delta2 2.0.6

Segments and tracks bacteria

DeLTA

NOTE This is version 2 of the DeLTA pipeline. For version 1, please check out branch 'version1'

DeLTA (Deep Learning for Time-lapse Analysis) is a deep learning-based image processing pipeline for segmenting and tracking single cells in time-lapse microscopy movies.

:scroll: To get started check out the documentation at delta.readthedocs.io

:bug: If you encounter bugs or have questions about the software, please use Gitlab's issue system

For the latest hotness check out the dev branch. You can also quickly test DeLTA on our data or your own with Google Colab for free here

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See also our papers for more details:

Version 2: O’Connor OM, Alnahhas RN, Lugagne J-B, Dunlop MJ (2022) DeLTA 2.0: A deep learning pipeline for quantifying single-cell spatial and temporal dynamics. PLoS Comput Biol 18(1): e1009797

Version 1: Lugagne J-B, Lin H, & Dunlop MJ (2020) DeLTA: Automated cell segmentation, tracking, and lineage reconstruction using deep learning. PLoS Comput Biol 16(4): e1007673

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Contributions

A big thank you to the following people who shared their data and training sets with us, they help us make DeLTA more generalizable:

• Simon van Vliet at University of Basel
• Zoran Marinković and Marianne Grognot in Philippe Nghe's group at ESPCI Paris
• Noah Olsman and Daniel Eaton in Johan Paulsson's group at Harvard
• Shuai Yang in Fan Jin's group at Shenzhen Institutes of Advanced Technology
• Jordi van Gestel and Noam Golan in Avigdor Eldar's group at Tel-Aviv University

Please reach out if you have created your own sets and think they would be helpful to the community!