AI-Driven Image Analysis for Cancer Cell Biology
Authors: Gillian Lovell | Last updated: 19th April 2023
Overview
Cancer cell biology is a field rapidly moving toward more complex biological models, as well as more sensitive and precious cell types (including primary and stem cells). Label-free live-cell imaging and analysis is a useful method in this capacity for acquiring data on cell growth and behavior without perturbance.
Artificial Intelligence (AI) in live-cell imaging has enabled highly accurate, robust and unbiased image quantification. In this video we describe how Incucyte® AI Confluence analysis uses an expert-trained convolutional neural network (CNN) to accurately identify a wide range of cells including tumor cells, fibroblasts and stem cells.
- Document type: Video
- Watch time: 10 minutes
Key Takeaways
- A live-cell imaging and analysis system that enables non-perturbing measurement of cell proliferation
- Image identification and quantification powered by an AI model trained on a wide range of healthy and dying cells
- Achieving accurate quantification of tumor cells with little user input required to collect robust and reproducible data across experiments
AI-Driven Image Analysis for Cancer Cell Biology
Live-cell image analysis is an important tool for gathering cell growth data in the field of cancer cell biology. This poster describes how Incucyte® AI Confluence analysis can be used to enable non-perturbing measurement of cell proliferation using a trained convolutional neural network.
