ArticleBiology open2020
A fully automated deep learning pipeline for high-throughput colony segmentation and classification.
Article in Biology open, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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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.
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Who cites it
7 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
- Leveraging automated time-lapse microscopy coupled with deep learning to automate colony forming assay.Frontiers in oncology · 2025Article
- [PSI]-CIC: A Deep-Learning Pipeline for the Annotation of Sectored Saccharomyces cerevisiae Colonies.Bulletin of mathematical biology · 2024Article
- UMicroorganisms · 2024Article
- petiteFinder: an automated computer vision tool to compute Petite colony frequencies in baker's yeast.BMC bioinformatics · 2023Article
- An enhancer screen identifies new suppressors of small-RNA-mediated epigenetic gene silencing.PLoS genetics · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Adenine auxotrophy is a commonly used non-selective genetic marker in yeast research. It allows investigators to easily visualize and quantify various genetic and epigenetic events by simply reading out colony color. However, manual counting of large numbers of colonies is extremely time-consuming, difficult to reproduce and possibly inaccurate. Using cutting-edge neural networks, we have developed a fully automated pipeline for colony segmentation and classification, which speeds up white/red colony quantification 100-fold over manual counting by an experienced researcher. Our approach uses readily available training data and can be smoothly integrated into existing protocols, vastly speeding up screening assays and increasing the statistical power of experiments that employ adenine auxotrophy.
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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.