cellacdc

Welcome to 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.

Core developers: Francesco Padovani, Timon Stegmaier, and Benedikt Mairhoermann.

Overview of pipeline and GUI

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 Segment Anything Model (SAM), YeaZ, cellpose, StarDist, YeastMate, omnipose, delta, DeepSea, 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 the table below how it compares to other popular tools available (Table 1 of our publication).

Comparison of Cell-ACDC with other tools
Feature	Cell-ACDC	YeaZ	Cell-pose	Yeast-Mate	Deep-Cell	Phylo-Cell	Cell-Profiler	ImageJ Fiji	Yeast-Spotter	Yeast-Net	Morpho-LibJ
Deep-learning segmentation	✅	✅	✅	✅	✅	❌	✅	✅	✅	✅	❌
Traditional segmentation	✅	❌	❌	❌	❌	✅	✅	✅	❌	❌	✅
Tracking	✅	✅	❌	❌	✅	✅	✅	✅	❌	❌	❌
Manual corrections	✅	✅	✅	✅	✅	✅	✅	✅	❌	❌	✅
Automatic real-time tracking	✅	❌	❌	❌	❌	❌	❌	❌	❌	❌	❌
Automatic propagation of corrections	✅	❌	❌	❌	❌	✅	❌	❌	❌	❌	❌
Automatic mother-bud pairing	✅	❌	❌	✅	❌	✅	❌	❌	❌	❌	❌
Pedigree annotations	✅	❌	❌	✅	✅	✅	✅	✅	❌	❌	❌
Cell division annotations	✅	❌	❌	❌	❌	✅	✅	✅	❌	❌	❌
Downstream analysis	✅	❌	❌	❌	✅	✅	✅	✅	❌	❌	❌
3D z-stacks	✅	❌	✅	❌	✅	❌	✅	✅	❌	❌	✅
Multiple model organisms	✅	❌	✅	❌	✅	❌	✅	✅	❌	❌	✅
Bio-formats	✅	❌	❌	❌	❌	❌	✅	✅	❌	❌	✅
User manual	✅	✅	✅	✅	✅	❌	✅	✅	✅	✅	✅
Open source	✅	✅	✅	✅	✅	✅	✅	✅	✅	✅	✅
Does not require a licence	✅	✅	✅	✅	✅	❌	✅	✅	✅	✅	✅

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.

Scientific publications where Cell-ACDC was used

Check here for a list of the scientific publications where Cell-ACDC was used.

Resources

Complete user guide
Installation guide
User manual
Publication of Cell-ACDC
Image.sc Forum to ask any question. Make sure to tag the Topic with the tag cell-acdc
GitHub issues for reporting issues, request a feature or ask questions
X thread
Scientific publications where Cell-ACDC was used

Citing Cell-ACDC and the available models

If you find Cell-ACDC useful, please cite it as follows:

Padovani, F., Mairhörmann, B., Falter-Braun, P., Lengefeld, J. & Schmoller, K. M. Segmentation, tracking and cell cycle analysis of live-cell imaging data with Cell-ACDC. BMC Biology 20, 174 (2022). DOI: 10.1186/s12915-022-01372-6

Alternatively, you can also use Cite this repository button on the right ribbon of the GitHub page.

IMPORTANT: when citing Cell-ACDC make sure to also cite the paper of the segmentation models and trackers you used! See here for a list of models currently available in Cell-ACDC.

Contact

Do not hesitate to contact us here on GitHub (by opening an issue) or directly at the email padovaf@tcd.ie for any problem and/or feedback on how to improve the user experience!

Contributing

At Cell-ACDC we encourage contributions to the code! Please read our contributing guide to get started.

Metadata

Release files for cellacdc 1.8.0

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for cellacdc 1.8.0
File	Size	Uploaded
cellacdc-1.8.0.tar.gz	25.7 MB	Sep 1, 2026	Details

Built distribution (wheel)

Table of built distributions (wheels) for cellacdc 1.8.0
File	Interpreter	ABI	Platform	Reset
cellacdc-1.8.0-py3-none-any.whl 21.0 MB Sep 1, 2026	Python 3	none	any	Details

Total release size: 46.7 MB

Release files / cellacdc-1.8.0.tar.gz

Download URL	cellacdc-1.8.0.tar.gz
Size	25.7 MB
Tags	Source
SHA-256 checksum How to use checksums	`b58059eddae64f030c36bf38aa0810191a8ed0f4951d0d1742e9fe8ca73eb115`
BLAKE2b-256 checksum How to use checksums	`e9d0bd942d6f8f0097efb751acfc7fe665dc9362e64f215b7c2c72aa0a64b0ee`
Upload date	Sep 1, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/6.2.0 CPython/3.12.12`

Release files / cellacdc-1.8.0-py3-none-any.whl

Download URL	cellacdc-1.8.0-py3-none-any.whl
Size	21.0 MB
Tags	Python 3
SHA-256 checksum How to use checksums	`66404cf32a6170d11952353b7e4a9327fb30daf9acc84b160ceb8c6ad5410784`
BLAKE2b-256 checksum How to use checksums	`80fef5a3c9b5a7ce0fc3824055ab583c1b8cc796e1fbe3d2d3341b81d88c2245`
Upload date	Sep 1, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/6.2.0 CPython/3.12.12`