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Bunk, David; Moriasy, Julian; Thoma, Felix; Jakubke, Christopher; Osman, Christof und Hoerl, David (2022): YeastMate: neural network-assisted segmentation of mating and budding events in Saccharomyces cerevisiae. In: Bioinformatics, Bd. 38, Nr. 9: S. 2667-2669

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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 standalone application with a graphical user interface (GUI) and a Fiji plugin as easy-to-use frontends.

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