Data from: Segmentation of dense and multi-species bacterial colonies using models trained on synthetic microscopy images

This repository contains the relevant data and code for the article "Segmentation of dense and multi-species bacterial colonies using models trained on synthetic microscopy images" by Vincent Hickl, Abid Khan, René M. Rossi, Bruno F. B. Silva, and Katharina Maniura-Weber (arXiv version 3):

Raw synthetic images of rod-shaped and circular cells

Confocal microscopy images of densely packed colonies of Pseduomonas aeruginosa, and of mixed colonies of P. aeruginosa and Staphylococcus aureus

Brigthfield microscopy images of rod-shaped and circular cells

Synthetic images processed by cycleGAN, used as training data for segmentation models.

A selection of trained segmentation models for dense Pseudomonas monolayers, and rod-shaped/circular cells in both confocal and brightfield microscopy images.

The code for processing raw synthetic images using CycleGAN can be found at https://github.com/abid1214/cyclegan

Further relevant code can be found at https://github.com/vhickl/synth-bacteria-segmentation, including code to:

generate raw synthetic images of rod-shaped and spherical bacteria

analyze orientational order in densely-packed colonies of rod-shaped cells

quantitatively compare segmentation masks of bacterial colonies

Please contact the authors ([e-mail]) if there are any issues or questions regarding this data.