Quantitative analysis of cell culture through learning-enabled lens-free microscopy

We present a learning-enabled lens-free microscope for quantitative analysis of cell cultures. Leveraging the advances of recent years in learning algorithms, we developed a suite of neural networks that detect, quantify and track the cells. The detection algorithm locates the cells. The quantification algorithm, measures different cell metrics directly from cell phase image patches centred on the cells detections. Measured features include among others: cell morphology (dry mass, thickness, aspect ratio, ...) and local neighbourhood (density, contact surface, …). Finally, the tracking algorithm predicts the position of a given cell at next time point, making it possible to monitor a cell across time. To train these models we designed a semi-automated pipeline able to generate a supervised training datasets of up to millions of cells. The measurements obtained from the proposed method open up for modelling the cell cultures and providing biological insights.