AI-Driven Image Analysis for Label-Free Quantification of Chemotherapeutic Cytotoxicity
Authors: J. Trigg, G. Lovell, D. Porto, N. Holtz, N. Bevan, T. Dale | Last updated: May 2024
Overview
Glioblastoma multiform (GBM) is an aggressive glioma that originates from astrocytes and is associated with poor prognosis. Several barriers exist in the treatment of GBM, including the localization of the tumor within the brain, a high rate of malignant invasion, tumor heterogeneity, and an intrinsic resistance to conventional therapies. Despite concerted efforts to overcome these, a lack of translational in vitro models and robust analytical tools makes deciphering the complex molecular interactions challenging.
In this poster, we describe a robust solution for label-free cell segmentation and live/dead classification of individual cells using integrated AI-based software. We exemplify how this approach can provide high-throughput insights into glial cell health in response to clinically relevant chemotherapeutic treatments.
- Document type: Poster
- Page count: 1
- Read time: 5 minutes
Key Takeaways
- Discover a powerful, robust approach for assessing cytotoxicity in glial cell types
- Explore how label-free analysis enables non-perturbing quantification of cytotoxicity
- Learn how the Incucyte® AI Cell Health Analysis Software Module uses pre-trained neural networks to accurately segment individual cells
