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The automated analysis of microscopy images is a challenge in the context ofsingle-cell tracking and quantification. This work has as goals the study ofthe performance of deep learning for segmenting microscopy images and theimprovement of the previously available pipeline for tracking single cells.Deep learning techniques, mainly convolutional neural networks, have beenapplied to cell segmentation problems and have shown high accuracy and fastperformance. To perform the image segmentation, an analysis of hyperparameterswas done in order to implement a convolutional neural network with U-Netarchitecture. Furthermore, different models were built in order to optimize thesize of the network and the number of learnable parameters. The trained networkis then used in the pipeline that localizes the traps in a microfluidic device,performs the image segmentation on trap images, and evaluates the fluorescenceintensity and the area of single cells over time. The tracking of the cellsduring an experiment is performed by image processing algorithms, such ascentroid estimation and watershed. Finally, with all improvements in the neuralnetwork to segment single cells and in the pipeline, quasi-real-time imageanalysis was enabled, where 6.20GB of data was processed in 4 minutes.

Analysis of the performance of U-Net neural networks for the segmentation of living cells