Segmentation, tracking and cell cycle analysis of live-cell imaging data with Cell-ACDC.

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 meth

SUBMITTER: Padovani F 

PROVIDER: S-EPMC9356409 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature