A convolutional neural network segments yeast microscopy images with high accuracy

Current cell segmentation methods for Saccharomyces cerevisiae face challenges under a variety of standard experimental and imaging conditions. Here the authors develop a convolutional neural network for accurate, label-free cell segmentation.

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Autores principales:	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, Sahand Jamal Rahi
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/1386b197c37e4a83a88513c47ac98ec5
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spelling	oai:doaj.org-article:1386b197c37e4a83a88513c47ac98ec52021-12-02T14:40:42ZA convolutional neural network segments yeast microscopy images with high accuracy10.1038/s41467-020-19557-42041-1723https://doaj.org/article/1386b197c37e4a83a88513c47ac98ec52020-11-01T00:00:00Zhttps://doi.org/10.1038/s41467-020-19557-4https://doaj.org/toc/2041-1723Current cell segmentation methods for Saccharomyces cerevisiae face challenges under a variety of standard experimental and imaging conditions. Here the authors develop a convolutional neural network for accurate, label-free cell segmentation.Nicola DietlerMatthias MinderVojislav GligorovskiAugoustina Maria EconomouDenis Alain Henri Lucien JolyAhmad SadeghiChun Hei Michael ChanMateusz KozińskiMartin WeigertAnne-Florence BitbolSahand Jamal RahiNature PortfolioarticleScienceQENNature Communications, Vol 11, Iss 1, Pp 1-8 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q 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 Sahand Jamal Rahi A convolutional neural network segments yeast microscopy images with high accuracy
description	Current cell segmentation methods for Saccharomyces cerevisiae face challenges under a variety of standard experimental and imaging conditions. Here the authors develop a convolutional neural network for accurate, label-free cell segmentation.
format	article
author	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 Sahand Jamal Rahi
author_facet	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 Sahand Jamal Rahi
author_sort	Nicola Dietler
title	A convolutional neural network segments yeast microscopy images with high accuracy
title_short	A convolutional neural network segments yeast microscopy images with high accuracy
title_full	A convolutional neural network segments yeast microscopy images with high accuracy
title_fullStr	A convolutional neural network segments yeast microscopy images with high accuracy
title_full_unstemmed	A convolutional neural network segments yeast microscopy images with high accuracy
title_sort	convolutional neural network segments yeast microscopy images with high accuracy
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/1386b197c37e4a83a88513c47ac98ec5
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A convolutional neural network segments yeast microscopy images with high accuracy

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