APPLICATION NOTE
AI-enabled label free detection with IN Carta Image Analysis Software
Discover how AI-powered, label-free imaging preserves cell health while delivering precise, high-throughput cellular analysis.
In this application note, IN Carta® Image Analysis Software leverages AI-enabled, label-free detection to deliver accurate, non-invasive cellular analysis across multiple cell types.
This advanced approach for live-cell imaging eliminates the need for fluorescent dyes—preserving cell viability while accelerating data acquisition.
By training deep learning segmentation models (SINAP) on transmitted light images, researchers can achieve over 97% accuracy in nuclei and whole-cell detection. When applied to compound screening, this method demonstrates strong correlation with traditional fluorescence-based assays, confirming its reliability for high-throughput and phenotypic screening.
The result is a scalable, cost-efficient workflow that provides real-time biological insight while simplifying imaging and analysis.
