Automated bone cell classification for confocal laser scanning microscopy volumes.

Manual cell classification in microscopy images is a time-consuming process that heavily relies on the subjective perception of the investigator. Identifying bone cells introduces additional difficulties with irregular geometries, and in some culture conditions, the presence of bone mineral. As fluorescence-based lineage tracing becomes more common, classifying cell types based upon cell color can further increase subjectivity. Our goal is to develop and validate a fully automated cell classification algorithm that can (i) objectively identify cells in flattened volumetric image stacks from three-dimensional (3D) bone cell cultures and (ii) classify the cells (osteoblast-lineage) based on the color of their cell bodies. The algorithm used here was developed in MATLAB 2019a and validated by comparing code outputs to manual labeling for eleven images. The precision, recall, and F1 scores were higher than 0.75 for all cell classifications, with the majority being greater than 0.80. No significant differences were found between the manually labelled and automated cell counts or cell classifications. Analysis time for a single image averaged seventeen seconds compared to more than ten minutes for manual labeling. This demonstrates that the program offers a fast, repeatable, and accurate way to classify bone cells by fluorescence in confocal microscopy image data sets. This process can be expanded to improve investigation of other pre-clinical models and histological sections of pathological tissues where color or fluorescence-based differences are used for cell identification.