1. YeastMate: neural network-assisted segmentation of mating and budding events in Saccharomyces cerevisiae.
- Author
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Bunk D, Moriasy J, Thoma F, Jakubke C, Osman C, and Hörl D
- Subjects
- Microscopy, Neural Networks, Computer, Gene Library, Saccharomyces cerevisiae, Software
- 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., (© The Author(s) 2022. Published by Oxford University Press.)
- Published
- 2022
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