Application Note: Accurate Analysis of Cell Morphology and Viability
Authors: Gillian Lovell, Jasmine Trigg, Nicola Bevan | Last updated: March 2025
Overview
Label-free imaging allows for non-perturbing monitoring of cell behavior, but analyzing these images is complex due to the dynamic and varied morphologies of cells in culture. Traditional computer vision approaches struggle with this task, this study highlights the advantages of using a neural network for segmentation and machine learning-based classifiers for cell classification.
Learn more about the importance and challenges of accurately segmenting cell boundaries for quantifying cell morphology, particularly in label-free imaging environments, and how the integration of AI and machine learning for enhanced cell analysis provides robust solutions for monitoring and classifying cell morphology.
- Document type: Application Note
- Page count: 10
- Read time: 10 minutes
Key Takeaways
- Label-Free Imaging
- AI-Based Cell Segmentation
- Machine Learning Classifiers
- Advanced Label-Free Classification
Innovative AI Techniques for Accurate Analysis of Cell Morphology and Viability
Figure 3. Images display iPSC-derived microglia at 24h in culture. Outline denotes cell segmentation and color denotes class (magenta = live, ramified; teal = live, amoeboid; red = dead; A). Time course indicates the % live (viable) cells calculated using AI CH over 144h (B). Bar chart displays the proportion of viable ramified vs amoeboid cells at 24 and 96h (C).
