🧬 Toxoplasma 3D Tracking Project

This repository contains Python scripts and Jupyter Notebooks for tracking Toxoplasma in 3D using various implementations. The project features serial, parallel, and distributed versions, showcasing the flexibility and scalability of the tracking algorithms. This work is part of our contribution to the IEEE Big Data 2018 conference.

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🌟 Project Overview

The repository is structured to provide different approaches for tracking Toxoplasma:

• 🔗 Serial Version: A single-threaded approach for smaller datasets.
• ⚙️ Parallel Version: Optimized for running on a server with local multiprocessing.
• ☁️ Distributed Version: Leveraging Dask for distributed computation across a cluster, suitable for large-scale data.

Key Technologies Used

• Python 3.6
• Scientific Computing Libraries: NumPy, SciPy, scikit-learn, matplotlib
• Computer Vision Tools: OpenCV for image processing and tracking
• Parallel and Distributed Computing:
  • Dask for distributed processing
  • Joblib and multiprocessing for parallel execution
  • Jupyter Notebook for prototyping and visualization

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📄 Paper Details

This project is based on the following publication:

Paper Title: Toward Simple & Scalable 3D Cell Tracking
Conference: 2018 IEEE International Conference on Big Data (Big Data)
IEEE Link: View on IEEE
Pre-print Link: View on ResearchGate

Citation

If you use this code or build upon it for your research, please cite our paper using the following format:

@INPROCEEDINGS{8622403,
  author={M. S. Fazli, S. A. Vella, S. N. J. Moreno, G. E. Ward and S. P. Quinn},
  booktitle={2018 IEEE International Conference on Big Data (Big Data)},
  title={Toward Simple & Scalable 3D Cell Tracking},
  year={2018},
  pages={3217-3225},
  doi={10.1109/BigData.2018.8622403},
  keywords={Three-dimensional displays;Videos;Pipelines;Microscopy;Tracking;Two dimensional displays;Genetic algorithms;Cell Detection;Cell Tracking;3D video Tracking;3D Microscopic Videos;Large-Scale tracking method;Computer vision;Toxoplasma Gondii}
}

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🚀 How to Run the Code

Step 1: Prepare Your Data

Ensure that your 2D or 3D image data is stored locally or accessible from your server/cluster. Modify the code to specify the path to the image slices and any additional parameters required.

Step 2: Run the Code

For the Serial Version:

time python 3D_tracking_serial.py

For the Parallel Version:

python 3D_tracking_parallel.py

For the Distributed Version: Run the Jupyter Notebook files in an environment with Dask configured:

jupyter notebook 3D_Tracking_detection_module.ipynb

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🛠️ Prerequisites

Ensure that the following dependencies are installed:

pip install numpy scipy scikit-learn matplotlib opencv-python dask joblib

For the distributed version, additional Dask configurations may be needed for your cluster environment.

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✨ Customization Tips

• Cluster Configuration: Adjust the Dask client and scheduler settings for optimal performance.
• Memory Management: For large-scale data, tweak memory usage settings in Dask to prevent overflow.
• Parameter Adjustments: Modify paths, cluster numbers, and other configurable parameters in the scripts/notebooks as per your data and requirements.

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📧 Contact

For questions or collaboration inquiries, feel free to reach out:

• 📧 [email protected]
• 📧 [email protected]

🚀 Explore the scripts and notebooks to experience efficient and scalable Toxoplasma tracking solutions!