A convolutional neural network segments yeast microscopy images with high accuracy

Nicola Dietler, Matthias Minder, Vojislav Gligorovski, Augoustina Maria Economou, Denis Alain Henri Lucien Joly, Ahmad Sadeghi, Chun Hei Michael Chan, Mateusz Koziński, Martin Weigert, Anne-Florence Bitbol and Sahand Jamal Rahi ()
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Nicola Dietler: École polytechnique fédérale de Lausanne (EPFL)
Matthias Minder: École polytechnique fédérale de Lausanne (EPFL)
Vojislav Gligorovski: École polytechnique fédérale de Lausanne (EPFL)
Augoustina Maria Economou: École polytechnique fédérale de Lausanne (EPFL)
Denis Alain Henri Lucien Joly: École polytechnique fédérale de Lausanne (EPFL)
Ahmad Sadeghi: École polytechnique fédérale de Lausanne (EPFL)
Chun Hei Michael Chan: École polytechnique fédérale de Lausanne (EPFL)
Mateusz Koziński: École polytechnique fédérale de Lausanne (EPFL)
Martin Weigert: École polytechnique fédérale de Lausanne (EPFL)
Anne-Florence Bitbol: École polytechnique fédérale de Lausanne (EPFL)
Sahand Jamal Rahi: École polytechnique fédérale de Lausanne (EPFL)

Nature Communications, 2020, vol. 11, issue 1, 1-8

Abstract: Abstract The 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.

Date: 2020
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DOI: 10.1038/s41467-020-19557-4

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