ArticleScientific reports2022
Rapid and automatic detection of micronuclei in binucleated lymphocytes image.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Deep learning and object detection methods for scoring cell types within the human buccal cell micronucleus and cytome assays for human biomonitoring.Mutagenesis · 2026Review
- Review
- An efficient cell micronucleus classification network based on multi-layer perception attention mechanism.Scientific reports · 2025Article
- micronuclAI enables automated quantification of micronuclei for assessment of chromosomal instability.Communications biology · 2025Article
- Application of image-recognition techniques to automated micronucleus detection in the in vitro micronucleus assay.Genes and environment : the official journal of the Japanese Environmental Mutagen Society · 2024Article
- Chromosomal Aberrations, Micronuclei, Blood Parameters and Received Doses in Workers Exposed to Ionizing Radiation.La Medicina del lavoro · 2023Article
- Reply to the opinion paper: is micronucleus test a suitable method for monitoring oral mucosa exposed to dental bleachings in smokers?Archives of toxicology · 2023Article
- Development of a micronucleus test using the EpiAirway™ organotypic human airway model.Genes and environment : the official journal of the Japanese Environmental Mutagen Society · 2023Article
- High-precision automatic identification method for dicentric chromosome images using two-stage convolutional neural network.Scientific reports · 2023Article
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Authors and funding
7 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Cytokinesis block micronucleus (CBMN) assay is a widely used radiation biological dose estimation method. However, the subjectivity and the time-consuming nature of manual detection limits CBMN for rapid standard assay. The CBMN analysis is combined with a convolutional neural network to create a software for rapid standard automated detection of micronuclei in Giemsa stained binucleated lymphocytes images in this study. Cell acquisition, adhesive cell mass segmentation, cell type identification, and micronucleus counting are the four steps of the software's analysis workflow. Even when the cytoplasm is hazy, several micronuclei are joined to each other, or micronuclei are attached to the nucleus, this algorithm can swiftly and efficiently detect binucleated cells and micronuclei in a verification of 2000 images. In a test of 20 slides, the software reached a detection rate of 99.4% of manual detection in terms of binucleated cells, with a false positive rate of 14.7%. In terms of micronuclei detection, the software reached a detection rate of 115.1% of manual detection, with a 26.2% false positive rate. Each image analysis takes roughly 0.3 s, which is an order of magnitude faster than manual detection.
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