Quantitative biological analysis of brightfield cell images using deep learning

Jesús D. Pineda; Saga Helgadottir; Benjamin Midtvedt; Alan Abirsh; Caroline B. Adiels; Stefano Romeo; Daniel Midtvedt; Giovanni Volpe

doi:10.1117/12.2596044

1 August 2021 Quantitative biological analysis of brightfield cell images using deep learning

Jesús D. Pineda, Saga Helgadottir, Benjamin Midtvedt, Alan Abirsh, Caroline B. Adiels, Stefano Romeo, Daniel Midtvedt, Giovanni Volpe

Author Affiliations +

Proceedings Volume 11804, Emerging Topics in Artificial Intelligence (ETAI) 2021; 118041H (2021) https://doi.org/10.1117/12.2596044
Event: SPIE Nanoscience + Engineering, 2021, San Diego, California, United States

Abstract

Quantitative analysis of cell structures is essential for pharmaceutical drug screening and medical diagnostics. This work introduces a deep-learning-powered approach to extract quantitative biological information from brightfield microscopy images. Specifically, we train a conditional generative adversarial neural network (cGAN) to virtually stain lipid droplets, cytoplasm, and nuclei from brightfield images of human stem-cell-derived fat cells (adipocytes). Subsequently, we demonstrate that these virtually-stained images can be successfully employed to extract quantitative biologically relevant measures in a downstream cell-profiling analysis. To make this method readily available for future applications, we provide a Python software package that is available online for free access.

Conference Presentation

Citation Download Citation

Jesús D. Pineda, Saga Helgadottir, Benjamin Midtvedt, Alan Abirsh, Caroline B. Adiels, Stefano Romeo, Daniel Midtvedt, and Giovanni Volpe "Quantitative biological analysis of brightfield cell images using deep learning", Proc. SPIE 11804, Emerging Topics in Artificial Intelligence (ETAI) 2021, 118041H (1 August 2021); https://doi.org/10.1117/12.2596044

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available