To improve image quality without altering or introducing new biological information, we apply Zero-Shot Deconvolution Networks (ZS-DeconvNet), a deep-learning-based method that enhances resolution in an unsupervised manner. ZS-DeconvNet significantly refines image clarity and sharpness across multiple microscopy modalities without requiring large, labeled datasets, or introducing artifacts. By combining the strengths of multi-modal light sheet microscopy and ZS-DeconvNet, we achieve improved visualization of subcellular structures, offering clearer and more detailed representations of existing data. This approach holds significant potential for advancing high-resolution imaging in biomedical research and other related fields. |
| format | Article |
| id | doaj-art-5d5f73b84aaa4bd9a17f4f0ced6064a9 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-5d5f73b84aaa4bd9a17f4f0ced6064a92024-11-08T14:42:05ZengMDPI AGSensors1424-82202024-11-012421708310.3390/s24217083Advanced Imaging Integration: Multi-Modal Raman Light Sheet Microscopy Combined with Zero-Shot Learning for Denoising and Super-ResolutionPooja Kumari0Shaun Keck1Emma Sohn2Johann Kern3Matthias Raedle4CeMOS Research and Transfer Center, University of Applied Science, 68163 Mannheim, GermanyCeMOS Research and Transfer Center, University of Applied Science, 68163 Mannheim, GermanyUniversitätsklinikum Mannheim, Universität Heidelberg, 68167 Mannheim, GermanyUniversitätsklinikum Mannheim, Universität Heidelberg, 68167 Mannheim, GermanyCeMOS Research and Transfer Center, University of Applied Science, 68163 Mannheim, GermanyThis study presents an advanced integration of Multi-modal Raman Light Sheet Microscopy with zero-shot learning-based computational methods to significantly enhance the resolution and analysis of complex three-dimensional biological structures, such as 3D cell cultures and spheroids. The Multi-modal Raman Light Sheet Microscopy system incorporates Rayleigh scattering, Raman scattering, and fluorescence detection, enabling comprehensive, marker-free imaging of cellular architecture. These diverse modalities offer detailed spatial and molecular insights into cellular organization and interactions, critical for applications in biomedical research, drug discovery, and histological studies. To improve image quality without altering or introducing new biological information, we apply Zero-Shot Deconvolution Networks (ZS-DeconvNet), a deep-learning-based method that enhances resolution in an unsupervised manner. ZS-DeconvNet significantly refines image clarity and sharpness across multiple microscopy modalities without requiring large, labeled datasets, or introducing artifacts. By combining the strengths of multi-modal light sheet microscopy and ZS-DeconvNet, we achieve improved visualization of subcellular structures, offering clearer and more detailed representations of existing data. This approach holds significant potential for advancing high-resolution imaging in biomedical research and other related fields.https://www.mdpi.com/1424-8220/24/21/7083raman scatteringrayleigh scatteringzero-shot deconvolution networksdenoisingfluorescencelight sheet |
| spellingShingle | Pooja Kumari Shaun Keck Emma Sohn Johann Kern Matthias Raedle Advanced Imaging Integration: Multi-Modal Raman Light Sheet Microscopy Combined with Zero-Shot Learning for Denoising and Super-Resolution Sensors raman scattering rayleigh scattering zero-shot deconvolution networks denoising fluorescence light sheet |
| title | Advanced Imaging Integration: Multi-Modal Raman Light Sheet Microscopy Combined with Zero-Shot Learning for Denoising and Super-Resolution |
| title_full | Advanced Imaging Integration: Multi-Modal Raman Light Sheet Microscopy Combined with Zero-Shot Learning for Denoising and Super-Resolution |
| title_fullStr | Advanced Imaging Integration: Multi-Modal Raman Light Sheet Microscopy Combined with Zero-Shot Learning for Denoising and Super-Resolution |
| title_full_unstemmed | Advanced Imaging Integration: Multi-Modal Raman Light Sheet Microscopy Combined with Zero-Shot Learning for Denoising and Super-Resolution |
| title_short | Advanced Imaging Integration: Multi-Modal Raman Light Sheet Microscopy Combined with Zero-Shot Learning for Denoising and Super-Resolution |
| title_sort | advanced imaging integration multi modal raman light sheet microscopy combined with zero shot learning for denoising and super resolution |
| topic | raman scattering rayleigh scattering zero-shot deconvolution networks denoising fluorescence light sheet |
| url | https://www.mdpi.com/1424-8220/24/21/7083 |
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