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