Preamble

This repository contains the code used to analyse the data acquired during the development of project LIGHTNING (https://www.nature.com/articles/s41467-022-29172-0).
In this repo you also have the opportunity to test the segmentation code on your own fluorescence images/movies, either online or on your computer by cloning the repo (see below).

As for the code applied to LIGHTNING data (folder: codes_lightning):

• segmentation_and_fits_update.py performs the segmentation of the high quality image, the registration with the binned images, extracts the pixel-wise time response of fluorescence and fits the values of tau for each curve (Supplementary Information §1.8.1-3 )
• analyse_results_amplitude.py applies the threshold criteria to the pixel-wise distribution of tau for each bacteria to keep or remove the bacteria from the global results (See Supplementary Information §1.8.3 )
• proba_attribution_fig4c.py attributes label to bacteria based on covariance matrices.

If you wish to test the segmentation, follow the instructions below.

Introduction

This code's input is a TIF movie of fluorescence objects. This code's expected output is an image with an area of interest for each object in the movie.

Example:

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Flexibility

The jupyter notebook provided allows to upload your own video and test the segmentation. The interface is interactive and allows you to test the various parameters to obtain the best segmentation.
Once you have found the optimal parameters for your segmentation you can collect the segment_image function and inculde it in your image analysis process.

Interactive parameters:

To have a detailed explanation of the code, TODO(link to SI of the paper).

You can also do intuitive trials. Here is how each parameter affects the intermediary steps of the code which are displayed as images:

Contrast mask:

Autolevel mask:

Maximum positions (center of the isolated objects):

Segmentation mask and contours:

The range of the parameters can be changed to better suit your needs ! Simply modify the number in parenthesis in the line:

interact(segment_image, contrast = (1, 4), autolevel = (1, 10), dist_max = False, dist_seg = False, disk_size = (1, 4), max_contrast = (1,10), soft_hard = (0.1, 1.9))

Use

To try the segmentation on your own data you have two choices depending on your data policy.

• [simple/open data] You can try the online version (you will have to upload your data on the internet) by clicking here :

• [requires install/private data] You can download the code and use it on your computer: in the command line cd to a directory where you want to save the code and write:

  git clone https://github.com/Alienor134/image_segmentation.git
  cd image_segmentation
  conda create --name segmentation --file requirements.txt
  conda activate segmentation
  jupyter lab
  

  TODO: docker with repo2docker

License

Alienor134/image_segmentation is licensed under the GNU General Public License v3.0