Dataset Information

Rapid and automatic detection of micronuclei in binucleated lymphocytes image.

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.

SUBMITTER: Shen X

PROVIDER: S-EPMC8913785 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Rapid and automatic detection of micronuclei in binucleated lymphocytes image.

Shen Xiang X Chen Ying Y Li Chaowen C Yang Fucheng F Wen Zhanbo Z Zheng Jinlin J Zhou Zhenggan Z

Scientific reports 20220310 1

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 ...[more]

PMID: 35273270

Similar Datasets

Project description:Underwater video monitoring systems are being widely used in fisheries to investigate fish behavior in relation to fishing gear and fishing gear performance during fishing. Such systems can be useful to evaluate the catch composition as well. In demersal trawl fisheries, however, their applicability can be challenged by low light conditions, mobilized sediment and scattering in murky waters. In this study, we introduce a novel observation system (called NepCon) which aims at reducing current limitations by combining an optimized image acquisition setup and tailored image analyses software. The NepCon system includes a high-contrast background to enhance the visibility of the target objects, a compact camera and an artificial light source. The image analysis software includes a machine learning algorithm which is evaluated here to test automatic detection and count of Norway lobster (Nephrops norvegicus). NepCon is specifically designed for applications in demersal trawls and this first phase aims at increasing the accuracy of N. norvegicus detection at the data acquisition level. To find the best contrasting background for the purpose we compared the output of four image segmentation methods applied to static images of N. norvegicus fixed in front of four test background colors. The background color with the best performance was then used to evaluate computer vision and deep learning approaches for automatic detection, tracking and counting of N. norvegicus in the videos. In this initial phase we tested the system in an experimental setting to understand the feasibility of the system for future implementation in real demersal fishing conditions. The N. norvegicus directed trawl fishery typically has no assistance from underwater observation technology and therefore are largely conducted blindly. The demonstrated perception system achieves 76% accuracy (F-score) in automatic detection and count of N. norvegicus, which provides a significant elevation of the current benchmark.

Dataset Information

Rapid and automatic detection of micronuclei in binucleated lymphocytes image.

Publications

Rapid and automatic detection of micronuclei in binucleated lymphocytes image.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets