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bioRxiv · 10.1101/2021.10.13.464238

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

Bunk, D.·Moriasy, J.·Thoma, F.·Jakubke, C.·Osman, C.·Hörl, D.
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Abstract

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 packaged installers for our whole software stack for Windows, macOS and Linux. A detailed user guide is available at https://yeastmate.readthedocs.io.

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Bunk, D., Moriasy, J., Thoma, F., Jakubke, C., Osman, C., Hörl, D.. 2021-10-14. YeastMate: Neural network-assisted segmentation of mating and budding events in S. cerevisiae. https://doi.org/10.1101/2021.10.13.464238

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