3DeeCellTracker is a deep-learning based pipeline for tracking cells in 3D time-lapse images of deforming/moving organs (eLife, 2021).
3DeeCellTracker v1.0.0 has been released
To install 3DeeCellTracker, please follow the instructions below:
Note: We have tested the installation and the tracking programs in two environments:
- (Local) Ubuntu 20.04; NVIDIA GeForce RTX 3080Ti; Tensorflow 2.5.0
- (Google Colab) Tensorflow 2.12.0 (You need to upload your data for tracking)
Create a new conda environment and activate it by running the following commands in your terminal:
$ conda create -n track python=3.8 pip
$ conda activate track
Install TensorFlow 2.x by following the instructions provided in the TensorFlow installation guide.
Install the 3DeeCellTracker package by running the following command in your terminal:
$ pip install 3DeeCellTracker==1.0.0
Once the installation is complete, you can start using 3DeeCellTracker for your 3D cell tracking tasks within the Jupyter notebooks provided in the GitHub repository.
If you encounter any issues or have any questions, please refer to the project's documentation or raise an issue in the GitHub repository.
To learn how to track cells using 3DeeCellTracker, please refer to the following notebooks for examples. We recommend using StarDist for segmentation, as we have optimized the StarDist-based tracking programs for more convenient and quick cell tracking. Users can also use the old way with 3D U-Net.
Train a custom deep neural network for segmenting cells in new optical conditions:
Track cells in deforming organs:
Track cells in freely moving animals:
(Optional) Train FFN with custom data:
The data and model files for demonstrating above notebooks can be downloaded here:
Here are two protocols for using 3DeeCellTracker:
Version 1.0.0
Wen, C. (2024). Deep Learning-Based Cell Tracking in Deforming Organs and Moving Animals. In: Wuelfing, C., Murphy, R.F. (eds) Imaging Cell Signaling. Methods in Molecular Biology, vol 2800. Humana, New York, NY. DOI: 10.1007/978-1-0716-3834-7_14
Version 0.4
Wen, C. and Kimura, K.D. (2022). Tracking Moving Cells in 3D Time Lapse Images Using 3DeeCellTracker. Bio-protocol 12(4): e4319. DOI: 10.21769/BioProtoc.4319
Multiple users have reported encountering a ValueError
of shape mismatch when running the tracker.match()
function.
After investigation, it was found that the issue resulted from an incorrect setting of siz_xyz
,
which should be set to the dimensions of the 3D image as (height, width, depth).
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
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. The original programs used in eLife 2021 was contained in the "Deprecated_programs" folder.
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 |
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If you used this package in your research, please cite our paper:
Depending on the segmentation method you used (StarDist3D or U-Net3D), you may also cite either of following papers:
Martin Weigert, Uwe Schmidt, Robert Haase, Ko Sugawara, and Gene Myers. Star-convex Polyhedra for 3D Object Detection and Segmentation in Microscopy. The IEEE Winter Conference on Applications of Computer Vision (WACV), Snowmass Village, Colorado, March 2020
Çiçek, Ö., Abdulkadir, A., Lienkamp, S.S., Brox, T., Ronneberger, O. (2016). 3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation. In: Ourselin, S., Joskowicz, L., Sabuncu, M., Unal, G., Wells, W. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016. MICCAI 2016. Lecture Notes in Computer Science(), vol 9901. Springer, Cham.
We wish to thank JetBrains for supporting this project with free open source Pycharm license.