Single-shot reconstruction of three-dimensional morphology of biological cells in digital holographic microscopy using a physics-driven neural network
Abstract Recent advances in deep learning-based image reconstruction techniques have led to significant progress in phase retrieval using digital in-line holographic microscopy (DIHM). However, existing phase retrieval methods have technical limitations in 3D morphology reconstruction from single-sh...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-05-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-025-60200-x |
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| _version_ | 1849769005575831552 |
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| author | Jihwan Kim Youngdo Kim Hyo Seung Lee Eunseok Seo Sang Joon Lee |
| author_facet | Jihwan Kim Youngdo Kim Hyo Seung Lee Eunseok Seo Sang Joon Lee |
| author_sort | Jihwan Kim |
| collection | DOAJ |
| description | Abstract Recent advances in deep learning-based image reconstruction techniques have led to significant progress in phase retrieval using digital in-line holographic microscopy (DIHM). However, existing phase retrieval methods have technical limitations in 3D morphology reconstruction from single-shot holograms of biological cells. In this study, we propose a deep learning model, named MorpHoloNet, for single-shot reconstruction of 3D morphology by integrating physics-driven and coordinate-based neural networks. By simulating optical diffraction of coherent light through a 3D phase shift distribution, MorpHoloNet is optimized by minimizing the loss between simulated and input holograms on the detector plane. MorpHoloNet enables direct reconstruction of 3D complex light field and 3D morphology of a test sample from its single-shot hologram without requiring multiple phase-shifted holograms or angular scanning. It would be utilized to reconstruct spatiotemporal variations in 3D translational and rotational behaviors, as well as morphological deformations of biological cells from consecutive single-shot holograms captured using DIHM. |
| format | Article |
| id | doaj-art-2279c1c635f44e3fb509f3366fbd8d40 |
| institution | DOAJ |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-2279c1c635f44e3fb509f3366fbd8d402025-08-20T03:03:37ZengNature PortfolioNature Communications2041-17232025-05-0116111510.1038/s41467-025-60200-xSingle-shot reconstruction of three-dimensional morphology of biological cells in digital holographic microscopy using a physics-driven neural networkJihwan Kim0Youngdo Kim1Hyo Seung Lee2Eunseok Seo3Sang Joon Lee4Department of Mechanical Engineering, Pohang University of Science and TechnologyDepartment of Mechanical Engineering, Pohang University of Science and TechnologyDepartment of Mechanical Engineering, Pohang University of Science and TechnologyDepartment of Mechanical Engineering, Sogang UniversityDepartment of Mechanical Engineering, Pohang University of Science and TechnologyAbstract Recent advances in deep learning-based image reconstruction techniques have led to significant progress in phase retrieval using digital in-line holographic microscopy (DIHM). However, existing phase retrieval methods have technical limitations in 3D morphology reconstruction from single-shot holograms of biological cells. In this study, we propose a deep learning model, named MorpHoloNet, for single-shot reconstruction of 3D morphology by integrating physics-driven and coordinate-based neural networks. By simulating optical diffraction of coherent light through a 3D phase shift distribution, MorpHoloNet is optimized by minimizing the loss between simulated and input holograms on the detector plane. MorpHoloNet enables direct reconstruction of 3D complex light field and 3D morphology of a test sample from its single-shot hologram without requiring multiple phase-shifted holograms or angular scanning. It would be utilized to reconstruct spatiotemporal variations in 3D translational and rotational behaviors, as well as morphological deformations of biological cells from consecutive single-shot holograms captured using DIHM.https://doi.org/10.1038/s41467-025-60200-x |
| spellingShingle | Jihwan Kim Youngdo Kim Hyo Seung Lee Eunseok Seo Sang Joon Lee Single-shot reconstruction of three-dimensional morphology of biological cells in digital holographic microscopy using a physics-driven neural network Nature Communications |
| title | Single-shot reconstruction of three-dimensional morphology of biological cells in digital holographic microscopy using a physics-driven neural network |
| title_full | Single-shot reconstruction of three-dimensional morphology of biological cells in digital holographic microscopy using a physics-driven neural network |
| title_fullStr | Single-shot reconstruction of three-dimensional morphology of biological cells in digital holographic microscopy using a physics-driven neural network |
| title_full_unstemmed | Single-shot reconstruction of three-dimensional morphology of biological cells in digital holographic microscopy using a physics-driven neural network |
| title_short | Single-shot reconstruction of three-dimensional morphology of biological cells in digital holographic microscopy using a physics-driven neural network |
| title_sort | single shot reconstruction of three dimensional morphology of biological cells in digital holographic microscopy using a physics driven neural network |
| url | https://doi.org/10.1038/s41467-025-60200-x |
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