ArticleBiomedical optics express2019
No-search focus prediction at the single cell level in digital holographic imaging with deep convolutional neural network.
Article in Biomedical optics express, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
9 citing papers in PubMed, 41 citations in OpenAlex.
- AI-driven digital holographic microscopy for label-free quantitative cellular analysis: toward low-cost and field-deployable platforms.Biomedical optics express · 2026Review
- Establishing a reference focal plane using convolutional neural networks and beads for brightfield imaging.Scientific reports · 2024Article
- On the use of deep learning for phase recovery.Light, science & applications · 2024Review
- Advances in Digital Holographic Interferometry.Journal of imaging · 2022Review
- HoloPhaseNet: fully automated deep-learning-based hologram reconstruction using a conditional generative adversarial model.Biomedical optics express · 2022Article
- Deep learning in optical metrology: a review.Light, science & applications · 2022Review
- Comprehensive deep learning model for 3D color holography.Scientific reports · 2022Article
- Classification of unlabeled cells using lensless digital holographic images and deep neural networks.Quantitative imaging in medicine and surgery · 2021Article
- Automated single cardiomyocyte characterization by nucleus extraction from dynamic holographic images using a fully convolutional neural network.Biomedical optics express · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Digital propagation of an off-axis hologram can provide the quantitative phase-contrast image if the exact distance between the sensor plane (such as CCD) and the reconstruction plane is correctly provided. In this paper, we present a deep-learning convolutional neural network with a regression layer as the top layer to estimate the best reconstruction distance. The experimental results obtained using microsphere beads and red blood cells show that the proposed method can accurately predict the propagation distance from a filtered hologram. The result is compared with the conventional automatic focus-evaluation function. Additionally, our approach can be utilized at the single-cell level, which is useful for cell-to-cell depth measurement and cell adherent studies.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.