Welcome to DeLTA’s documentation!

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.

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

Gitlab repository: https://gitlab.com/dunloplab/delta

🐛 If you encounter bugs, have questions about the software, suggestions for new features, or even comments about the documentation, please use Gitlab’s issue system

Overview

DeLTA revolves around a pipeline that can process movies of rod-shaped bacteria growing in 2D setups such as agarose pads, as well as movies of E. coli cells trapped in a microfluidic device known as a “mother machine”. Our pipeline is centered around two U-Net neural networks that are used sequentially:

• To perform semantic binary segmentation of our cells as in the original U-Net paper.

• To track cells from one movie frame to the next, and to identify cell divisions and mother/daughter cells.

A third U-Net can be used to identify regions of interest in the image before perfoming segmentation. This is used with mother machine movies to identify single chambers, but by default only 1 ROI covering the entire field-of-view is used in the 2D version. The U-Nets are implemented in Tensorflow 2 via the Keras API.

Getting started

You can quickly check how DeLTA performs on your data for free by using our Google Colab notebook

To get started on your own system, check out the installation instructions, download our latest models, and run the pipeline on our data or your own.

See also our example scritps and results analysis examples

Contents

• Installation
  • Hardware requirements
  • Prerequisites
  • Install from conda-forge
  • Install with conda from delta_env.yml
  • Install with pip
  • Check installation
  • Import DeLTA
  • Troubleshooting
  • Recent installations library versions
• Downloading assets
  • Automated download
  • Download and path options
• Configuration files
  • Presets and configuration levels
  • Explicit config file path
  • Modify configuration on-the-fly
  • Old config imports
• Pipeline
  • Basic usage
  • Selectively process frames, positions, and features
  • Preprocessing
  • More details
  • Reloading objects
• Output files
  • Pickle file
  • MAT file
  • MP4 movie file
• Training with the data module
  • Training generators and datasets
  • Data augmentation
  • Prediction generators
• Utilities module
  • The XPreader class
  • The Lineage class
• Model module
• Example scripts
  • Run pipeline
  • Training scripts
  • Evaluation scripts
• Results analysis
  • 2D results from .pkl file and Position/Lineage object
  • Mother machine from MAT file

API

Navigate our API below:

delta

Created on Tue Sep 14 10:30:20 2021

Indices and tables

• Index

• Module Index

• Search Page