cellacdc 1.2.4rc57

Cell segmentation, tracking and event annotation

Cell-ACDC

A GUI-based Python framework for segmentation, tracking, cell cycle annotations and quantification of microscopy data

Written in Python 3 by Francesco Padovani and Benedikt Mairhoermann.

Overview of pipeline and GUI

Resources

• Installation
• User Manual with detailed instructions
• Publication of Cell-ACDC
• Forum for discussions (feel free to ask any question)
• Report issues, request a feature or ask questions by opening a new issue here.
• Twitter thread

Citation

If you find Cell-ACDC useful, please cite:

Francesco Padovani, Benedikt Mairhörmann, Pascal Falter-Braun, Jette Lengefeld, and Kurt M. Schmoller Segmentation, tracking and cell cycle analysis of live-cell imaging data with Cell-ACDC. BMC Biol 20, 174 (2022) https://doi.org/10.1186/s12915-022-01372-6

How to contribute

Contributions to Cell-ACDC are always very welcome! For more details see instructions here.

Overview

Let's face it, when dealing with segmentation of microscopy data we often do not have time to check that everything is correct, because it is a tedious and very time consuming process. Cell-ACDC comes to the rescue! We combined the currently best available neural network models (such as YeaZ, cellpose, StarDist, YeastMate, omnipose, delta, etc.) and we complemented them with a fast and intuitive GUI.

We developed and implemented several smart functionalities such as real-time continuous tracking, automatic propagation of error correction, and several tools to facilitate manual correction, from simple yet useful brush and eraser to more complex flood fill (magic wand) and Random Walker segmentation routines.

See below how it compares to other popular tools available (Table 1 of our publication).

Is it only about segmentation?

Of course not! Cell-ACDC automatically computes several single-cell numerical features such as cell area and cell volume, plus the mean, max, median, sum and quantiles of any additional fluorescent channel's signal. It even performs background correction, to compute the protein amount and concentration.

You can load and analyse single 2D images, 3D data (3D z-stacks or 2D images over time) and even 4D data (3D z-stacks over time).

Finally, we provide Jupyter notebooks to visualize and interactively explore the data produced.

Do not hesitate to contact me here on GitHub (by opening an issue) or directly at my email here for any problem and/or feedback on how to improve the user experience!

Update v1.2.4

First release that is finally available on PyPi.

Main new feature: custom trackers! You can now add any tracker you want by implementing a simple tracker class. See the manual at the section "Adding trackers to the pipeline".

Additionally, this release includes many UI/UX improvements such as color and style customisation, alongside a inverted LUTs.

Installation using Anaconda (recommended)

NOTE: If you don't know what Anaconda is or you are not familiar with it, we recommend reading the detailed installation instructions found in manual here.

1. Install Anaconda or Miniconda for Python 3.9. IMPORTANT: For Windows make sure to choose the 64 bit version.
2. Open a terminal. On Windows, use the Anaconda Prompt and NOT the Command Prompt.
3. Update conda with conda update conda. Optionally, consider removing unused packages with the command conda clean --all
4. Create a virtual environment with the command conda create -n acdc python=3.9
5. Upgrade pip with the command python -m pip install --upgrade pip
6. Activate the environment conda activate acdc
7. Install Cell-ACDC with the command pip install cellacdc

Installation using Pip

1. Download and install Python 3.9
2. Open a terminal. On Windows we recommend using the PowerShell that you can install from here. On macOS use the Terminal app.
3. Upgrade pip: Windows: py -m pip install --updgrade pip, macOS/Unix: python3 -m pip install --updgrade pip
4. Navigate to a folder where you want to create the virtual environment
5. Create a virtual environment: Windows: py -m venv acdc, macOS/Unix python3 -m venv acdc
6. Activate the environment: Windows: .\acdc\Scripts\activate, macOS/Unix: source acdc/bin/activate
7. Install Cell-ACDC with the command pip install cellacdc

Install from source

If you want to contribute or try out experimental features (and, if you have time, maybe report a bug or two :D), you can install the developer version from source as follows:

1. Open a terminal and navigate to a folder where you want to download Cell-ACDC
2. Clone the repo with the command git clone https://github.com/SchmollerLab/Cell_ACDC.git (if you are on Windows you need to install git first. Install it from here)
3. Install Anaconda or Miniconda
4. Update conda with conda update conda. Optionally, consider removing unused packages with the command conda clean --all
5. Create a new conda environment with the command conda create -n acdc python=3.9
6. Activate the environment with the command conda activate acdc
7. In the terminal, navigate to the Cell_ACDC folder that you cloned before and install Cell-ACDC with the command pip install -e ..

Running Cell-ACDC

1. Open a terminal (on Windows use the Anaconda Prompt if you installed with conda otherwise we recommend installing and using the PowerShell 7)
2. Activate the environment (conda: conda activate acdc, pip on Windows: .\env\Scripts\activate, pip on Unix: source env/bin/activate)
3. Run the command acdc or cellacdc

Usage

For details about how to use Cell-ACDC please read the User Manual downloadable from here