RT Journal Article
SR Electronic
T1 Cell-ACDC: a user-friendly toolset embedding state-of-the-art neural networks for segmentation, tracking and cell cycle annotations of live-cell imaging data
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2021.09.28.462199
DO 10.1101/2021.09.28.462199
A1 Padovani, Francesco
A1 Mairhörmann, Benedikt
A1 Falter-Braun, Pascal
A1 Lengefeld, Jette
A1 Schmoller, Kurt M.
YR 2021
UL http://biorxiv.org/content/early/2021/09/30/2021.09.28.462199.abstract
AB Live-cell imaging is a powerful tool to study dynamic cellular processes on the level of single cells with quantitative detail. Microfluidics enables parallel high-throughput imaging, creating a downstream bottleneck at the stage of data analysis. Recent progress on deep learning image analysis dramatically improved cell segmentation and tracking. Nevertheless, manual data validation and correction is typically still required and broadly used tools spanning the complete range of live-cell imaging analysis, from cell segmentation to pedigree analysis and signal quantification, are still needed. Here, we present Cell-ACDC, a user-friendly graphical user-interface (GUI)-based framework written in Python, for segmentation, tracking and cell cycle annotation. We included two state-of-the-art and high-accuracy deep learning models for single-cell segmentation of yeast and mammalian cells implemented in the most used deep learning frameworks TensorFlow and PyTorch. Additionally, we developed and implemented a cell tracking method and embedded it into an intuitive, semi-automated workflow for label-free cell cycle annotation of single cells. 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 or downstream image analysis.Source code https://github.com/SchmollerLab/Cell_ACDCCompeting Interest StatementThe authors have declared no competing interest.