A convolutional neural network segments yeast microscopy images with high accuracy

Author:

Dietler Nicola,Minder Matthias,Gligorovski VojislavORCID,Economou Augoustina MariaORCID,Joly Denis Alain Henri Lucien,Sadeghi Ahmad,Chan Chun Hei Michael,Koziński MateuszORCID,Weigert MartinORCID,Bitbol Anne-FlorenceORCID,Rahi Sahand JamalORCID

Abstract

AbstractThe identification of cell borders (‘segmentation’) in microscopy images constitutes a bottleneck for large-scale experiments. For the model organism Saccharomyces cerevisiae, current segmentation methods face challenges when cells bud, crowd, or exhibit irregular features. We present a convolutional neural network (CNN) named YeaZ, the underlying training set of high-quality segmented yeast images (>10 000 cells) including mutants, stressed cells, and time courses, as well as a graphical user interface and a web application (www.quantsysbio.com/data-and-software) to efficiently employ, test, and expand the system. A key feature is a cell-cell boundary test which avoids the need for fluorescent markers. Our CNN is highly accurate, including for buds, and outperforms existing methods on benchmark images, indicating it transfers well to other conditions. To demonstrate how efficient large-scale image processing uncovers new biology, we analyze the geometries of ≈2200 wild-type and cyclin mutant cells and find that morphogenesis control occurs unexpectedly early and gradually.

Funder

Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung

EPFL

Publisher

Springer Science and Business Media LLC

Subject

General Physics and Astronomy,General Biochemistry, Genetics and Molecular Biology,General Chemistry

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