ArticleScientific reports2020
Deep learning method for comet segmentation and comet assay image analysis.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 11 papers.
What it found
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Who cites it
11 citing papers in PubMed, 30 citations in OpenAlex.
- AIComet: a reliable automated scoring program for DNA damage assessment in the comet assay.Archives of toxicology · 2026Article
- AI-based methods for the assessment of DNA damage and repair mechanisms.Frontiers in systems biology · 2026Review
- Artificial Intelligence and Machine Learning-Based Approaches for Genetic Damage Prediction.Methods in molecular biology (Clifton, N.J.) · 2026Article
- The Ku80-p53-SIRT1 axis in DNA damage response contributes to sporadic and familial ALS and FTD.Nature communications · 2025Article
- Concentrated Growth Factor Induces ER Stress and Apoptosis by Increasing Ceramide Generation in Selected Tumour Cell Lines.Journal of cellular and molecular medicine · 2025Article
- The anticancer activity of fucoidan coated selenium nanoparticles and curcumin nanoparticles against colorectal cancer lines.Scientific reports · 2025Article
- Article
- GamaComet: A Deep Learning-Based Tool for the Detection and Classification of DNA Damage from Buccal Mucosa Comet Assay Images.Diagnostics (Basel, Switzerland) · 2022Article
- Article
- Segmentation-based analysis of single-cell immunoblots.Electrophoresis · 2021Article
- Genotoxicity of chloroacetamide herbicides and their metabolitesInternational journal of molecular medicine · 2021Article
Corrections and comments
- Erratum issued
Authors and funding
11 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Comet assay is a widely used method, especially in the field of genotoxicity, to quantify and measure DNA damage visually at the level of individual cells with high sensitivity and efficiency. Generally, computer programs are used to analyze comet assay output images following two main steps. First, each comet region must be located and segmented, and next, it is scored using common metrics (e.g., tail length and tail moment). Currently, most studies on comet assay image analysis have adopted hand-crafted features rather than the recent and effective deep learning (DL) methods. In this paper, however, we propose a DL-based baseline method, called DeepComet, for comet segmentation. Furthermore, we created a trainable and testable comet assay image dataset that contains 1037 comet assay images with 8271 manually annotated comet objects. From the comet segmentation test results with the proposed dataset, the DeepComet achieves high average precision (AP), which is an essential metric in image segmentation and detection tasks. A comparative analysis was performed between the DeepComet and the state-of-the-arts automatic comet segmentation programs on the dataset. Besides, we found that the DeepComet records high correlations with a commercial comet analysis tool, which suggests that the DeepComet is suitable for practical application.
Identifiers
What OpenQuestion holds
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.