Bo-Net: Deep learning-based model for automatic bone stromal cell segmentation of fluorescence microscopy images

Background and objectives: Bone is a multicellular organ that is the site of complex pathophysiological events (such as cancer). 3D confocal and multiphoton microscopy, followed by manual analysis and quantification, allow the identification of molecular, cellular, and tissue mechanisms in their original context, enabling the investigation of spatial biology at subcellular level. The automation of these analyses is becoming increasingly important due to their time-consuming nature, lack of standardization, and inter-subject variability. Methods: Deep learning applied to image analysis could overcome current limitations and optimize preclinical research. This includes automatic semantic segmentation of bone cells (osteoblasts, osteoclasts, and blood vessels) and mineral component, followed by parameter extraction and quantification. Accordingly, fluorescence microscopy images were generated, pre-processed, and fed (total 21,395 images: 17,104 for training, 4291 for validation) into a neural network-based architecture named Bo-Net. Results: The fully automatic Bo-Net achieved strong performance across the tested metrics and segmentation accuracy similar to experienced biologists (R = 0.81–0.99 for parameters tested) and improved analysis time from days to seconds in a variety of experimental conditions, confirming its relevance in different biologically relevant contexts. Conclusions: As a result, a fully automated, yet reliable, tool to optimize the analysis of multiparametric fluorescence microscopy images available to the bone research community was generated.