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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
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]