CNN-Based Cell Analysis: From Image to Quantitative Representation - Allier, Cédric; Hervé, Lionel; Paviolo, Chiara; Mandula, Ondrej; Cioni, Olivier; Pierré, William; Andriani, Francesca; Padmanabhan, Kiran; Morales, Sophie (Frontiers in Physics, 2022)
We present a novel deep learning-based quantification pipeline for the analysis of cell culture images acquired by lens-free microscopy. The image reconstruction part of the pipeline features a convolutional neural network performing phase unwrapping and accelerating the inverse problem optimization. It allows phase retrieval at the 4K level (3840×2748 pixels) in three seconds only. The analysis part of the pipeline features a suite of convolutional neural networks estimating different cell metrics from the reconstructed image, i.e. cell surface area, such as cell dry mass, cell length, and cell thickness. The networks have been trained to predict quantitative representation of the cell measurements that can be next translated into measurement lists with a local maxima algorithm. In this paper, we discuss the performance and limitations of this novel deep learning-based quantification pipeline in comparison with a standard image processing solution. The main advantage brought by this method is the fast processing time, i.e. the analysis rate of ~25.000 cells measurements per second. Although our proof of principle has been established with lens-free microscopy, the approach of using quantitative cell representation in a deep learning framework can be similarly applied to other microscopy techniques.