Pipeline: (2D) Cell Confluency AI-Powered

This pipeline leverages the Cellpose model to segment cell regions directly from 2D projections, enabling accurate confluency measurement across a wide range of cell types and imaging conditions. By calculating the proportion of the field of view (FOV) occupied by the segmented cell area, it provides a quantitative confluency value that reflects cell coverage.

The deep learning-based segmentation ensures robust performance even in high-confluency or morphologically diverse samples, where traditional rule-based methods may struggle. This pipeline is ideal for monitoring cell growth, evaluating proliferation, and performing high-throughput confluency analysis with minimal user input.

Output: Cell mask, FOV area and segmented cell area measurements





Access to analysis pipelines is available upon request. Please send your inquiry to info@tomocube.com to explore the options best suited to your needs.