ReviewCurrent opinion in cell biology2023
Live-cell imaging in the deep learning era.
Review in Current opinion in cell biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 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
28 citing papers in PubMed.
- A deep learning approach to assess transendothelial cell trafficking performance.Scientific reports · 2026Article
- Review
- AI-Driven Innovations for Quality Control and Standardization: Future Strategies in Adipose-Derived Stem Cell Manufacturing.International journal of molecular sciences · 2026Review
- Review
- A multicellular analysis calcium imaging toolbox for ImageJ.Cell reports methods · 2026Article
- Beyond the membrane: rethinking EGFR signaling in physiology and cancer.Cellular and molecular life sciences : CMLS · 2026Review
- Simulation-based inference of cell migration dynamics in complex spatial environments.NPJ systems biology and applications · 2026Article
- Article
- Comparison of Deep Learning Approaches for Extreme Low-SNR Image Restoration.bioRxiv : the preprint server for biology · 2026Article
- Leveraging AI for cell biology discovery.Biochemical Society transactions · 2026Review
- Discovering the unexpected: Insights into the dynamics of mouse neural tube closure revealed by time-lapse imaging.Developmental biology · 2026Review
- Self-Driving Microscopes: AI Meets Super-Resolution Microscopy.Small methods · 2026Review
- Label-free TIMING: an efficient, reliable and scalable AI workflow for automated profiling of cell-cell interaction behaviors in nanowell arrays.Frontiers in bioinformatics · 2026Article
- Contrastive learning for cell division detection and tracking in live cell imaging data.BMC bioinformatics · 2025Article
- Closed-loop optogenetic control of cell biology enables outcome-driven microscopy.Nature communications · 2025Article
- Volumetric localization microscopy with deep learning.Nature communications · 2025Article
- Towards a unified framework for the function of endoplasmic reticulum exit sites.Nature reviews. Molecular cell biology · 2025Review
- EIciRNAs in focus: current understanding and future perspectives.RNA biology · 2025Review
- AIstain: Enhancing microglial phagocytosis analysis through deep learning.Cell reports methods · 2025Article
- Time, the final frontier.Molecular oncology · 2025Article
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.
Funding
Abstract
Live imaging is a powerful tool, enabling scientists to observe living organisms in real time. In particular, when combined with fluorescence microscopy, live imaging allows the monitoring of cellular components with high sensitivity and specificity. Yet, due to critical challenges (i.e., drift, phototoxicity, dataset size), implementing live imaging and analyzing the resulting datasets is rarely straightforward. Over the past years, the development of bioimage analysis tools, including deep learning, is changing how we perform live imaging. Here we briefly cover important computational methods aiding live imaging and carrying out key tasks such as drift correction, denoising, super-resolution imaging, artificial labeling, tracking, and time series analysis. We also cover recent advances in self-driving microscopy.
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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.