ArticleVisual computing for industry, biomedicine, and art2022
Machine learning for enumeration of cell colony forming units.
Article in Visual computing for industry, biomedicine, and art, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Colony-YOLO: A Lightweight Micro-Colony Detection Network Based on Improved YOLOv8n.Microorganisms · 2025Article
- C-COUNT: a convolutional neural network-based tool for automated scoring of erythroid colonies.Experimental hematology · 2025Article
- Synergistic enhancement of polycyclic aromatic hydrocarbon degradation by Arthrobacter sp. SZ-3 and Pseudomonas putida B6-2 under high Tween80 concentration: mechanisms and efficiency.International microbiology : the official journal of the Spanish Society for Microbiology · 2025Article
- Article
- Leveraging automated time-lapse microscopy coupled with deep learning to automate colony forming assay.Frontiers in oncology · 2025Article
- An Indirect Fluorescence Microscopy Method to Assess Vaginal Lactobacillus Concentrations.Microorganisms · 2024Article
- Hybrid Approach to Colony-Forming Unit Counting Problem Using Multi-Loss U-Net Reformulation.Sensors (Basel, Switzerland) · 2023Article
- Annotated dataset for deep-learning-based bacterial colony detection.Scientific data · 2023Article
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Authors and funding
1 author.
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Abstract
As one of the most widely used assays in biological research, an enumeration of the bacterial cell colonies is an important but time-consuming and labor-intensive process. To speed up the colony counting, a machine learning method is presented for counting the colony forming units (CFUs), which is referred to as CFUCounter. This cell-counting program processes digital images and segments bacterial colonies. The algorithm combines unsupervised machine learning, iterative adaptive thresholding, and local-minima-based watershed segmentation to enable an accurate and robust cell counting. Compared to a manual counting method, CFUCounter supports color-based CFU classification, allows plates containing heterologous colonies to be counted individually, and demonstrates overall performance (slope 0.996, SD 0.013, 95%CI: 0.97-1.02, p value < 1e-11, r = 0.999) indistinguishable from the gold standard of point-and-click counting. This CFUCounter application is open-source and easy to use as a unique addition to the arsenal of colony-counting tools.
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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.