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YeastMate: neural network-assisted segmentation of mating and budding events in Saccharomyces cerevisiae.


ABSTRACT:

Summary

Here, we introduce YeastMate, a user-friendly deep learning-based application for automated detection and segmentation of Saccharomyces cerevisiae cells and their mating and budding events in microscopy images. We build upon Mask R-CNN with a custom segmentation head for the subclassification of mother and daughter cells during lifecycle transitions. YeastMate can be used directly as a Python library or through a standalone application with a graphical user interface (GUI) and a Fiji plugin as easy-to-use frontends.

Availability and implementation

The source code for YeastMate is freely available at https://github.com/hoerlteam/YeastMate under the MIT license. We offer installers for our software stack for Windows, macOS and Linux. A detailed user guide is available at https://yeastmate.readthedocs.io.

Supplementary information

Supplementary data are available at Bioinformatics online.

SUBMITTER: Bunk D 

PROVIDER: S-EPMC9048668 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature

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Publications

YeastMate: neural network-assisted segmentation of mating and budding events in Saccharomyces cerevisiae.

Bunk David D   Moriasy Julian J   Thoma Felix F   Jakubke Christopher C   Osman Christof C   Hörl David D  

Bioinformatics (Oxford, England) 20220401 9


<h4>Summary</h4>Here, we introduce YeastMate, a user-friendly deep learning-based application for automated detection and segmentation of Saccharomyces cerevisiae cells and their mating and budding events in microscopy images. We build upon Mask R-CNN with a custom segmentation head for the subclassification of mother and daughter cells during lifecycle transitions. YeastMate can be used directly as a Python library or through a standalone application with a graphical user interface (GUI) and a  ...[more]

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