YeastMate: neural network-assisted segmentation of mating and budding events in Saccharomyces cerevisiae.

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

SUBMITTER: Bunk D 

PROVIDER: S-EPMC9048668 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature