UPBAS-Net: An Upsampling-Powered Boundary-Aware Segmentation Network for Fluorescent Spots in Microscopy Images

Accurate detection and segmentation of fluorescent spots in microscopy cell images remain challenging. Traditional methods, based on centroid localization or pixel-wise semantic segmentation, often fail to delineate individual spot boundaries. This limitation significantly hinders the quantitative analysis of morphological heterogeneity and the interpretation of densely distributed subcellular signals. Here, we propose UPBAS-Net, a unified computational framework that integrates Fourier interpolation-based preprocessing with an enhanced YOLOv8 architecture incorporating an additional upsampling layer to improve shallow feature extraction and enable boundary-aware instance segmentation of fluorescent spots at subpixel resolution. It overcomes the limitations of traditional centroid localization and pixel-wise classification, enabling accurate delineation of spot boundaries. Experimental results show that UPBAS-Net achieves substantial improvements in spot localization accuracy, with F1-score gains up to 8.27% compared to the deepBlink model across multiple benchmark data sets. Furthermore, it demonstrates excellent scalability with the simultaneous segmentation of fluorescent spots and cellular boundaries, enabling integrated spatial correlation analysis at single-cell resolution. Additionally, we provide a user-friendly web-based analytical platform with containerized workflow management, enabling nonprogrammers to perform automated spot and cell segmentation using pretrained models. The platform is freely accessible at http://cellpropack.com/.