A package for tracking cells in 3D time lapse images
Project description
3DeeCellTracker
3DeeCellTracker is a deep-learning based pipeline for tracking cells in 3D time lapse images of deforming/moving organs (eLife, 2021).
Updates:
3DeeCellTracker v0.4.2 was released with following issues fixed (2022.06.02)
- Solved the saving mistakes when cell number > 255.
Installation
- Create a conda environment for a PC with GPU including prerequisite packages using the 3DCT.yml file:
$ conda env create -f 3DCT.yml
- (NOT RECOMMEND) Users can create a conda environment for a PC with only CPU, but it will be slow and may fail.
$ conda env create -f 3DCT-CPU.yml
- Install the 3DeeCellTracker package solely by pip
$ pip install 3DeeCellTracker
- Update the 3DeeCellTracker package to the latest version
$ pip install --update 3DeeCellTracker
For detailed instructions, see here.
Quick Start
To learn how to track cells use 3DeeCellTracker, see following notebooks for examples:
-
Track cells in deforming organs:
-
Track cells in freely moving animals:
-
Train a new 3D U-Net for segmenting cells in new optical conditions:
The data and model files for demonstrating above notebooks can be downloaded here.
Note: Codes above were based on the latest version. For old programs used in eLife 2021, please check the "Deprecated_programs" folder.
Video Tutorials
We have made tutorials explaining how to use our software. See links below (videos in Youtube):
Tutorial 1: Install 3DeeCellTracker and train the 3D U-Net
Tutorial 2: Tracking cells by 3DeeCellTracker
Tutorial 3: Annotate cells for training 3D U-Net
Tutorial 4: Manually correct the cell segmentation
A Text Tutorial
We have wrote a tutorial explaining how to install and use 3DeeCellTracker. See Bio-protocol, 2022
How it works
We designed this pipeline for segmenting and tracking cells in 3D + T images in deforming organs. The methods have been explained in Wen et al. bioRxiv 2018 and in Wen et al. eLife, 2021.
Overall procedures of our method (Wen et al. eLife, 2021–Figure 1)
Examples of tracking results (Wen et al. eLife, 2021–Videos)
Neurons in a ‘straightened’ freely moving worm |
Cardiac cells in a zebrafish larva | Cells in a 3D tumor spheriod |
---|---|---|
Citation
If you used this package in your research and is interested in citing it here's how you do it:
@article{
author = {Wen, Chentao and Miura, Takuya and Voleti, Venkatakaushik and Yamaguchi, Kazushi and Tsutsumi, Motosuke and Yamamoto, Kei and Otomo, Kohei and Fujie, Yukako and Teramoto, Takayuki and Ishihara, Takeshi and Aoki, Kazuhiro and Nemoto, Tomomi and Hillman, Elizabeth MC and Kimura, Koutarou D},
doi = {10.7554/eLife.59187},
journal = {eLife},
month = {mar},
title = {{3DeeCellTracker, a deep learning-based pipeline for segmenting and tracking cells in 3D time lapse images}},
volume = {10},
year = {2021}
}
Acknowledgements
We wish to thank JetBrains for supporting this project with free open source Pycharm license.
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