AI-Driven Image Analysis Enables Simplified, Label-Free Cytotoxicity Screening
Authors: Gillian Lovell, Jasmine Trigg, Daniel Porto, Nevine Holtz, Nicola Bevan, Timothy Dale, Daniel Appledorn | Last updated: May 2023
Overview
The increasing use of precious, patient-derived cells has driven the need for non-perturbing and label-free cell measurements. Incucyte® Live-Cell Analysis Systems enable long-term imaging of biology within a cell culture incubator to minimize cell disturbance.
To gain insight into cell growth and viability label free, we developed the Incucyte® AI Cell Health Analysis Software Module which uses two deep neural networks to robustly segment cells and infer cell viability in a single step. These AI models were trained on a wide diversity of cell morphologies to ensure the analysis is applicable across a broad variety of tumor cell types.
- Document type: Poster
- Page count: 1
- Read time: 5 minutes
Key Takeaways
- AI-driven image analysis enabling scientists to derive more data from microscopy images
- Simplified workflow for quantification of cell count and viability
- Accurate, adaptable cell segmentation and robust live/dead classification
Incucyte® AI Cell Health Analysis Workflow
Figure 1. Schematic diagram of the Incucyte® AI Cell Health Analysis Workflow. Phase contrast images are acquired and automatically processed. Cells are segmented and classified as Live or Dead; data can be visualized in real time for quantification of cell count and viability.
