YeastMate: Neural network-assisted segmentation of mating and budding events in <i>S. cerevisiae</i>

Bunk, David M.; Moriasy, Julian; Thoma, Felix; Jakubke, Christopher; Osman, Christof; Hörl, David

doi:10.1101/2021.10.13.464238

Cited by 1 publication

(3 citation statements)

References 15 publications

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“…Here we present an open-source graphical user interface (GUI)-based framework (written in Python 3) embedding state-of-the-art neural networks (YeaZ [5], Cellpose [6], StarDist [8] and YeastMate [7]) selectable by the user and smart algorithms that allow for fast, replicable, and accurate microscopy image analysis. The provided tools cover the entire image analysis pipeline from a raw microscopy file to the quantification of the feature of interest.…”

Section: Methodsmentioning

confidence: 99%

“…While it is possible to perform segmentation for single frames in the GUI, we highly recommend using the dedicated segmentation and tracking script for whole batches. We embedded four neural networks that were recently published: YeaZ [5] and YeastMate [7] for yeast cells, and Cellpose [6] and StarDist [8] for multiple model organisms (bright-field and phase contrast). The modularity of the code allows for easy and quick implementation of any other segmentation algorithm (traditional or deep-learning-based).…”

Section: Methodsmentioning

confidence: 99%

“…More specifically, in the case of live-cell microscopy of yeast and other organisms (e.g. mammalian stem cells), neural networks recently published (YeaZ [5], Cellpose [6], YeastMate [7] and StarDist [8]) drastically improved the segmentation process. However, even these neural networks do not achieve perfect segmentation, and-depending on the question-manual verification or correction of segmentation and tracking is often still essential for high-quality microscopy image analysis.…”

Section: Open Accessmentioning

confidence: 99%

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

et al. 2022

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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

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