High-precision automatic identification method for dicentric chromosome images using two-stage convolutional neural network

Abstract

Dicentric chromosome analysis is the gold standard for biological dose assessment. To enhance the efficiency of biological dose assessment in large-scale radiation catastrophes, automatic identification of dicentric chromosome images is a promising and objective method. In this paper, an automatic identification method for dicentric chromosome images using two-stage convolutional neural network is proposed based on Giemsa-stained automatic microscopic imaging. To automatically segment the adhesive chromosome masses, a k-means based adaptive image segmentation and watershed segmentation algorithm is applied. The first-stage CNN is used to identify the dicentric chromosome images from all the images and the second-stage CNN works to specifically identify the dicentric chromosome images. This two-stage CNN identification method can effectively detects chromosome images with concealed centromeres, poorly expanded and long-armed entangled chromosomes, and tricentric chromosomes. The novel two-stage CNN method has a chromosome identification accuracy of 99.4%, a sensitivity of 85.8% sensitivity, and a specificity of 99.6%, effectively reducing the false positive rate of dicentric chromosome. The analysis speed of this automatic identification method can be 20 times quicker than manual detection, providing a valuable reference for other image identification situations with small target rates.

Multiwell-based G0-PCC assay for radiation biodosimetry

“…; https://doi.org/10.1101/2024.05. 27.596074 doi: bioRxiv preprint just CDK1/Cyclin B [18][19][20]. Unlike fusion-induced G0-PCC, which directly supplies resting cells with all necessary kinases, chemically-induced G0-PCC fails to introduce this diverse array of mitotic factors into cells.…”

“…The use of CNN for radiation biodosimetry is gaining recognition [26][27][28]. CNNs are frequently employed for image analysis tasks, such as identification of objects and human faces, classification of images based on their content, and detection of diseases in medical images.…”