dextrusion 0.0.3

DeXtrusion: automatic detection of cell extrusion in epithelial tissu

DeXtrusion

DeXtrusion is a machine learning based python pipeline to detect cell extrusions in epithelial tissues movies. It can also detect cell divisions and SOPs, and can easily be trained to detect other dynamic events.

This repository contains the code source of the python DeXtrusion library, the trained neural networks (ready to use) with Jupyter notebooks to run, train or retrain DeXtrusion networks, and Fiji macros for visualization/analysis of the results.

• Presentation
• Installation
• Usage
• Data
• DeXtrusion networks
• Fiji macros
• Jupyter notebooks
• References

Presentation

DeXtrusion takes as input a movie of an epithelium and outputs the spatio-temporal location of cell extrusion events or othe event as cell divisions. The movie is discretized into small overlapping rolling windows which are individually classified for event detection by a trained neural network. Results are then put together in event probability map for the whole movie or as spatio-temporal points indicating each event.

 Example of the detection of an extrusion event (probability map, red).

Event classification

The movie is discretized into small windows that span all the movie in a rolling windows fashion with overlap between the different windows. Each window is classified by a trained neural network as containing or not a cellular event as cell extrusion. The different classes available in the main DeXtrusion network are:

• No event
• Cell extrusion/cell death
• Cell division
• SOPs

It is easy to add or remove a cellular event to this list, but necessitates to train new neural networks for this. Jupyter notebook is available in this repository to do it.

Event probability map

Each window is associated an event probability which allow to generate an events probability map on the whole movie. This probability maps can be refined to precise spatio-temporal spots for each event. The results can be visualized by overlaying the initial movie and all the probability maps saved by DeXtrusion in Fiji with the saved by DeXtrusion with the deXtrusion_overlayProbabilityMaps.ijm macro.


Example of probability maps (green: division, red: extrusion, blue: SOP

Event spot localization

An event is visible in the probability map as a volume of connected pixels of high probability. To convert the probability map to a list of event, we place the event localization at the center of each of these high probability volumes. Moreover, to reduce false positive detections, the volumes can be thresholded to keep only big enough volume of high enough probability values. The list of spots obtained in this way are saved in ROIS .zip file that can be open in Fiji through the ROIManager tool. The macro deXtrusion_showROIs.ijm allows to directly visualize with Fiji the results saved by DeXtrusion.

Installation

DeXtrusion is distributed as a python module, compatible with python3. You can install it in a virtual environment to be sure to have the required versions. DeXtrusion can be installed either manually through the setup file or with pip:

• DeXtrusion can be directly installed via pip. In your python virtual environment, enter: pip install dextrusion to install it. You should also download the trained neural network DeXNet that you want to use from this repository.

• To install manually DeXtrusion, clone this github repository, and inside this repository type: python setup.py install. If you want to install it in a virtual environment, you should have activated it before. It will install all the necessary dependencies.

Detailled installation on Linux (Ubuntu)

Here's a step by step command lines to run in a Terminal to install DeXtrusion on Linux from scratch. It is creating a new virtual environment on which DeXtrusion will be installed (not necessary but recommended). Tested with python 3.8.

• Installation with pip

python3 -m venv dextrusenv               ## create a virtual environment with python 3 called dextrusenv (you can choose the name)
source dextrusenv/bin/activate           ## activate the virtual environment: now python commands will be runned in that environment
pip install dextrusion                  ## install DeXtrusion, download all the necessary dependencies, can take time

DeXtrusion is installed and can be runned with:

python -m dextrusion 	                  ## run DeXtrusion. Next time to run it again with the same networks, you only have to do this line

DeXtrusion needs neural networks to classify each sliding window. To used our trained neural networks (in the DeXNets repository), you have to download and uncompress it. From the command line:

mkdir DeXNets							## create directory on which neural networks will be put
mkdir DeXNets/notum_ExtSOPDiv			## create directory for the neural networks traind with 4 events available in this repository
cd DeXNets/notum_ExtSOPDiv               ## go inside the desired neural networks directory (here the network trained with 4 events)
wget https://gitlab.pasteur.fr/gletort/dextrusion/-/blob/main/DeXNets/notum_ExtSOPDiv/notumExtSOPDiv0.zip   ## download the first neural network
wget https://gitlab.pasteur.fr/gletort/dextrusion/-/blob/main/DeXNets/notum_ExtSOPDiv/notumExtSOPDiv1.zip   ## download the second neural network
unzip notumExtSOPDiv0.zip                ## uncompress it. It is now ready to be used
unzip notumExtSOPDiv1.zip
cd ../..                                 ## go back to main DeXtrusion directory

• You can also clone this repository and install DeXtrusion from the source code python setup.py install instead of using pip installation.

Detailled installation on Windows

You need to have anaconda installed to run python and create a new virtual environment. On the command prompt, write:

$ C:\ProgramData\Anaconda3\Scripts\Activate     ## activate conda (should be the path of your anaconda installation)
$ conda create --name dextrusionenv python=3.8  ## to create virtual environment called dextrusionenv
$ conda activate dextrusionenv                  ## activate the virtual environment that you juste created
$ pip install dextrusion                        ## install DeXtrusion in it
$ python -m dextrusion                          ## Run it

Choose the neural network DeXNets to use

DeXtrusion needs neural networks to classify each sliding window. To used our trained neural networks (in the DeXNets repository), you have to download and uncompress it. From DeXtrusion user interface, to select a network to run, you have to go inside its uncompressed directory (click on Browse and go there). If you want to use several networks together for the detection for better results (see our paper), the networks folders should be placed within the same directory. Go inside that directy with the interface, and it will use all the networks presents in that directory.

Usage

DeXtrusion can be used directly within python. To run an already trained network and detect cell events, run python3 -m dextrusion in your virtual environment. It will open a graphical interface to perform the detection.

We also propose Jupyter notebooks in this repository, to use dextrusion options. You can find a notebook to perform the detection, train a new neural network, retrain a network or evaluate the performance of the network compared to a manual detection.

Data

Data used for the training of our neural networks (raw movies with manual anotation of events) are freely available here: XXXXXX.

DeXNets: deXtrusion neural networks

DeXtrusion was trained on E-cadherin tagged drosophilia notum but can be easily adapted to other markers/biological conditions. Retraining of DeXtrusion network may be necessary when images are quite different.

In the deXNets folder of this repository, we proposed several trained networks:

• notum_Ext: trained on drosophilia notum movies with only extrusion events
• notum_ExtSOP: trained on drosophilia notum movies with extrusion and SOP events
• notum_ExtSOPDiv: trained on drosophilia notum movies with extrusion, division and SOP events
• notum_all: trained on drosophilia notum movies with extrusion, division and SOP events, on all our annotated data (training and test). Networks to use by default.

Download them and unzip to be ready to use in DeXtrusion.

Train/retrain a new DeXNet

If you want to train/retrain a neural network, to add a new event to detect or to improve its performance on new datasets, Jupyter notebooks are proposed in this repository: Jupyter notebooks. You can just follow step by step the notebook.

If you want to change the architecture of DeXNet, you will have to change it in the src/dextrusion/Network.py file. The architecture is defined in the action_model function.

Fiji macros

Fiji macros to prepare data for DeXtrusion neural network training or to handle DeXtrusion results are available in the Fiji macros folder of this repository.

• deXtrusion_checkAndClearROIs.ijm allows to check manually all the detected ROIs and to keep only the correct ones. It shows each event (point ROI) detected by deXtrusion as a pop-up window centered temporally and spatially around the event. The user is asked wether the detection is correct (if there is an event in the current window) by pressing y or n. If yes, the ROI is kept, else it will be removed from the list of ROIs.

• deXtrusion_scoreROIs_Random.ijm allows to score manually the precision of the detected events. The macro will open randomly a given number of ROIs, and show a window centered temporally and spatially around the event. The user is asked wether the detection is correct (if it contains the event) by pressing y or n. The macro will output the count of correct and false detections and the precision. The ROIs file will not be edited and the ROIs left after running this macro should not be saved as it deletes all the ROIs explored to be sure not to draw them twice.

• deXtrusion_overlayProbabilityMaps.ijm allows to visualize the probability maps together with the original image. The user is asked for the original image to visualize and will open the probability maps in the results folder where they should be saved by default. The directory to look in can be changed in the macro.

• deXtrusion_showROIs.ijm displays the original movie (chosen by the user) and the ROI file found in the results folder. The location of the ROI file can be changed in the macro by changing the resfold parameter and the event to show (by default extrusion) can be changed in the type parameter.

• deXtrusion_subsetMovieAndRois.ijm save a temporal subset of a movie, and keep only the corresponding ROIs.

Jupyter notebooks

We provide in this repository several jupyter notebooks to run, train/retrain, evaluate DeXtrusion.

• detectEventsOnMovie.ipynb allows you to run DeXtrusion with the graphical interaface to choose the parameters.
• deXtrusion_TrainNetwork.ipynb to train a new DeXNet network with your own data (or using our data publicly available, see data section).
• deXtrusion_RetrainNetwork.ipynb to retrain an already trained neural network with new data.
• deXtrusion_CompareRois.ipynb to compare two ROIs files (manual detection vs DeXtrusion detection) and gives the score. It can also generate ROIs files of the false detection (false positives and/or false negatives).
• deXtrusion_testClassification_OnWindows.ipynb: run the trained neural network (DeXNet) on test data on generated windows, and print the resulting confusion matrix of the classification (DeXtrusion classification compared to the manual classification).

Notebooks usage

• If you have Jupyter installed on your computer, to add your virtual environment to the environment Jupyter can see:

ipython kernel install --user --name=dextrusionenv

You can refer to this tutorial for instructions on creating virtual environment and using it with jupyter.

• You can also install Jupyter Lab or Jupyter notebook in your virtual environment if you don't have it installed in your computer:

pip install jupyterlab
jupyter-lab

Refer to Jupyter webpage for more information.

References

License

DeXtrusion is distributed open-source under the BSD-3 license, see the license file in this repository.

When you use DeXtrusion source code, neural networks or data for your projects, please cite our paper.