deeptrack 0.4.0

A deep learning oriented microscopy image simulation package

DeepTrack 2.0

DeepTrack is a comprehensive deep learning framework for digital microscopy. We provide tools to create physical simulations of customizable optical systems, to generate and train neural network models, and to analyze experimental data.

Getting started

Installation

To install, clone the folder 'deeptrack' to your project directory. The ability to install the package through pip is comming soon.

Dependencies:

• tensorflow (>=2.2.0)
• keras

Optional dependencies:

• matplotlib
• ffmpeg

Tutorials

The folder 'tutorials' contains notebooks with common applications. These may serve as a useful starting point from which to build a solution. The notebooks can be read in any order, but we provide a suggested order to introduce new concepts more naturally. This order is as follows:

1. deeptrack_introduction_tutorial gives an overview of how to use DeepTrack 2.0.
2. tracking_particle_cnn_tutorial demonstrates how to track a point particle with a convolutional neural network (CNN).
3. tracking_multiple_particles_unet_tutorial demonstrates how to track multiple particles using a U-net.
4. characterizing_aberrations_tutorial demonstrates how to add and characterize aberrations of an optical device.
5. distinguishing_particles_in_brightfield_tutorial demonstrates how to use a U-net to track and distinguish particles of different sizes in brightfield microscopy.
6. analyzing_video_tutorial demonstrates how to create videos and how to train a neural network to analyze them.

Examples

The examples folder contains notebooks which explains the different modules in more detail. Also these can be read in any order, but we provide a recommended order where more fundamental topics are introduced early. This order is as follows:

1. features_example
2. properties_example
3. scatterers_example
4. optics_example
5. aberrations_example
6. noises_example
7. augmentations_example
8. image_example
9. generators_example
10. models_example
11. losses_example
12. utils_example
13. sequences_example
14. math_example

Documentation

The detailed documentation of DeepTrack 2.0 is available at the following link: https://deeptrack-20.readthedocs.io/en/latest/

Funding

This work was supported by the ERC Starting Grant ComplexSwimmers (Grant No. 677511).