ArticleNature communications2020
Non-invasive single-cell morphometry in living bacterial biofilms.
Article in Nature communications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
What it found
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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
26 citing papers in PubMed.
- 3D imaging with enhanced transparency, signal-to-background ratios, and antigen detection using HyPer-3D.Cell reports methods · 2026Article
- Understanding the development of bacterial colony: Physiology, new technology, and modeling.Quantitative biology (Beijing, China) · 2025Review
- Pairwise encounters boost bacterial motion by transient velocity spikes.Biophysical journal · 2025Article
- Neural network-based image analysis of co-localized microorganisms and human cells on implant materials.Scientific reports · 2025Article
- Segmentation of dense and multi-species bacterial colonies using models trained on synthetic microscopy images.PLoS computational biology · 2025Article
- Resolving spatiotemporal dynamics in bacterial multicellular populations: approaches and challenges.Microbiology and molecular biology reviews : MMBR · 2025Review
- Immobilization and Monitoring ofMicroorganisms · 2025Article
- Deep generative modeling of annotated bacterial biofilm images.NPJ biofilms and microbiomes · 2025Article
- Biofilm-mediated immune dysregulation in chronic pulmonary diseases: mechanisms and clinical implications.Frontiers in microbiology · 2025Review
- DeepSeeded: Volumetric Segmentation of Dense Cell Populations with a Cascade of Deep Neural Networks in Bacterial Biofilm Applications.Expert systems with applications · 2024Article
- High content quantitative imaging of Mycobacterium tuberculosis responses to acidic microenvironments within human macrophages.FEBS open bio · 2023Article
- Review
- Advances in linking single-cell bacterial stress response to population-level survival.Current opinion in biotechnology · 2023Review
- BCM3D 2.0: accurate segmentation of single bacterial cells in dense biofilms using computationally generated intermediate image representations.NPJ biofilms and microbiomes · 2022Article
- Synthetic Micrographs of Bacteria (SyMBac) allows accurate segmentation of bacterial cells using deep neural networks.BMC biology · 2022Article
- 3D GAN image synthesis and dataset quality assessment for bacterial biofilm.Bioinformatics (Oxford, England) · 2022Article
- DeepBacs for multi-task bacterial image analysis using open-source deep learning approaches.Communications biology · 2022Article
- Microbial Biofilms: Structural Plasticity and Emerging Properties.Microorganisms · 2022Article
- A Data-Efficient Framework for the Identification of Vaginitis Based on Deep Learning.Journal of healthcare engineering · 2022Article
- Optically Accessible Microfluidic Flow Channels for Noninvasive High-Resolution Biofilm Imaging Using Lattice Light Sheet Microscopy.The journal of physical chemistry. B · 2021Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Fluorescence microscopy enables spatial and temporal measurements of live cells and cellular communities. However, this potential has not yet been fully realized for investigations of individual cell behaviors and phenotypic changes in dense, three-dimensional (3D) bacterial biofilms. Accurate cell detection and cellular shape measurement in densely packed biofilms are challenging because of the limited resolution and low signal to background ratios (SBRs) in fluorescence microscopy images. In this work, we present Bacterial Cell Morphometry 3D (BCM3D), an image analysis workflow that combines deep learning with mathematical image analysis to accurately segment and classify single bacterial cells in 3D fluorescence images. In BCM3D, deep convolutional neural networks (CNNs) are trained using simulated biofilm images with experimentally realistic SBRs, cell densities, labeling methods, and cell shapes. We systematically evaluate the segmentation accuracy of BCM3D using both simulated and experimental images. Compared to state-of-the-art bacterial cell segmentation approaches, BCM3D consistently achieves higher segmentation accuracy and further enables automated morphometric cell classifications in multi-population biofilms.
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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.