PuSH - Publikationsserver des Helmholtz Zentrums München

Padovani, F. ; Stegmaier, T. ; Mairhörmann, B. ; Schmoller, K.M.

Analysis of multidimensional microscopy data using cell-ACDC.

J. Vis. Exp. 2025 (2025)
Verlagsversion Forschungsdaten DOI PMC
Open Access Hybrid
Creative Commons Lizenzvertrag
Recent advances in quantitative microscopy for the life sciences have enabled experimental biologists to probe cells with unprecedented resolution and speed. At the same time, the AI revolution has dramatically increased the amount of information that can be extracted from multidimensional microscopy data. However, the large amount of data generated and the complexity of state-of-the-art AI models pose a severe bottleneck at the image analysis stage. Cell-ACDC is an open-source, user-friendly software that provides a powerful end-to-end solution for segmentation, tracking, and quantitative analysis of single cells in multidimensional microscopy data. It is tailored for experimental biologists who may lack the advanced technical expertise required to implement such models. This article shows how to utilize the framework to easily leverage the most recent models, along with many tools for smart and semi-automated data correction, to maximize the amount of biological information obtainable. Cell-ACDC supports multi-channel, time-lapse, and z-stack microscopy data, and provides a dedicated toolset tailored to each type of data dimensionality. Because of its modular design, which allows new models to be seamlessly integrated and directly accessed by biologists, Cell-ACDC has the potential to serve as a reference tool for microscopy data analysis.
Altmetric
Weitere Metriken?
[➜Einloggen]
Zusatzinfos bearbeiten [➜Einloggen]
Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
ISSN (print) / ISBN 1940-087X
e-ISSN 1940-087X
Zeitschrift Journal of Visualized Experiments
Quellenangaben Band: 2025, Heft: 225 Seiten: , Artikelnummer: , Supplement: ,
Verlag JoVE
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Functional Epigenetics (IFE)
Institute of Network Biology (INET)
Institute of Computational Biology (ICB)