ELEPHANT: Tracking cell lineages in 3D by incremental deep learning

	Developer	Ko Sugawara
	Forum	Image.sc forum Please post feedback and questions to the forum. It is important to add the tag elephant to your posts so that we can reach you quickly.
	Source code	GitHub
	Publication	Sugawara, K., Çevrim, C. & Averof, M. Tracking cell lineages in 3D by incremental deep learning. eLife 2022. doi:10.7554/eLife.69380

---

ELEPHANT is a platform for 3D cell tracking, based on incremental and interactive deep learning.

It works on client-server architecture. The server is built as a web application that serves deep learning-based algorithms.

This repository provides an implementation of the ELEPHANT server. The ELEPHANT client can be found here.

Please refer to the documentation for details.

---

Setting up the ELEPHANT Server

There are three options to set up the ELEPHANT server.

• Setting up with Docker

  This option is recommended if you have a powerful computer that satisfies the server requirements (Docker) with root privileges.

• Setting up with Singularity

  This option is recommended if you can access a powerful computer that satisfies the server requirements (Singularity) as a non-root user (e.g. HPC cluster).

• Setting up with Google Colab

  Alternatively, you can set up the ELEPHANT server with Google Colab, a freely available product from Google Research. In this option, you don't need to have a high-end GPU or a Linux machine to start using ELEPHANT's deep learning capabilities.

The detailed instructions for each option can be found in the documentation.

Acknowledgements

• PyTorch
• Numpy
• Scipy
• scikit-image
• Flask
• uWSGI
• NGINX
• Redis
• RabbitMQ
• Supervisord
• uwsgi-nginx-flask-docker
• ngrok
• Google Colab

Citation

Please cite our paper on eLife.

• Sugawara, K., Çevrim, C. & Averof, M. Tracking cell lineages in 3D by incremental deep learning. eLife 2022. doi:10.7554/eLife.69380

@article {Sugawara2022,
	author = {Sugawara, Ko and {\c{C}}evrim, {\c{C}}a?r? and Averof, Michalis},
	title = {Tracking cell lineages in 3D by incremental deep learning},
	year = {2022},
	doi = {10.7554/eLife.69380},
	abstract = {Deep learning is emerging as a powerful approach for bioimage analysis. Its use in cell tracking is limited by the scarcity of annotated data for the training of deep-learning models. Moreover, annotation, training, prediction, and proofreading currently lack a unified user interface. We present ELEPHANT, an interactive platform for 3D cell tracking that addresses these challenges by taking an incremental approach to deep learning. ELEPHANT provides an interface that seamlessly integrates cell track annotation, deep learning, prediction, and proofreading. This enables users to implement cycles of incremental learning starting from a few annotated nuclei. Successive prediction-validation cycles enrich the training data, leading to rapid improvements in tracking performance. We test the software's performance against state-of-the-art methods and track lineages spanning the entire course of leg regeneration in a crustacean over 1 week (504 time-points). ELEPHANT yields accurate, fully-validated cell lineages with a modest investment in time and effort.},
	URL = {https://doi.org/10.7554/eLife.69380},
	journal = {eLife}
}

download as .bib file

License

BSD-2-Clause