ArticleScientific reports2016
Image Quality Ranking Method for Microscopy.
Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 23 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
23 citing papers in PubMed.
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- Evolution of the PRNT: Merging tradition and innovation to set the gold standard in the era of automation.Human vaccines & immunotherapeutics · 2025Review
- Digital 3D exoscope is an effective tool for the surgery of falx and parasagittal meningiomas.Acta neurochirurgica · 2025Article
- Multi-marker Similarity Enables Reduced-Reference and Interpretable Image Quality Assessment in Optical Microscopy.Research (Washington, D.C.) · 2025Article
- Review and experimental comparison of speckle-tracking algorithms for X-ray phase contrast imaging.Journal of synchrotron radiation · 2025Article
- Single-shot isotropic differential interference contrast microscopy.Nature communications · 2023Article
- Multiplex Immunofluorescence Image Quality Checking Using DAPI Channel-referenced Evaluation.The journal of histochemistry and cytochemistry : official journal of the Histochemistry Society · 2023Article
- Glia Cell Morphology Analysis Using the Fiji GliaMorph Toolkit.Current protocols · 2023Article
- Fast intraoperative histology-based diagnosis of gliomas with third harmonic generation microscopy and deep learning.Scientific reports · 2022Article
- Optimization and Fabrication of Multi-Level Microchannels for Long-Term Imaging of Bacterial Growth and Expansion.Micromachines · 2022Article
- Shot noise limits on binary detection in multiphoton imaging.Biomedical optics express · 2021Article
- Deriving high contrast fluorescence microscopy images through low contrast noisy image stacks.Biomedical optics express · 2021Article
- Nonmechanical parfocal and autofocus features based on wave propagation distribution in lensfree holographic microscopy.Scientific reports · 2021Article
- Multi-focus Image Fusion for Confocal Microscopy Using U-Net Regression Map.Proceedings of the ... IAPR International Conference on Pattern Recognition. International Conference on Pattern Recognition · 2021Article
- An Affordable Image-Analysis Platform to Accelerate Stomatal Phenotyping During Microscopic Observation.Frontiers in plant science · 2021Article
- From leaf to label: A robust automated workflow for stomata detection.Ecology and evolution · 2020Article
- Two-photon image-scanning microscopy with SPAD array and blind image reconstruction.Biomedical optics express · 2020Article
- Phasetime: Deep Learning Approach to Detect Nuclei in Time Lapse Phase Images.Journal of clinical medicine · 2019Article
- Fourier ring correlation simplifies image restoration in fluorescence microscopy.Nature communications · 2019Article
- No-Reference Quality Assessment Method for Blurriness of SEM Micrographs with Multiple Texture.Scanning · 2019Article
Corrections and comments
- Erratum issued
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Automated analysis of microscope images is necessitated by the increased need for high-resolution follow up of events in time. Manually finding the right images to be analyzed, or eliminated from data analysis are common day-to-day problems in microscopy research today, and the constantly growing size of image datasets does not help the matter. We propose a simple method and a software tool for sorting images within a dataset, according to their relative quality. We demonstrate the applicability of our method in finding good quality images in a STED microscope sample preparation optimization image dataset. The results are validated by comparisons to subjective opinion scores, as well as five state-of-the-art blind image quality assessment methods. We also show how our method can be applied to eliminate useless out-of-focus images in a High-Content-Screening experiment. We further evaluate the ability of our image quality ranking method to detect out-of-focus images, by extensive simulations, and by comparing its performance against previously published, well-established microscopy autofocus metrics.
Identifiers
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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.