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
Reference31 articles.
1. Carpenter, A. E. et al. Cellprofiler: image analysis software for identifying and quantifying cell phenotypes. Genome Biol. 7, R100 (2006).
2. Gordon, A. et al. Single-cell quantification of molecules and rates using open-source microscope-based cytometry. Nat. Methods 4, 175–181 (2007).
3. Wang, Q., Niemi, J., Tan, C.-M., You, L. & West, M. Image segmentation and dynamic lineage analysis in single-cell fluorescence microscopy. Cytom. A 77A, 101–110 (2009).
4. Bredies, K. & Wolinski, H. An active-contour based algorithm for the automated segmentation of dense yeast populations on transmission microscopy images. Comput. Vis. Sci. 14, 341–352 (2011).
5. Pelet, S., Dechant, R., Lee, S. S., van Drogen, F. & Peter, M. An integrated image analysis platform to quantify signal transduction in single cells. Integr. Biol. 4, 1274–1282 (2012).
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