Cédric P. Allier,1 Lionel Hervé,1 Chiara Paviolo,1 Ondrej Mandula,1 Olivier Cioni,1 William Pierré,1 Kiran Padmanabhan,2 Francesca Andriani,2 Sophie Morales1
1CEA-LETI (France) 2Institut de Génomique Fonctionnelle de Lyon (France)
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.
We present a CNN-based quantification pipeline for the imaging and analysis of adherent cell cultures. The imaging part features two CNNs dedicated to lens-free microscopy performing accelerated holographic reconstruction and phase unwrapping. The analysis part features CNNs estimating several cellular metrics. These CNNs maps phase image into 2D quantitative representations of cell positions and measurements. The outputs images are processed by a local maxima algorithm to obtain a list of cell measurements. Here, we discuss the performance and limitations of this CNN-based quantification pipeline. The advantage is the fast processing time, i.e. the analysis of ~10.000 cells in 10 seconds.
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.
The alert did not successfully save. Please try again later.
Cédric P. Allier, Lionel Hervé, Chiara Paviolo, Ondrej Mandula, Olivier Cioni, William Pierré, Kiran Padmanabhan, Francesca Andriani, Sophie Morales, "From phase imaging to CNN-based quantitative representation," Proc. SPIE PC12136, Unconventional Optical Imaging III, PC121360T (30 May 2022); https://doi.org/10.1117/12.2625595