ArticleFEMS microbiology reviews2021
Advances and opportunities in image analysis of bacterial cells and communities.
Article in FEMS microbiology reviews, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 48 papers, 1 of them a synthesis that pooled it.
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
48 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Guideline
- Exploring the genetic landscape of ciprofloxacin-induced DNA supercompaction in Escherichia coli.Nucleic acids research · 2026Article
- Influence of cell-cell distance on the ecology and evolution of microbial communities.The ISME journal · 2026Review
- Automated microbial recognition from gram-stained micrographs via CNN and metadata decision support.Frontiers in cellular and infection microbiology · 2026Article
- TrackRefiner a tool for refinement of bacillus cell tracking data.NPJ systems biology and applications · 2025Article
- Low-Cost Microalgae Cell Concentration Estimation in Hydrochemistry Applications Using Computer Vision.Sensors (Basel, Switzerland) · 2025Article
- Dissecting the physics of bacterial biofilms with agent-based simulations.Current opinion in solid state & materials science · 2025Article
- Attention-driven UNet enhancement for accurate segmentation of bacterial spore outgrowth in microscopy images.Scientific reports · 2025Article
- Neural network-based image analysis of co-localized microorganisms and human cells on implant materials.Scientific reports · 2025Article
- OmniSegger: A time-lapse image analysis pipeline for bacterial cells.PLoS computational biology · 2025Article
- Deep generative modeling of annotated bacterial biofilm images.NPJ biofilms and microbiomes · 2025Article
- Enhancing AI microscopy for foodborne bacterial classification using adversarial domain adaptation to address optical and biological variability.Frontiers in artificial intelligence · 2025Article
- Grid partitioning image analysis of highly aggregative bacteriumFrontiers in microbiology · 2025Article
- PyUAT: An open-source Python framework for uncertainty-aware, efficient, and scalable model-driven cell tracking.PloS one · 2025Article
- Effect of shear rate on earlyBiofilm · 2024Article
- Quantification of bacterial shape using moment invariants enables distinguishing populations during cellular plasmolysis.MethodsX · 2024Article
- Staphylococcus epidermidis biofilm assembly and self-dispersion: bacteria and matrix dynamics.International microbiology : the official journal of the Spanish Society for Microbiology · 2024Article
- Article
- Machine learning for microbiologists.Nature reviews. Microbiology · 2024Review
- Tools and methods for high-throughput single-cell imaging with the mother machine.bioRxiv : the preprint server for biology · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The cellular morphology and sub-cellular spatial structure critically influence the function of microbial cells. Similarly, the spatial arrangement of genotypes and phenotypes in microbial communities has important consequences for cooperation, competition, and community functions. Fluorescence microscopy techniques are widely used to measure spatial structure inside living cells and communities, which often results in large numbers of images that are difficult or impossible to analyze manually. The rapidly evolving progress in computational image analysis has recently enabled the quantification of a large number of properties of single cells and communities, based on traditional analysis techniques and convolutional neural networks. Here, we provide a brief introduction to core concepts of automated image processing, recent software tools and how to validate image analysis results. We also discuss recent advances in image analysis of microbial cells and communities, and how these advances open up opportunities for quantitative studies of spatiotemporal processes in microbiology, based on image cytometry and adaptive microscope control.
Indexed as
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.