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

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 mot...

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Bibliographic Details
Published in:Bioinformatics (Oxford, England) England), 2022-02
Main Authors: Bunk, David, Moriasy, Julian, Thoma, Felix, Jakubke, Christopher, Osman, Christof, Hörl, David
Format: Article
Language:English
Online Access:Get full text
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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 stand-alone GUI application and a Fiji plugin as easy to use frontends. 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 data are available at Bioinformatics online.
ISSN:1367-4811
DOI:10.1093/bioinformatics/btac107