ArticleCommunications biology2022
DeepBacs for multi-task bacterial image analysis using open-source deep learning approaches.
Article in Communications biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 52 papers, 1 of them a synthesis that pooled it.
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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
52 citing papers in PubMed, 1 synthesis or guideline pooled it, 105 citations in OpenAlex.
- AI-Enabled Imaging for Pathogen Detection Under Stress Conditions: A Systematic Review.Comprehensive reviews in food science and food safety · 2026Pooled it
- Microbial Colony Image Enhancement Algorithm Based on Composite Adversarial D-DCGAN Neural Network.Microorganisms · 2026Article
- Neural Network-Based System for Rapid Diagnostic Decision Support Using FLIM Data: Segmentation and Classification in Liver, Skin, and Pancreatic Tissues.International journal of molecular sciences · 2026Article
- AI-empowered super-resolution microscopy: a revolution in nanoscale cellular imaging.Nature methods · 2026Review
- Microscopy Cell Segmentation: Review and Benchmarking of Task-Specific and Foundation Models.Journal of imaging · 2026Review
- SAMJ: fast image annotation on ImageJ/Fiji via segment anything model.Nature communications · 2026Article
- Overcoming resolution constraints in automated colony counting via a high-performance deep learning framework using SAHI.Scientific reports · 2026Article
- Deep-learning deconvolution and segmentation of fluorescent membranes for high-precision bacterial cell-size profiling.Communications biology · 2026Article
- Development of Human Conjunctival Goblet Cell Segmentation Datasets to Improve Quantitation.Scientific data · 2026Article
- Synthetic data enables human-grade microtubule analysis with foundation models for segmentation.PLoS computational biology · 2026Article
- Immortal AI, Mortal Life: Long-Term Perspectives on AI and Human Knowledge.Journal of medical Internet research · 2026Article
- Review
- A foundation model for multi-task cross-distribution restoration of fluorescence microscopy images.Nature communications · 2026Article
- Molecular basis for anti-jumbo phage immunity by AVAST type 5.Molecular cell · 2026Article
- Novel convolutional neural network for bacterial identification of confocal microscopic datasets.Scientific reports · 2026Article
- A comparison of super-resolution microscopy techniques for imaging tightly packed microcolonies of an obligate intracellular bacterium.Journal of microscopy · 2026Article
- DeBCR: a sparsity-efficient framework for image enhancement through a deep-learning-based solution to inverse problems.Communications engineering · 2026Article
- Systematic evaluation of computational methods for cell segmentation.Briefings in bioinformatics · 2026Article
- mAIcrobe: an open-source framework for high-throughput bacterial image analysis.Communications AI & computing · 2026Article
- Bactericidal membrane attack complex formation initiates at the new pole of E. coli.EMBO reports · 2026Article
Corrections and comments
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Authors and funding
11 authors at 7 institutions in 4 countries.
Funding
Abstract
This work demonstrates and guides how to use a range of state-of-the-art artificial neural-networks to analyse bacterial microscopy images using the recently developed ZeroCostDL4Mic platform. We generated a database of image datasets used to train networks for various image analysis tasks and present strategies for data acquisition and curation, as well as model training. We showcase different deep learning (DL) approaches for segmenting bright field and fluorescence images of different bacterial species, use object detection to classify different growth stages in time-lapse imaging data, and carry out DL-assisted phenotypic profiling of antibiotic-treated cells. To also demonstrate the ability of DL to enhance low-phototoxicity live-cell microscopy, we showcase how image denoising can allow researchers to attain high-fidelity data in faster and longer imaging. Finally, artificial labelling of cell membranes and predictions of super-resolution images allow for accurate mapping of cell shape and intracellular targets. Our purposefully-built database of training and testing data aids in novice users' training, enabling them to quickly explore how to analyse their data through DL. We hope this lays a fertile ground for the efficient application of DL in microbiology and fosters the creation of tools for bacterial cell biology and antibiotic research.
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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.