A project to track colloids using confocal and deep learning.

Colloidoscope is essentially a U-net trained on simulated data. This is designed to to improve tracking on very small colloids (~200nm) with a very bad point spread function, that is anisotropic and has high z blur.

Pretrained weights and hyperparameter training history available upon request.

pip3 install git+https://github.com/wahabk/colloidoscope

I have provided custom jupyter notebooks in examples/.

You can run colloidoscope in google colab:

Colloidoscope centers around the class DeepColloid. This contains all the functions you will need.

First import and read your data:

import colloidoscope as cd

dc = cd.DeepColloid()
path = 'path/to/image.tif'
image = dc.read_tif(path)
# If you have a lif file Colloidoscope wraps the explore_lif Reader class
Reader = dc.ExploreLifReader(args, kwargs)

Then simply use dc.detect() to track your image, this will return a pandas DataFrame just like TrackPy.

df = dc.detect(image, weights_path='path/to/model_weights.pt')

Please check examples/ for scripts and jupyter notebooks.

• All available average precision functions only work with bounding boxes. Colloidoscope has a function dc.average_precision(true_positions, predicted_precisions) that will find AP based on positions alone - since the distance is more important for this project than the IOU.
• You can use / adapt dc.simulate() if you want to quickly draw simulated particles in 3D.

Check setup.py.

If you want to use colloidoscopes positions simulation you will need to install hoomdblue. This is included if you used Option 2 when installing but please install on your own if you used pip (Option 1). I found the easiest way is to use conda:

conda install -c conda-forge hoomd -y

I provide a docker image to make gpu training easier

Step 1 : Build image, this will build an image named colloids

docker build . --tag=colloids 
docker build . --tag=colloids --network=host # if you're on vpn

Step 2 : Create container in interactive mode, this will start a shell inside the container

docker run -it \
	-v <data_dir>:<data_dir> \ # mount data directory as volume
	-v <repo_dir>:/colloidoscope \ # mount git repo directory as volume for interactive access
	--gpus all \ # allow the container to use all gpus
	--network=host \ # if you're on vpn
	colloids \ 

Note: If you want to launch the container on a custom hard drive use:

sudo dockerd --data-root <custom_dir>

Thanks to Yushi Yang @yangyushi for contributing the position simulations and helping me with the entire project in general.

This project wraps explore_lif which is written by Mathieu Leocmach and C. Ybert.