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Related Experiment Video

Updated: May 5, 2026

In Vitro Generation of Heart Field-specific Cardiac Progenitor Cells

In Vitro Generation of Heart Field-specific Cardiac Progenitor Cells

Published on: July 3, 2019

Generative Ai for Cardiovascular Cell Type-Specific Fluorescence Colorization of Live-Cell hPSC-Derived Cardiac

Arun Kumar Reddy Kandula^1,2, Tanakit Phamornratanakun^1,2, Angello Huerta Gomez¹

¹Department of Biomedical Engineering, University of North Texas, Denton, Texas, USA.

Advanced Intelligent Discovery

|August 26, 2025

Summary

Generative AI colorizes bright-field images of cardiac organoids, enabling efficient, accurate cell type identification without fluorescent labels. This advances heart development and disease modeling using AI-powered imaging analysis.

Keywords:

bright-field microscope cardiac organoids fluorescence imaging generative adversarial networks human pluripotent stem cell (hPSC)image colorization

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Last Updated: May 5, 2026

In Vitro Generation of Heart Field-specific Cardiac Progenitor Cells

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Published on: July 3, 2019

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Published on: September 15, 2021

Author Spotlight: Improving Reproducibility in Vascular Organoids Using ROCK Inhibitors and Microwell Confinement

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Published on: December 13, 2024

Area of Science:

Cardiovascular Biology
Biomedical Imaging
Artificial Intelligence

Background:

Human pluripotent stem cell-derived cardiac organoids (COs) are vital for heart development and disease research.
Current characterization relies on time-consuming fluorescent labeling, limiting live-cell analysis.
Bright-field microscopy offers morphological insights but lacks cell-type specificity.

Purpose of the Study:

To develop an AI system for efficient, non-invasive cell type identification in COs.
To overcome limitations of bright-field microscopy in COs analysis.
To enable accurate quantification of cardiovascular cells within COs.

Main Methods:

Utilized conditional generative adversarial networks (cGANs) for image colorization.
Applied AI to phase contrast microscopy images of COs.
Generated synthetic fluorescence images from bright-field data.

Main Results:

The AI system successfully colorized phase contrast images, mimicking fluorescence labeling.
Achieved high efficiency and accuracy in cardiovascular cell type identification.
Enabled detailed quantification of cardiomyocytes and endothelial cells within COs.

Conclusions:

Generative AI can provide detailed cardiovascular cell information from standard bright-field images of COs.
This AI approach enhances COs characterization, accelerating heart research.
The method offers a non-invasive, efficient alternative to traditional fluorescent imaging.