3DeeCellTracker 0.5.1a0

A package for tracking cells in 3D time lapse images in deforming organs or moving animals

3DeeCellTracker

3DeeCellTracker is a deep-learning based pipeline for tracking cells in 3D time lapse images of deforming/moving organs (eLife, 2021).

Updates:

3DeeCellTracker v0.5.0-alpha has been released

• Allows you to use StarDist3D for segmentation
• Reduces the requirements for fine-tuning parameters
• Decouples the code to facilitate reuse by third-party developers.

Installation

To install 3DeeCellTracker, please follow the instructions below:

Prerequisites

• A computer with an NVIDIA GPU that supports CUDA. We have tested all examples in a Nvidia Geforce GPU 3090.
• Anaconda or Miniconda installed.

Steps

1. 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
   

2. Install TensorFlow. We have tested Tensorflow 2.5.0 in our computer.

3. Install 3DeeCellTracker by running the following command in your terminal:

   $ pip install 3DeeCellTracker==0.5.0a0
   

   After completing the installation steps, you can start using 3DeeCellTracker for your 3D cell tracking tasks within the jupyter notebooks we have provided (See below). If you encounter any issues or have any questions, please refer to the project's documentation or raise an issue in the GitHub repository.

Quick Start

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.

1. Train a custom deep neural network for segmenting cells in new optical conditions:

   • Train 3D StarDist (notebook with results)
   • Train 3D U-Net (clear notebook).
   • Train 3D U-Net (results).

2. Track cells in deforming organs:

   • Single mode + StarDist (notebook with results);
   • Single mode + UNet (clear notebook);
   • single mode + UNet (results)

3. Track cells in freely moving animals:

   • Ensemble mode + StarDist (notebook with results);
   • Ensemble mode + UNet (clear notebook)
   • Ensemble mode + UNet (results)

The data and model files for demonstrating above notebooks can be downloaded here:

• StarDist-based notebooks.
• UNet-based notebooks.

Note: Codes above were based on the latest version. For old programs used in eLife 2021, please check the "Deprecated_programs" folder.

Frequently Reported Issue and Solution (for v0.4)

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).

Video Tutorials (for v0.4)

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 (for v0.4)

We have written 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.