Colony morphology (CM) is an important criterion to evaluate health of Pluripotent Stem Cells (PSCs) in culture or to select induced-PSC colonies after reprogramming. However, manual evaluation of CM is time-consuming, not quantitative and poorly reproducible. We designed an unbiased method to evaluate CM of non-labeled PSCs using microplate images acquired from a flatbed scanner. The high-throughput automated analysis is based on image processing and algorithms for segmentation, count and multi-parametric classification of colonies.

This is poster number 52 in Poster Session

Authors:

Lucia Maddalena (National Research Council, Institute for High-Performance Computing and Networking)

Laura Casalino (National Research Council, Institute of Genetics and Biophysics)

Mario Rosario Guarracino (National Research Council, Institute for High-Performance Computing and Networking)

Keywords:

image segmentation, machine learning