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Updated: Jan 10, 2026

Analysis of Multidimensional Microscopy Data Using Cell-ACDC

Analysis of Multidimensional Microscopy Data Using Cell-ACDC

Published on: November 7, 2025

Analysis of Multidimensional Microscopy Data Using Cell-ACDC.

Francesco Padovani¹, Timon Stegmaier², Benedikt Mairhörmann³

¹Institute of Functional Epigenetics, Molecular Targets and Therapeutics Center, Helmholtz Zentrum München; francesco.padovani@helmholtz-munich.de.

Journal of Visualized Experiments : Jove

|November 24, 2025

Summary

Cell-ACDC is a new open-source software simplifying complex AI-driven image analysis for biologists. It enables efficient segmentation, tracking, and quantitative analysis of microscopy data, maximizing biological insights.

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Last Updated: Jan 10, 2026

Analysis of Multidimensional Microscopy Data Using Cell-ACDC

Analysis of Multidimensional Microscopy Data Using Cell-ACDC

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Improved Visualization and Quantitative Analysis of Drug Effects Using Micropatterned Cells

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Fabrication of a Multiplexed Artificial Cellular MicroEnvironment Array

Fabrication of a Multiplexed Artificial Cellular MicroEnvironment Array

Published on: September 7, 2018

Area of Science:

Life Sciences
Quantitative Microscopy
Artificial Intelligence in Biology

Background:

Quantitative microscopy generates vast, complex multidimensional data.
Advanced AI models enhance data extraction but pose analysis bottlenecks.
Experimental biologists often lack specialized technical skills for complex image analysis.

Purpose of the Study:

To introduce Cell-ACDC, an open-source software solution for microscopy image analysis.
To provide an end-to-end, user-friendly platform for segmentation, tracking, and quantitative analysis.
To empower experimental biologists by simplifying the use of advanced AI models.

Main Methods:

Development of Cell-ACDC, an open-source, user-friendly software.
Implementation of an end-to-end workflow for single-cell analysis in multidimensional microscopy data.
Integration of state-of-the-art AI models with tools for semi-automated data correction.

Main Results:

Cell-ACDC offers a powerful solution for segmentation, tracking, and quantitative analysis.
The software supports multi-channel, time-lapse, and z-stack microscopy data.
Its modular design allows seamless integration of new AI models.

Conclusions:

Cell-ACDC addresses the bottleneck in microscopy image analysis for life scientists.
It democratizes the use of advanced AI tools for biological research.
Cell-ACDC is poised to become a reference tool for quantitative microscopy data analysis.