Ionizing radiation-induced repair foci identification using deep convolutional neural networks

DNA double-strand breaks (DSBs), visualized as ionizing radiation-induced repair foci (IRIFs), represent a severe form of DNA damage and are central to radiation biology, cancer research, and space health. Quantifying IRIF induction and repair is critical for biodosimetry but is traditionally performed using approaches sensitive to image variability. This study developed a robust and scalable method for accurate IRIF detection and quantification using deep learning. A modified U-Net architecture was employed for semantic segmentation of IRIFs in fluorescence microscopy images. The model was trained on 95,000 synthetically generated image-mask pairs designed to replicate signal, noise, and morphological characteristics of real microscopy data. Model performance was evaluated both on synthetic data and on a real-world dataset of 345 manually annotated mouse fibroblast images from the NASA GeneLab OSD-366 experiment, encompassing multiple radiation types and doses. Segmentation and foci detection performance were assessed using Dice coefficient, Intersection over Union (IoU), precision and recall. On synthetic test data, the model achieved a mean Dice coefficient of 0.995 and IoU of 0.993. On real-world data, Dice coefficients ranged from 0.72 to 0.88 on average across radiation categories. Foci detection demonstrated high sensitivity (0.95–0.98) and precision (0.82–0.97), with overall recall of 0.97 and precision of 0.89, indicating robust detection with limited false negatives. This work demonstrates that training on synthetic data enables reliable generalization to real radiation biology images. The proposed approach improves reproducibility and robustness,and may support high-throughput biodosimetry, with potential implications for radiation protection, personalized medicine, and astronaut health assessment, pending further validation across additional time points, cell types, and data sources.