Quantification of cell viability by automated analysis of live cell imaging

Accurate assessment of cell viability is fundamental in biomedical research, with applications ranging from cancer biology to drug discovery. Traditional assays based on metabolic activity or membrane integrity are cost-effective but limited to endpoint measurements, often overlooking the dynamic nature of cell survival and death. Here, we present a workflow that combines live cell imaging with automated image analysis to provide continuous, unbiased quantification of cell viability. Using a fluorescent marker of membrane integrity and nuclear staining, time-lapse microscopy captures cell fate dynamics under diverse experimental conditions. Automated segmentation and classification, implemented through the open-source DIPlib library, enable reproducible distinction between viable and non-viable cells while minimizing observer bias. The method is scalable, adaptable to different imaging platforms and suitable for high-throughput applications.