SCIENTIFIC POSTER
Label-free cell imaging and analysis using transmitted light and deep learning
Discover how transmitted-light imaging combined with deep learning enables accurate label-free cell and nuclear detection. Achieve >90% detection rates across multiple cell lines while simplifying workflows and reducing reagent costs.
Advancing Label-Free Cell Imaging with AI-Driven Analysis
Poster: Label-Free Cell Imaging and Deep Learning Analysis Using Transmitted-Light Imaging
This scientific poster demonstrates how transmitted-light imaging combined with deep learning models enables accurate, label-free cell segmentation and phenotypic screening for high-content assays.
In this poster, you will learn how researchers:
- Perform label-free cell and nuclear segmentation directly from transmitted-light images
- Achieve >90% detection accuracy across multiple cell lines using AI-trained models
- Train customizable AI models in IN Carta® Image Analysis Software using cross-channel supervision
- Reduce dependence on fluorescent dyes while preserving cell viability and enabling real-time monitoring
- Generate dose-response curves and IC₅₀ values comparable to fluorescence-based methods
- Streamline high-content screening workflows with automated imaging and AI-driven analysis
Download the poster to see how deep learning–enabled label-free imaging can simplify cell-based assays while improving analysis accuracy, throughput, and experimental efficiency.
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Download the Poster on Label-Free Cell Imaging
Product family
Imaging
Product primary application
IN Carta Image Analysis Software; ImageXpress HCS.ai High-Content Screening System; Cancer Research Solutions; Cell Imaging & Analysis; Disease Modeling; Drug Discovery and Development; Live Cell Imaging
CMP
701Nr00000skC5XIAU
Title
Label-free cell imaging and analysis using transmitted light and deep learning
Resource URL
Resource Image URL
