Segmenting individual cells from microscopy images is crucial for quantitative biological data extraction. While supervised machine learning (SL) methods have been used, they require extensive pre-processing with human-annotated labels, which is time-consuming, introduces bias, and lacks generalizability. This paper introduces a self-supervised learning (SSL) approach using cellular motion between consecutive images to train a classifier for cell and background segmentation. This method eliminates adjustable parameters, is modality-independent, outperforms existing SL methods, and is fully automated, thus reducing user variability and bias. The SSL algorithm is a novel approach with promising features for broader adoption in cell biology research.

Michael C. Robitaille, Jeff M. Byers, Joseph A. Christodoulides, Marc P. Raphael