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.
Highlights
Cytokinesis block micronucleus (CBMN) assay is a widely used radiation biological dose estimation method
CBMN analysis is a way of producing binucleate cells by adding a proper quantity of cytochalasin B to the cell culture process, causing the nucleus to divide normally while the cytoplasm does not
Compared with dicentric chromosome detection, CBMN analysis is easier and faster, making it is more appropriate for rapid biological dosimetry in a large number of persons
Summary
Cytokinesis block micronucleus (CBMN) assay is a widely used radiation biological dose estimation method. 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. When employing imaging flow cytometers, only fluorescent dyes can be used to mark cell nuclei and micronucleus, and the detection method can be the conventional gating s trategy[27] or the deep learning CNN a lgorithm[28,29]. Is frequently favored when employing automatic microscopes[20,21] This algorithm uses a threshold algorithm to determine the location of the cell nucleus, the distance between each nucleus to determine the location and range of the cell, and the conventional gating strategy or a pre-trained convolutional neural network (CNN) model to determine the type of cell and the number of MNi
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