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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2412.09775 |
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| _version_ | 1866912777751232512 |
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| author | Chandler, Talon Ivanov, Ivan E. Sturm, Gabriel Xiao, Sheng Zhao, Xiang Hillsley, Alexander Ryan, Allyson Quinn Liu, Ziwen Varra, Sricharan Reddy Theodoro, Ilan Hirata-Miyasaki, Eduardo Sundarraman, Deepika Verma, Amitabh Sekhar, Madhurya Liu, Chad Pradeep, Soorya Lee, See-Chi Rhoads, Shannon N. Zanellati, Maria Clara Cohen, Sarah Arias, Carolina Leonetti, Manuel D. Jacobo, Adrian Balla, Keir Royer, Loïc A. Mehta, Shalin B. |
| author_facet | Chandler, Talon Ivanov, Ivan E. Sturm, Gabriel Xiao, Sheng Zhao, Xiang Hillsley, Alexander Ryan, Allyson Quinn Liu, Ziwen Varra, Sricharan Reddy Theodoro, Ilan Hirata-Miyasaki, Eduardo Sundarraman, Deepika Verma, Amitabh Sekhar, Madhurya Liu, Chad Pradeep, Soorya Lee, See-Chi Rhoads, Shannon N. Zanellati, Maria Clara Cohen, Sarah Arias, Carolina Leonetti, Manuel D. Jacobo, Adrian Balla, Keir Royer, Loïc A. Mehta, Shalin B. |
| contents | Correlative computational microscopy can accelerate imaging and modeling of cellular dynamics by relaxing trade-offs inherent to dynamic imaging. Existing computational microscopy frameworks are either specialized or overly generic, limiting use to fixed configurations or domain experts. We introduce WaveOrder, a generalist wave-optical framework for imaging the architectural order of biomolecules. WaveOrder reconstructs diverse specimen properties from multi-channel acquisitions, with or without fluorescence. It provides a unified representation of linear optical properties and differentiable physics-based image formation models spanning widefield, confocal, light-sheet, and oblique label-free geometries. WaveOrder uses physics-informed ML to auto-tune model parameters and solve blind shift-variant restoration problems. This open-source, PyTorch-based framework enables scalable quantitative imaging across scales from organelles to adult zebrafish, and improves restoration of cellular structures in high-throughput experiments. We validate WaveOrder on diverse imaging applications, demonstrating its ability to recover biomolecular structure beyond the limits of existing approaches. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_09775 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | WaveOrder: A differentiable wave-optical framework for scalable biological microscopy with diverse modalities Chandler, Talon Ivanov, Ivan E. Sturm, Gabriel Xiao, Sheng Zhao, Xiang Hillsley, Alexander Ryan, Allyson Quinn Liu, Ziwen Varra, Sricharan Reddy Theodoro, Ilan Hirata-Miyasaki, Eduardo Sundarraman, Deepika Verma, Amitabh Sekhar, Madhurya Liu, Chad Pradeep, Soorya Lee, See-Chi Rhoads, Shannon N. Zanellati, Maria Clara Cohen, Sarah Arias, Carolina Leonetti, Manuel D. Jacobo, Adrian Balla, Keir Royer, Loïc A. Mehta, Shalin B. Optics Computer Vision and Pattern Recognition Quantitative Methods Correlative computational microscopy can accelerate imaging and modeling of cellular dynamics by relaxing trade-offs inherent to dynamic imaging. Existing computational microscopy frameworks are either specialized or overly generic, limiting use to fixed configurations or domain experts. We introduce WaveOrder, a generalist wave-optical framework for imaging the architectural order of biomolecules. WaveOrder reconstructs diverse specimen properties from multi-channel acquisitions, with or without fluorescence. It provides a unified representation of linear optical properties and differentiable physics-based image formation models spanning widefield, confocal, light-sheet, and oblique label-free geometries. WaveOrder uses physics-informed ML to auto-tune model parameters and solve blind shift-variant restoration problems. This open-source, PyTorch-based framework enables scalable quantitative imaging across scales from organelles to adult zebrafish, and improves restoration of cellular structures in high-throughput experiments. We validate WaveOrder on diverse imaging applications, demonstrating its ability to recover biomolecular structure beyond the limits of existing approaches. |
| title | WaveOrder: A differentiable wave-optical framework for scalable biological microscopy with diverse modalities |
| topic | Optics Computer Vision and Pattern Recognition Quantitative Methods |
| url | https://arxiv.org/abs/2412.09775 |