Citation
If you use Cell-ACDC in your publication, 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
Important
When citing Cell-ACDC make sure to also cite the paper of the segmentation models and trackers you used! Below you find the links to the models currently available in Cell-ACDC.
Segmentation models
Trackers
BibTeX entry
@ARTICLE{CellACDC,
title = "Segmentation, tracking and cell cycle analysis of live-cell
imaging data with {Cell-ACDC}",
author = "Padovani, Francesco and Mairh{\"o}rmann, Benedikt and
Falter-Braun, Pascal and Lengefeld, Jette and Schmoller, Kurt M",
abstract = "BACKGROUND: High-throughput live-cell imaging is a powerful tool
to study dynamic cellular processes in single cells but creates
a bottleneck at the stage of data analysis, due to the large
amount of data generated and limitations of analytical
pipelines. Recent progress on deep learning dramatically
improved cell segmentation and tracking. Nevertheless, manual
data validation and correction is typically still required and
tools spanning the complete range of image analysis are still
needed. RESULTS: We present Cell-ACDC, an open-source
user-friendly GUI-based framework written in Python, for
segmentation, tracking and cell cycle annotations. We included
state-of-the-art deep learning models for single-cell
segmentation of mammalian and yeast cells alongside cell
tracking methods and an intuitive, semi-automated workflow for
cell cycle annotation of single cells. Using Cell-ACDC, we found
that mTOR activity in hematopoietic stem cells is largely
independent of cell volume. By contrast, smaller cells exhibit
higher p38 activity, consistent with a role of p38 in regulation
of cell size. Additionally, we show that, in S. cerevisiae,
histone Htb1 concentrations decrease with replicative age.
CONCLUSIONS: Cell-ACDC provides a framework for the application
of state-of-the-art deep learning models to the analysis of live
cell imaging data without programming knowledge. Furthermore, it
allows for visualization and correction of segmentation and
tracking errors as well as annotation of cell cycle stages. We
embedded several smart algorithms that make the correction and
annotation process fast and intuitive. Finally, the open-source
and modularized nature of Cell-ACDC will enable simple and fast
integration of new deep learning-based and traditional methods
for cell segmentation, tracking, and downstream image analysis.
Source code: https://github.com/SchmollerLab/Cell\_ACDC.",
journal = "BMC Biol.",
publisher = "Springer Science and Business Media LLC",
volume = 20,
number = 1,
pages = "174",
month = aug,
year = 2022,
keywords = "Bioimage analysis; Cell cycle analysis; Cell tracking;
Deep-learning cell segmentation; Live-cell imaging",
copyright = "https://creativecommons.org/licenses/by/4.0",
language = "en",
doi = "10.1186/s12915-022-01372-6",
}
RIS entry
TY - JOUR
AU - Padovani, Francesco
AU - Mairhörmann, Benedikt
AU - Falter-Braun, Pascal
AU - Lengefeld, Jette
AU - Schmoller, Kurt M
AD - Institute of Functional Epigenetics (IFE), Molecular Targets and
Therapeutics Center (MTTC), Helmholtz Center Munich, 85764,
Munich-Neuherberg, Germany. francesco.padovani@helmholtz-muenchen.de.;
Institute of Functional Epigenetics (IFE), Molecular Targets and
Therapeutics Center (MTTC), Helmholtz Center Munich, 85764,
Munich-Neuherberg, Germany.; Institute of Network Biology (INET),
Molecular Targets and Therapeutics Center (MTTC), Helmholtz Center Munich,
85764, Munich-Neuherberg, Germany.; Institute of Network Biology (INET),
Molecular Targets and Therapeutics Center (MTTC), Helmholtz Center Munich,
85764, Munich-Neuherberg, Germany.; Microbe-Host Interactions, Faculty of
Biology, Ludwig-Maximilians-University (LMU) München, 82152, Planegg-,
Martinsried, Germany.; Institute of Biotechnology, HiLIFE, University of
Helsinki, Biocenter 2, P.O.Box 56 (Viikinkaari 5 D), 00014, Helsinki,
Finland.; Department of Biosciences and Nutrition (BioNut), Karolinska
Institutet, Huddinge, Sweden.; Institute of Functional Epigenetics (IFE),
Molecular Targets and Therapeutics Center (MTTC), Helmholtz Center Munich,
85764, Munich-Neuherberg, Germany. kurt.schmoller@helmholtz-muenchen.de.;
German Center for Diabetes Research (DZD), 85764, Munich-Neuherberg,
Germany. kurt.schmoller@helmholtz-muenchen.de.
TI - Segmentation, tracking and cell cycle analysis of live-cell imaging data
with Cell-ACDC
T2 - BMC Biol.
JF - BMC Biology
VL - 20
IS - 1
SP - 174
PY - 2022
DA - 2022/8/5
PB - Springer Science and Business Media LLC
AB - BACKGROUND: High-throughput live-cell imaging is a powerful tool to study
dynamic cellular processes in single cells but creates a bottleneck at the
stage of data analysis, due to the large amount of data generated and
limitations of analytical pipelines. Recent progress on deep learning
dramatically improved cell segmentation and tracking. Nevertheless, manual
data validation and correction is typically still required and tools
spanning the complete range of image analysis are still needed. RESULTS:
We present Cell-ACDC, an open-source user-friendly GUI-based framework
written in Python, for segmentation, tracking and cell cycle annotations.
We included state-of-the-art deep learning models for single-cell
segmentation of mammalian and yeast cells alongside cell tracking methods
and an intuitive, semi-automated workflow for cell cycle annotation of
single cells. Using Cell-ACDC, we found that mTOR activity in
hematopoietic stem cells is largely independent of cell volume. By
contrast, smaller cells exhibit higher p38 activity, consistent with a
role of p38 in regulation of cell size. Additionally, we show that, in S.
cerevisiae, histone Htb1 concentrations decrease with replicative age.
CONCLUSIONS: Cell-ACDC provides a framework for the application of
state-of-the-art deep learning models to the analysis of live cell imaging
data without programming knowledge. Furthermore, it allows for
visualization and correction of segmentation and tracking errors as well
as annotation of cell cycle stages. We embedded several smart algorithms
that make the correction and annotation process fast and intuitive.
Finally, the open-source and modularized nature of Cell-ACDC will enable
simple and fast integration of new deep learning-based and traditional
methods for cell segmentation, tracking, and downstream image analysis.
Source code: https://github.com/SchmollerLab/Cell_ACDC.
SN - 1741-7007
DO - 10.1186/s12915-022-01372-6
C2 - PMC9356409
UR - http://dx.doi.org/10.1186/s12915-022-01372-6
UR - https://www.ncbi.nlm.nih.gov/pubmed/35932043
UR - https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9356409
KW - Bioimage analysis
KW - Cell cycle analysis
KW - Cell tracking
KW - Deep-learning cell segmentation
KW - Live-cell imaging
ER -