ArticleBiomedical optics express2022
HoloPhaseNet: fully automated deep-learning-based hologram reconstruction using a conditional generative adversarial model.
Article in Biomedical optics express, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- Digital in-line holographic microscopy for label-free identification and tracking of biological cells.Military Medical Research · 2024Review
- Nanoplastics in Water: Artificial Intelligence-Assisted 4D Physicochemical Characterization and Rapid In Situ Detection.Environmental science & technology · 2024Article
- On the use of deep learning for phase recovery.Light, science & applications · 2024Review
- Biological Cells as Natural Biophotonic Devices: Fundamental and Applications-introduction to the feature issue.Biomedical optics express · 2022Article
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Authors and funding
2 authors.
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Abstract
Quantitative phase imaging with off-axis digital holography in a microscopic configuration provides insight into the cells' intracellular content and morphology. This imaging is conventionally achieved by numerical reconstruction of the recorded hologram, which requires the precise setting of the reconstruction parameters, including reconstruction distance, a proper phase unwrapping algorithm, and component of wave vectors. This paper shows that deep learning can perform the complex light propagation task independent of the reconstruction parameters. We also show that the super-imposed twin-image elimination technique is not required to retrieve the quantitative phase image. The hologram at the single-cell level is fed into a trained image generator (part of a conditional generative adversarial network model), which produces the phase image. Also, the model's generalization is demonstrated by training it with holograms of size 512×512 pixels, and the resulting quantitative analysis is shown.
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Registered trials
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