System- and Sample-agnostic Isotropic 3D Microscopy by Weakly Physics-informed, Domain-shift-resistant Axial Deblurring
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arXiv
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| Format: | Preprint |
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2024
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| author | Han, Jiashu Liu, Kunzan Isaacson, Keith B. Monakhova, Kristina Griffith, Linda G. You, Sixian |
| author_facet | Han, Jiashu Liu, Kunzan Isaacson, Keith B. Monakhova, Kristina Griffith, Linda G. You, Sixian |
| contents | Three-dimensional (3D) subcellular imaging is essential for biomedical research, but the diffraction limit of optical microscopy compromises axial resolution, hindering accurate 3D 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 (i.i.d.) noise and linear shift-invariant (LSI) point-spread functions (PSFs)) are often invalid. Here, we introduce SSAI-3D, a weakly physics-informed, domain-shift-resistant framework for robust isotropic 3D imaging. SSAI-3D enables robust axial deblurring by generating a PSF-flexible, noise-resilient, sample-informed training dataset and sparsely fine-tuning a large pre-trained blind deblurring network. SSAI-3D was 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 3D heterogeneous biological tissues with unknown blurring and noise across different microscopy systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_06337 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | System- and Sample-agnostic Isotropic 3D Microscopy by Weakly Physics-informed, Domain-shift-resistant Axial Deblurring Han, Jiashu Liu, Kunzan Isaacson, Keith B. Monakhova, Kristina Griffith, Linda G. You, Sixian Optics Image and Video Processing Biological Physics Three-dimensional (3D) subcellular imaging is essential for biomedical research, but the diffraction limit of optical microscopy compromises axial resolution, hindering accurate 3D 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 (i.i.d.) noise and linear shift-invariant (LSI) point-spread functions (PSFs)) are often invalid. Here, we introduce SSAI-3D, a weakly physics-informed, domain-shift-resistant framework for robust isotropic 3D imaging. SSAI-3D enables robust axial deblurring by generating a PSF-flexible, noise-resilient, sample-informed training dataset and sparsely fine-tuning a large pre-trained blind deblurring network. SSAI-3D was 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 3D heterogeneous biological tissues with unknown blurring and noise across different microscopy systems. |
| title | System- and Sample-agnostic Isotropic 3D Microscopy by Weakly Physics-informed, Domain-shift-resistant Axial Deblurring |
| topic | Optics Image and Video Processing Biological Physics |
| url | https://arxiv.org/abs/2406.06337 |