Neural network control of focal position during time-lapse microscopy of cells

Abstract Live-cell microscopy is quickly becoming an indispensable technique for studying the dynamics of cellular processes. Maintaining the specimen in focus during image acquisition is crucial for high-throughput applications, especially for long experiments or when a large sample is being contin...

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Autores principales:	Ling Wei, Elijah Roberts
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2018
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/c2496b00bc6c46dfbd0577b8fbb0bc40
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spelling	oai:doaj.org-article:c2496b00bc6c46dfbd0577b8fbb0bc402021-12-02T16:07:50ZNeural network control of focal position during time-lapse microscopy of cells10.1038/s41598-018-25458-w2045-2322https://doaj.org/article/c2496b00bc6c46dfbd0577b8fbb0bc402018-05-01T00:00:00Zhttps://doi.org/10.1038/s41598-018-25458-whttps://doaj.org/toc/2045-2322Abstract Live-cell microscopy is quickly becoming an indispensable technique for studying the dynamics of cellular processes. Maintaining the specimen in focus during image acquisition is crucial for high-throughput applications, especially for long experiments or when a large sample is being continuously scanned. Automated focus control methods are often expensive, imperfect, or ill-adapted to a specific application and are a bottleneck for widespread adoption of high-throughput, live-cell imaging. Here, we demonstrate a neural network approach for automatically maintaining focus during bright-field microscopy. Z-stacks of yeast cells growing in a microfluidic device were collected and used to train a convolutional neural network to classify images according to their z-position. We studied the effect on prediction accuracy of the various hyperparameters of the neural network, including downsampling, batch size, and z-bin resolution. The network was able to predict the z-position of an image with ±1 μm accuracy, outperforming human annotators. Finally, we used our neural network to control microscope focus in real-time during a 24 hour growth experiment. The method robustly maintained the correct focal position compensating for 40 μm of focal drift and was insensitive to changes in the field of view. About ~100 annotated z-stacks were required to train the network making our method quite practical for custom autofocus applications.Ling WeiElijah RobertsNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 8, Iss 1, Pp 1-10 (2018)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Ling Wei Elijah Roberts Neural network control of focal position during time-lapse microscopy of cells
description	Abstract Live-cell microscopy is quickly becoming an indispensable technique for studying the dynamics of cellular processes. Maintaining the specimen in focus during image acquisition is crucial for high-throughput applications, especially for long experiments or when a large sample is being continuously scanned. Automated focus control methods are often expensive, imperfect, or ill-adapted to a specific application and are a bottleneck for widespread adoption of high-throughput, live-cell imaging. Here, we demonstrate a neural network approach for automatically maintaining focus during bright-field microscopy. Z-stacks of yeast cells growing in a microfluidic device were collected and used to train a convolutional neural network to classify images according to their z-position. We studied the effect on prediction accuracy of the various hyperparameters of the neural network, including downsampling, batch size, and z-bin resolution. The network was able to predict the z-position of an image with ±1 μm accuracy, outperforming human annotators. Finally, we used our neural network to control microscope focus in real-time during a 24 hour growth experiment. The method robustly maintained the correct focal position compensating for 40 μm of focal drift and was insensitive to changes in the field of view. About ~100 annotated z-stacks were required to train the network making our method quite practical for custom autofocus applications.
format	article
author	Ling Wei Elijah Roberts
author_facet	Ling Wei Elijah Roberts
author_sort	Ling Wei
title	Neural network control of focal position during time-lapse microscopy of cells
title_short	Neural network control of focal position during time-lapse microscopy of cells
title_full	Neural network control of focal position during time-lapse microscopy of cells
title_fullStr	Neural network control of focal position during time-lapse microscopy of cells
title_full_unstemmed	Neural network control of focal position during time-lapse microscopy of cells
title_sort	neural network control of focal position during time-lapse microscopy of cells
publisher	Nature Portfolio
publishDate	2018
url	https://doaj.org/article/c2496b00bc6c46dfbd0577b8fbb0bc40
work_keys_str_mv	AT lingwei neuralnetworkcontroloffocalpositionduringtimelapsemicroscopyofcells AT elijahroberts neuralnetworkcontroloffocalpositionduringtimelapsemicroscopyofcells
_version_	1718384743004766208

Neural network control of focal position during time-lapse microscopy of cells

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