A convolutional neural network segments yeast microscopy images with high accuracy
A convolutional neural network segments yeast microscopy images with high accuracy
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London: Nature Publishing Group UK
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English
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London: Nature Publishing Group UK
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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...
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A convolutional neural network segments yeast microscopy images with high accuracy
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TN_cdi_doaj_primary_oai_doaj_org_article_1386b197c37e4a83a88513c47ac98ec5
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https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_1386b197c37e4a83a88513c47ac98ec5
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ISSN
2041-1723
E-ISSN
2041-1723
DOI
10.1038/s41467-020-19557-4
