ArticleNature communications2021
Sub-diffraction error mapping for localisation microscopy images.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed.
- Self-adaptive fine-tuning of deep learning super-resolution microscopy for artifact suppression in live-cell imaging.Innovation (Cambridge (Mass.)) · 2026Article
- DHPSFU: a Fiji plugin for fast and accurate double helix-PSF 3D single-molecule localisation microscopy.Scientific reports · 2025Article
- Resolution in super-resolution microscopy - facts, artifacts, technological advancements and biological applications.Journal of cell science · 2025Article
- Mapping super-resolution image quality.Light, science & applications · 2024Article
- Quantitatively mapping local quality of super-resolution microscopy by rolling Fourier ring correlation.Light, science & applications · 2023Article
- An integrated platform for high-throughput nanoscopy.Nature biotechnology · 2023Article
- Single-frame deep-learning super-resolution microscopy for intracellular dynamics imaging.Nature communications · 2023Article
- Quantitative Single-Molecule Localization Microscopy.Annual review of biophysics · 2023Review
- Cluster analysis for localisation-based data sets: dos and don'ts when quantifying protein aggregates.Frontiers in bioinformatics · 2023Review
- Fluorescence fluctuation-based super-resolution microscopy: Basic concepts for an easy start.Journal of microscopy · 2022Review
- Fast DNA-PAINT imaging using a deep neural network.Nature communications · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
Assessing the quality of localisation microscopy images is highly challenging due to the difficulty in reliably detecting errors in experimental data. The most common failure modes are the biases and errors produced by the localisation algorithm when there is emitter overlap. Also known as the high density or crowded field condition, significant emitter overlap is normally unavoidable in live cell imaging. Here we use Haar wavelet kernel analysis (HAWK), a localisation microscopy data analysis method which is known to produce results without bias, to generate a reference image. This enables mapping and quantification of reconstruction bias and artefacts common in all but low emitter density data. By avoiding comparisons involving intensity information, we can map structural artefacts in a way that is not adversely influenced by nonlinearity in the localisation algorithm. The HAWK Method for the Assessment of Nanoscopy (HAWKMAN) is a general approach which allows for the reliability of localisation information to be assessed.
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.