System- and sample-agnostic isotropic three-dimensional microscopy by weakly physics-informed, domain-shift-resistant axial deblurring
Abstract Three-dimensional subcellular imaging is essential for biomedical research, but the diffraction limit of optical microscopy compromises axial resolution, hindering accurate three-dimensional structural analysis. This challenge is particularly pronounced in label-free imaging of thick, heter...
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| Format: | Article |
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Nature Portfolio
2025-01-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-025-56078-4 |
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| author | Jiashu Han Kunzan Liu Keith B. Isaacson Kristina Monakhova Linda G. Griffith Sixian You |
| author_facet | Jiashu Han Kunzan Liu Keith B. Isaacson Kristina Monakhova Linda G. Griffith Sixian You |
| author_sort | Jiashu Han |
| collection | DOAJ |
| description | Abstract Three-dimensional subcellular imaging is essential for biomedical research, but the diffraction limit of optical microscopy compromises axial resolution, hindering accurate three-dimensional structural analysis. This challenge is particularly pronounced in label-free imaging of thick, heterogeneous tissues, where assumptions about data distribution (e.g. sparsity, label-specific distribution, and lateral-axial similarity) and system priors (e.g. independent and identically distributed noise and linear shift-invariant point-spread functions are often invalid. Here, we introduce SSAI-3D, a weakly physics-informed, domain-shift-resistant framework for robust isotropic three-dimensional imaging. SSAI-3D enables robust axial deblurring by generating a diverse, noise-resilient, sample-informed training dataset and sparsely fine-tuning a large pre-trained blind deblurring network. SSAI-3D is applied to label-free nonlinear imaging of living organoids, freshly excised human endometrium tissue, and mouse whisker pads, and further validated in publicly available ground-truth-paired experimental datasets of three-dimensional heterogeneous biological tissues with unknown blurring and noise across different microscopy systems. |
| format | Article |
| id | doaj-art-9e02dfa8e0f8429c88f3a9f5be7781cc |
| institution | Kabale University |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-9e02dfa8e0f8429c88f3a9f5be7781cc2025-01-19T12:31:42ZengNature PortfolioNature Communications2041-17232025-01-0116111410.1038/s41467-025-56078-4System- and sample-agnostic isotropic three-dimensional microscopy by weakly physics-informed, domain-shift-resistant axial deblurringJiashu Han0Kunzan Liu1Keith B. Isaacson2Kristina Monakhova3Linda G. Griffith4Sixian You5Research Laboratory of Electronics, MITResearch Laboratory of Electronics, MITNewton-Wellesley Hospital, Mass General BrighamResearch Laboratory of Electronics, MITDepartment of Biological Engineering, MITResearch Laboratory of Electronics, MITAbstract Three-dimensional subcellular imaging is essential for biomedical research, but the diffraction limit of optical microscopy compromises axial resolution, hindering accurate three-dimensional structural analysis. This challenge is particularly pronounced in label-free imaging of thick, heterogeneous tissues, where assumptions about data distribution (e.g. sparsity, label-specific distribution, and lateral-axial similarity) and system priors (e.g. independent and identically distributed noise and linear shift-invariant point-spread functions are often invalid. Here, we introduce SSAI-3D, a weakly physics-informed, domain-shift-resistant framework for robust isotropic three-dimensional imaging. SSAI-3D enables robust axial deblurring by generating a diverse, noise-resilient, sample-informed training dataset and sparsely fine-tuning a large pre-trained blind deblurring network. SSAI-3D is applied to label-free nonlinear imaging of living organoids, freshly excised human endometrium tissue, and mouse whisker pads, and further validated in publicly available ground-truth-paired experimental datasets of three-dimensional heterogeneous biological tissues with unknown blurring and noise across different microscopy systems.https://doi.org/10.1038/s41467-025-56078-4 |
| spellingShingle | Jiashu Han Kunzan Liu Keith B. Isaacson Kristina Monakhova Linda G. Griffith Sixian You System- and sample-agnostic isotropic three-dimensional microscopy by weakly physics-informed, domain-shift-resistant axial deblurring Nature Communications |
| title | System- and sample-agnostic isotropic three-dimensional microscopy by weakly physics-informed, domain-shift-resistant axial deblurring |
| title_full | System- and sample-agnostic isotropic three-dimensional microscopy by weakly physics-informed, domain-shift-resistant axial deblurring |
| title_fullStr | System- and sample-agnostic isotropic three-dimensional microscopy by weakly physics-informed, domain-shift-resistant axial deblurring |
| title_full_unstemmed | System- and sample-agnostic isotropic three-dimensional microscopy by weakly physics-informed, domain-shift-resistant axial deblurring |
| title_short | System- and sample-agnostic isotropic three-dimensional microscopy by weakly physics-informed, domain-shift-resistant axial deblurring |
| title_sort | system and sample agnostic isotropic three dimensional microscopy by weakly physics informed domain shift resistant axial deblurring |
| url | https://doi.org/10.1038/s41467-025-56078-4 |
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