This repository contains the data to reproduce the study `Single-cell segmentation in bacterial biofilms with optimized convolutional neural networks enables tracking of cell lineages and measurements of growth rates` by Jelli, Ohmura, Netter, et al. It contains the following four subfolders: - `training-data-from-experimentally-acquired-images`: the dataset that was created to train segmentation models - `trained-models`: contains five models trained on the trainind dataset to be used for predictions - `segmentation-predictions-for-different-species`: segmentations based on a trained model on biofilms of different species - `training-data-synthetic`: Synthetic microscope images and their corresponding label images together with scripts to create the data