An object detection-based model for automated screening of stem-cells senescence during drug screening

Deep learning-based cell senescence detection is crucial for accurate quantitative analysis of senescence assessment. However, senescent cells are small in size and have little differences in appearance and shape in different states, which leads to insensitivity problems such as missed and false detection. In addition, complex intelligent models are not conducive to clinical application. Therefore, to solve the above problems, we proposed a Faster Region Convolutional Neural Network (Faster R-CNN) detection model with Swin Transformer (Swin-T) and group normalization (GN), called STGF R-CNN, for the detection of different senescent cells to achieve quantification assessment of induced pluripotent stem cell-derived mesenchymal stem cells (iP-MSCs) senescence. Specifically, to enhance the representation learning ability of the network, Swin-T with a hierarchical structure was constructed. It utilizes a local window attention mechanism to capture features of different scales and levels. In addition, the GN strategy is adopted to achieve a lightweight model. To verify the effectiveness of the STGF R-CNN, a cell senescence dataset, the iP-MSCs dataset, was constructed, and a series of experiments were conducted. Experiment results show that it has the advantage of high senescent detection accuracy, mean Average Precision (mAP) is 0.835, Params is 46.06M, and FLOPs is 95.62G, which significantly reduces senescent assessment time from 12 h to less than 1 s. The STGF R-CNN has advantages over existing cell senescence detection methods, providing potential for anti-senescent drug screening. Our code is available at https://github.com/RY-97/STGF-R-CNN.