Welcome to ByoTrack’s documentation!

Tracking pipeline

ByoTrack is a Python library for tracking biological objects in microscopy videos (2D and 3D).

Its goal is to provide a fast, modular, and research-friendly tracking framework that integrates seamlessly with the Python scientific ecosystem and established bioimage analysis platforms such as Fiji, Icy and Napari.

ByoTrack defines a modular tracking API that can be easily extended to design and evaluate new methods. It also includes implementations of several state-of-the-art detection and tracking approaches following this API.

Some components are implemented natively in Python (e.g. WaveletDetector, KalmanLinker, KOFT, RTSSmoother, EMC2Stitcher), while others wrap existing tools (e.g. StarDistDetector, IcyEMHTLinker, TrackMateLinker), integrating with external software.

In addition, ByoTrack provides utilities for data loading and preprocessing, as well as evaluation and visualization of tracking results.

Note

ByoTrack has been primarily developed for scenarios involving up to a few thousand targets in 2D or 3D microscopy data. Some components assume that individual frames fit in memory, which may limit scalability to very large 3D volumes. If you encounter limitations with your use case, feel free to open an issue or contribute a pull request.

🏆 ByoTrack (PAST-FR) won the Cell Linking Benchmark of the Cell Tracking Challenge with its SKT/KOFT implementation (see our paper for details).

Warning

This project and documentation is under active development.

Cite Us

@article{hanson2024automatic,
   title={Automatic monitoring of neural activity with single-cell resolution in behaving Hydra},
   author={Hanson, Alison and Reme, Raphael and Telerman, Noah and Yamamoto, Wataru and Olivo-Marin, Jean-Christophe and Lagache, Thibault and Yuste, Rafael},
   journal={Scientific Reports},
   volume={14},
   number={1},
   pages={5083},
   year={2024},
   publisher={Nature Publishing Group UK London}
}

References

  • [1] F. De Chaumont, S. Dallongeville, N. Chenouard, et al., “Icy: an open bioimage informatics platform for extended reproducible research”, Nature methods, 2012.

  • [2] J.-C. Olivo-Marin, “Extraction of spots in biological images using multiscale products”, Pattern Recognition, 2002.

  • [3] U. Schmidt, M. Weigert, C. Broaddus, and G. Myers, “Cell detection with star-convex polygons”, MICCAI, 2018.

  • [4] N. Chenouard, I. Bloch, and J.-C. Olivo-Marin, “Multiple hypothesis tracking for cluttered biological image sequences”, IEEE TPAMI, 2013.

  • [5] T. Lagache, A. Hanson, J. Perez-Ortega, et al., “Tracking calcium dynamics from individual neurons in behaving animals”, PLoS Computational Biology, 2021.

  • [6] J. Schindelin, I. Arganda-Carreras, E. Frise, et al., “Fiji: an open-source platform for biological-image analysis”, Nature Methods, 2012.

  • [7] K. Jaqaman, D. Loerke, M. Mettlen, et al., “Robust single-particle tracking in live-cell time-lapse sequences.”, Nature Methods, 2008.

  • [8] J.-Y. Tinevez, N. Perry, J. Schindelin, et al., “TrackMate: An open and extensible platform for single-particle tracking.”, Methods, 2017.

  • [9] R. Reme, A. Newson, E. Angelini, J.-C. Olivo-Marin and T. Lagache, “Particle tracking in biological images with optical-flow enhanced kalman filtering”, IEEE ISBI, 2024.

  • [10] M. Maška, V. Ulman, D. Svoboda, P. Matula, et al., “A benchmark for comparison of cell tracking algorithms”, in Bioinformatics, 2014.

  • [11] R. Reme, A. Newson, E. Angelini, J.-C. Olivo-Marin and T. Lagache, “SINETRA: a Versatile Framework for Evaluating Single Neuron Tracking in Behaving Animals”, IEEE ISBI, 2025.

  • [12] A. Genovesio, Z. Belhassine, and J.-C. Olivo-Marin, “Adaptive gating in Gaussian Bayesian multi-target tracking”, IEEE ICIP, 2004.

Contents

Install

Getting started

API

API implementations

Datasets

Metrics

Icy support

Fiji support

Visualization