Application Note: Enabling Accurate Nuclei Detection
Last updated: November 2025
Overview
Introducing an innovative method for analyzing nuclei in live-cell imaging without the need for fluorescent labeling. Traditional methods often rely on fluorescent stains, which can be cytotoxic and unsuitable for live-cell applications. This application note details the development of the AI Nuclei Detection (AIND) model, trained to segment nuclei in phase-contrast images across various cell types.
This approach allows researchers to conduct real-time analysis of cellular behaviors, such as proliferation and differentiation, while maintaining cell viability. The AIND model has been validated against traditional fluorescent counting methods, showing strong correlation and enhanced accuracy, particularly in high-confluence conditions. The document also outlines the cell culture media and materials used in the experiments, emphasizing the role of the Incucyte® Live-Cell Analysis System in monitoring cell growth. This label-free analysis offers valuable insights into biological processes and cell morphology in a non-invasive manner.
- Document type: Application Note
- Page count: 8
- Read time: 15 minutes
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Key Takeaways
- AI Nuclei Detection enables label-free nucleus segmentation in live-cell imaging
- Preserve cell viability while analyzing proliferation and differentiation
- Validated against fluorescent counting, demonstrating strong correlation and improved accuracy
- Incucyte® systems for real-time monitoring of cell growth and morphology
Preview Image: Validation of AIND Segmentation
Figure 1: Time courses display label-free (grey) and fluorescence (teal) nucleus count for HT1080, MCF7, MDA-MB-231 and SKOV3 cell types (A). Phase-contrast images are overlaid with fluorescent nuclei (orange channel) and label-free nuclei mask (yellow outline; row B). Fluorescent nuclei display high correlation with label-free nuclei mask (row C).
