ArticleScientific reports2021
Automating cell counting in fluorescent microscopy through deep learning with c-ResUnet.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- SAMJ: fast image annotation on ImageJ/Fiji via segment anything model.Nature communications · 2026Article
- A deterministic method for quantifying spindle-shaped cells in noisy bright-field microscopy.Scientific reports · 2026Article
- BESTDR Enables Bayesian Quantification of Mechanism-Specific Drug Responses.Cancer research · 2026Article
- Automated counting of prostate cell types with image processing and machine learning.PloS one · 2026Article
- Investigation of quantitative structural changes in astrocyte cells after laser-induced shockwave.Biophotonics discovery · 2025Article
- Perspectives: Comparison of deep learning segmentation models on biophysical and biomedical data.Biophysical journal · 2025Article
- Optimizing Colocalized Cell Counting Using Automated and Semiautomated Methods.Neuroinformatics · 2025Article
- CsDETC: detection and counting of small targetFrontiers in cellular and infection microbiology · 2025Article
- Emerging Trends and Technologies Used for the Identification, Detection, and Characterisation of Plant-Parasitic Nematode Infestation in Crops.Plants (Basel, Switzerland) · 2024Review
- Evaluating Cellularity Estimation Methods: Comparing AI Counting with Pathologists' Visual Estimates.Diagnostics (Basel, Switzerland) · 2024Article
- Improving the power of drug toxicity measurements by quantitative nuclei imaging.Cell death discovery · 2024Article
- PCP Auto Count: A Novel Fiji/ImageJ plug-in for automated quantification of planar cell polarity and cell counting.bioRxiv : the preprint server for biology · 2024Article
- Fluorescent Neuronal Cells v2: multi-task, multi-format annotations for deep learning in microscopy.Scientific data · 2024Article
- Counter-on-chip for bacterial cell quantification, growth, and live-dead estimations.Scientific reports · 2024Article
- PCP auto count: a novel Fiji/ImageJ plug-in for automated quantification of planar cell polarity and cell counting.Frontiers in cell and developmental biology · 2024Article
- YOLOv5-FPN: A Robust Framework for Multi-Sized Cell Counting in Fluorescence Images.Diagnostics (Basel, Switzerland) · 2023Article
- ACCT is a fast and accessible automatic cell counting tool using machine learning for 2D image segmentation.Scientific reports · 2023Article
- Article
- Cell segmentation in images without structural fluorescent labels.Biological imaging · 2023Article
- Gut Microbiota in Nutrition and Health with a Special Focus on Specific Bacterial Clusters.Cells · 2022Review
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
Counting cells in fluorescent microscopy is a tedious, time-consuming task that researchers have to accomplish to assess the effects of different experimental conditions on biological structures of interest. Although such objects are generally easy to identify, the process of manually annotating cells is sometimes subject to fatigue errors and suffers from arbitrariness due to the operator's interpretation of the borderline cases. We propose a Deep Learning approach that exploits a fully-convolutional network in a binary segmentation fashion to localize the objects of interest. Counts are then retrieved as the number of detected items. Specifically, we introduce a Unet-like architecture, cell ResUnet (c-ResUnet), and compare its performance against 3 similar architectures. In addition, we evaluate through ablation studies the impact of two design choices, (i) artifacts oversampling and (ii) weight maps that penalize the errors on cells boundaries increasingly with overcrowding. In summary, the c-ResUnet outperforms the competitors with respect to both detection and counting metrics (respectively, [Formula: see text] score = 0.81 and MAE = 3.09). Also, the introduction of weight maps contribute to enhance performances, especially in presence of clumping cells, artifacts and confounding biological structures. Posterior qualitative assessment by domain experts corroborates previous results, suggesting human-level performance inasmuch even erroneous predictions seem to fall within the limits of operator interpretation. Finally, we release the pre-trained model and the annotated dataset to foster research in this and related fields.
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