ArticleBiomedical optics express2020
Full-color optically-sectioned imaging by wide-field microscopy via deep-learning.
Article in Biomedical optics express, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
3 citing papers in PubMed, 20 citations in OpenAlex.
- A practical guide to light-sheet microscopy for nanoscale imaging: Looking beyond the cell.Journal of applied physics · 2024Article
- Optical tomography in a single camera frame using fringe-encoded deep-learning full-field OCT.Biomedical optics express · 2024Article
- Content aware multi-focus image fusion for high-magnification blood film microscopy.Biomedical optics express · 2022Article
Corrections and comments
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Authors and funding
8 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Wide-field microscopy (WFM) is broadly used in experimental studies of biological specimens. However, combining the out-of-focus signals with the in-focus plane reduces the signal-to-noise ratio (SNR) and axial resolution of the image. Therefore, structured illumination microscopy (SIM) with white light illumination has been used to obtain full-color 3D images, which can capture high SNR optically-sectioned images with improved axial resolution and natural specimen colors. Nevertheless, this full-color SIM (FC-SIM) has a data acquisition burden for 3D-image reconstruction with a shortened depth-of-field, especially for thick samples such as insects and large-scale 3D imaging using stitching techniques. In this paper, we propose a deep-learning-based method for full-color WFM,
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Registered trials
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