Metadata-Version: 2.1
Name: delta2
Version: 2.0.4
Summary: Segments and tracks bacteria
Home-page: https://gitlab.com/dunloplab/delta
Author: Jean-Baptiste Lugagne, Owen OConnor
Author-email: jblugagne@bu.edu, ooconnor@bu.edu
License: MIT License
Platform: UNKNOWN
Requires-Python: >=3.7
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: scikit-image (>=0.18)
Requires-Dist: tifffile (>=2020)
Requires-Dist: opencv-python (>=4.1)
Requires-Dist: tensorflow (>=2.0)
Requires-Dist: ffmpeg-python
Requires-Dist: requests

# 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.

![](https://gitlab.com/dunloplab/delta/-/raw/images/DeLTAexample.gif)

:scroll: To get started check out the documentation at [delta.readthedocs.io](https://delta.readthedocs.io)

:bug: If you encounter bugs or have questions about the software, please use [Gitlab's issue system](https://gitlab.com/dunloplab/delta/-/issues)

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](https://colab.research.google.com/drive/1UL9oXmcJFRBAm0BMQy_DMKg4VHYGgtxZ?usp=sharing)

--------------------------
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](https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1009797)

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](https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1007673)


