Neural network control of focal position during time-lapse microscopy of cells
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 sc...
Saved in:
| Published in: | Scientific reports Vol. 8; no. 1; pp. 7313 - 10 |
|---|---|
| Main Authors: | , |
| Format: | Journal Article |
| Language: | English |
| Published: |
London
Nature Publishing Group UK
09.05.2018
Nature Publishing Group Nature Portfolio |
| Subjects: | |
| ISSN: | 2045-2322, 2045-2322 |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| 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
μ
m 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
μ
m 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. |
|---|---|
| AbstractList | 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. 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. 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.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. 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 μ m 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 μ m 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. |
| ArticleNumber | 7313 |
| Author | Wei, Ling Roberts, Elijah |
| Author_xml | – sequence: 1 givenname: Ling surname: Wei fullname: Wei, Ling organization: Department of Biophysics, Johns Hopkins University – sequence: 2 givenname: Elijah surname: Roberts fullname: Roberts, Elijah email: eroberts@jhu.edu organization: Department of Biophysics, Johns Hopkins University |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/29743647$$D View this record in MEDLINE/PubMed |
| BookMark | eNp9UslO3TAUtSpQoZQf6KKK1E03Ac-JN5Uq1AEJwYauLce5fvVrEqd2whN_X4dQpgXe2Lr3nOM7nHdobwgDIPSB4BOCWX2aOBGqLjGpSyq4qMvdG3RIMRclZZTuPXkfoOOUtjgfQRUn6i06oKriTPLqEF1ewhxNVwww7UL8U9gwTDF0RXCFCzYnxpD85MNQtHP0w6aYfA9lZ8YERe9tDMmG8XaBW-i69B7tO9MlOL6_j9Cv79-uz36WF1c_zs--XpRWVNVUUmmIxbwloiWgKmyJcowBBicwq4h1qmFGYEeEo1wRx4G6BmqgVnImCLAjdL7qtsFs9Rh9b-KtDsbru0CIG23i5G0H2mYF2WDcWGm5bF3T4lxDU7umyTGOs9aXVWucmx5aC3kCpnsm-jwz-N96E260UJwxSbPA53uBGP7OkCbd-7SMwwwQ5qRp7gkLqejy16cX0G2Y45BHtaBkVbEa84z6-LSih1L-ry0D6ApYFpAiuAcIwXqxh17tobM99J099C6T6hck6yez7DZ35bvXqWylpnExAcTHsl9h_QNksc__ |
| CitedBy_id | crossref_primary_10_1002_mbo3_1158 crossref_primary_10_3390_s23177579 crossref_primary_10_1111_nph_16137 crossref_primary_10_1109_TIP_2019_2947349 crossref_primary_10_1111_jmi_13037 crossref_primary_10_1109_TCI_2021_3059497 crossref_primary_10_1016_j_measurement_2023_112964 crossref_primary_10_1111_jfpp_16471 crossref_primary_10_1111_nph_19195 crossref_primary_10_1038_s41598_022_21822_z crossref_primary_10_1038_s41598_024_57123_w crossref_primary_10_1109_JLT_2025_3571337 crossref_primary_10_1111_1365_2664_14320 crossref_primary_10_1111_ppl_13524 crossref_primary_10_1002_ajb2_16382 crossref_primary_10_1002_jbio_202000227 crossref_primary_10_1038_s41598_021_81098_7 crossref_primary_10_1111_cote_12548 crossref_primary_10_1002_syst_202200011 crossref_primary_10_1111_acel_13213 crossref_primary_10_1111_jvp_12961 crossref_primary_10_1109_LRA_2021_3061333 |
| Cites_doi | 10.1093/bioinformatics/btw614 10.1186/s13059-017-1218-y 10.1038/nature14539 10.1038/nbt.2967 10.1098/rspb.1980.0020 10.1177/24.1.1254907 10.1016/j.cell.2010.04.033 10.1103/PhysRevLett.106.248102 10.1038/nature04588 10.1364/AO.50.004967 10.1016/j.bpj.2014.08.028 10.1146/annurev-micro-091213-112852 10.1364/BOE.8.001731 10.1016/j.proeng.2013.09.086 10.1038/srep34038 10.1017/S1431927615015652 10.1016/j.gde.2010.08.005 10.1016/j.tig.2017.06.005 10.1038/s41598-017-07599-6 10.1038/nrm1979 10.1016/j.molcel.2016.05.023 10.1103/PhysRevE.92.062717 10.1117/1.JBO.22.6.060503 10.1103/PhysRevLett.114.078101 10.1371/journal.pcbi.1005177 10.1080/00018732.2015.1037068 10.1101/170555 10.1109/ICCV.2015.178 10.1038/protex.2017.095 10.1038/onc.2017.341 10.1364/DH.2017.W2A.5 |
| ContentType | Journal Article |
| Copyright | The Author(s) 2018 2018. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
| Copyright_xml | – notice: The Author(s) 2018 – notice: 2018. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
| DBID | C6C AAYXX CITATION NPM 3V. 7X7 7XB 88A 88E 88I 8FE 8FH 8FI 8FJ 8FK ABUWG AEUYN AFKRA AZQEC BBNVY BENPR BHPHI CCPQU DWQXO FYUFA GHDGH GNUQQ HCIFZ K9. LK8 M0S M1P M2P M7P PHGZM PHGZT PIMPY PJZUB PKEHL PPXIY PQEST PQGLB PQQKQ PQUKI PRINS Q9U 7X8 5PM DOA |
| DOI | 10.1038/s41598-018-25458-w |
| DatabaseName | Springer Nature OA Free Journals CrossRef PubMed ProQuest Central (Corporate) Health & Medical Collection (ProQuest) ProQuest Central (purchase pre-March 2016) Biology Database (Alumni Edition) Medical Database (Alumni Edition) Science Database (Alumni Edition) ProQuest SciTech Collection ProQuest Natural Science Journals Hospital Premium Collection Hospital Premium Collection (Alumni Edition) ProQuest Central (Alumni) (purchase pre-March 2016) ProQuest Central (Alumni) ProQuest One Sustainability ProQuest Central UK/Ireland ProQuest Central Essentials Biological Science Collection ProQuest Central Database Suite (ProQuest) Natural Science Collection ProQuest One ProQuest Central Korea Health Research Premium Collection Health Research Premium Collection (Alumni) ProQuest Central Student SciTech Premium Collection ProQuest Health & Medical Complete (Alumni) Biological Sciences ProQuest Health & Medical Collection Medical Database Science Database Biological Science Database ProQuest Central Premium ProQuest One Academic (New) Publicly Available Content Database ProQuest Health & Medical Research Collection ProQuest One Academic Middle East (New) ProQuest One Health & Nursing ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central China ProQuest Central Basic MEDLINE - Academic PubMed Central (Full Participant titles) DOAJ Directory of Open Access Journals |
| DatabaseTitle | CrossRef PubMed Publicly Available Content Database ProQuest Central Student ProQuest One Academic Middle East (New) ProQuest Central Essentials ProQuest Health & Medical Complete (Alumni) ProQuest Central (Alumni Edition) SciTech Premium Collection ProQuest One Community College ProQuest One Health & Nursing ProQuest Natural Science Collection ProQuest Central China ProQuest Biology Journals (Alumni Edition) ProQuest Central ProQuest One Applied & Life Sciences ProQuest One Sustainability ProQuest Health & Medical Research Collection Health Research Premium Collection Health and Medicine Complete (Alumni Edition) Natural Science Collection ProQuest Central Korea Health & Medical Research Collection Biological Science Collection ProQuest Central (New) ProQuest Medical Library (Alumni) ProQuest Science Journals (Alumni Edition) ProQuest Biological Science Collection ProQuest Central Basic ProQuest Science Journals ProQuest One Academic Eastern Edition ProQuest Hospital Collection Health Research Premium Collection (Alumni) Biological Science Database ProQuest SciTech Collection ProQuest Hospital Collection (Alumni) ProQuest Health & Medical Complete ProQuest Medical Library ProQuest One Academic UKI Edition ProQuest One Academic ProQuest One Academic (New) ProQuest Central (Alumni) MEDLINE - Academic |
| DatabaseTitleList | MEDLINE - Academic Publicly Available Content Database PubMed CrossRef |
| Database_xml | – sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 3 dbid: PIMPY name: Publicly Available Content Database url: http://search.proquest.com/publiccontent sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Biology |
| EISSN | 2045-2322 |
| EndPage | 10 |
| ExternalDocumentID | oai_doaj_org_article_c2496b00bc6c46dfbd0577b8fbb0bc40 PMC5943362 29743647 10_1038_s41598_018_25458_w |
| Genre | Journal Article |
| GroupedDBID | 0R~ 3V. 4.4 53G 5VS 7X7 88A 88E 88I 8FE 8FH 8FI 8FJ AAFWJ AAJSJ AAKDD ABDBF ABUWG ACGFS ACSMW ACUHS ADBBV ADRAZ AENEX AEUYN AFKRA AJTQC ALIPV ALMA_UNASSIGNED_HOLDINGS AOIJS AZQEC BAWUL BBNVY BCNDV BENPR BHPHI BPHCQ BVXVI C6C CCPQU DIK DWQXO EBD EBLON EBS EJD ESX FYUFA GNUQQ GROUPED_DOAJ GX1 HCIFZ HH5 HMCUK HYE IPNFZ KQ8 LK8 M0L M1P M2P M48 M7P M~E NAO OK1 PIMPY PQQKQ PROAC PSQYO RIG RNT RNTTT RPM SNYQT UKHRP AASML AAYXX AFFHD AFPKN CITATION PHGZM PHGZT PJZUB PPXIY PQGLB NPM 7XB 8FK K9. PKEHL PQEST PQUKI PRINS Q9U 7X8 5PM |
| ID | FETCH-LOGICAL-c577t-26a1c04d15d1e970c19f33e0ef50371cf9b3a50f15f2491f4e2fbe8e2c64351e3 |
| IEDL.DBID | M2P |
| ISICitedReferencesCount | 18 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000431736000009&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 2045-2322 |
| IngestDate | Fri Oct 03 12:44:32 EDT 2025 Tue Nov 04 02:00:31 EST 2025 Sun Nov 09 12:58:42 EST 2025 Tue Oct 07 07:35:42 EDT 2025 Wed Feb 19 02:44:35 EST 2025 Sat Nov 29 04:07:53 EST 2025 Tue Nov 18 21:55:45 EST 2025 Fri Feb 21 02:38:50 EST 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 1 |
| Language | English |
| License | Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c577t-26a1c04d15d1e970c19f33e0ef50371cf9b3a50f15f2491f4e2fbe8e2c64351e3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
| OpenAccessLink | https://www.proquest.com/docview/2036773804?pq-origsite=%requestingapplication% |
| PMID | 29743647 |
| PQID | 2036773804 |
| PQPubID | 2041939 |
| PageCount | 10 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_c2496b00bc6c46dfbd0577b8fbb0bc40 pubmedcentral_primary_oai_pubmedcentral_nih_gov_5943362 proquest_miscellaneous_2037056920 proquest_journals_2036773804 pubmed_primary_29743647 crossref_primary_10_1038_s41598_018_25458_w crossref_citationtrail_10_1038_s41598_018_25458_w springer_journals_10_1038_s41598_018_25458_w |
| PublicationCentury | 2000 |
| PublicationDate | 2018-05-09 |
| PublicationDateYYYYMMDD | 2018-05-09 |
| PublicationDate_xml | – month: 05 year: 2018 text: 2018-05-09 day: 09 |
| PublicationDecade | 2010 |
| PublicationPlace | London |
| PublicationPlace_xml | – name: London – name: England |
| PublicationTitle | Scientific reports |
| PublicationTitleAbbrev | Sci Rep |
| PublicationTitleAlternate | Sci Rep |
| PublicationYear | 2018 |
| Publisher | Nature Publishing Group UK Nature Publishing Group Nature Portfolio |
| Publisher_xml | – name: Nature Publishing Group UK – name: Nature Publishing Group – name: Nature Portfolio |
| References | Schenk (CR18) 2016; 6 Lee, Covert (CR1) 2010; 20 Norman, Lord, Paulsson, Losick (CR11) 2015; 69 Altschuler, Wu (CR4) 2010; 141 Pollen (CR8) 2014; 32 Podlech (CR24) 2016; 22 Suel, Garcia-Ojalvo, Liberman, Elowitz (CR7) 2006; 440 CR19 Brenner (CR36) 1976; 24 CR39 Symmons, Raj (CR5) 2016; 62 CR35 CR12 Castillo-Secilla (CR20) 2017; 8 CR34 Yuan (CR6) 2017; 18 CR33 CR31 CR30 Abadi (CR41) 2016; 16 Van Valen (CR27) 2016; 12 Fuller, Kellner, Price (CR21) 2011; 50 Klein, Sharma, Bohrer, Avelis, Roberts (CR17) 2017; 33 Krizhevsky, Sutskever, Hinton (CR32) 2012; 25 De, Masilamani (CR37) 2013; 64 Marr, Hildreth (CR38) 1980; 207 Assaf, Roberts, Luthey-Schulten (CR10) 2011; 106 Sadanandan, Ranefall, Le Guyader, Wahlby (CR40) 2017; 7 Godin, Lounis, Cognet (CR2) 2014; 107 Liu, Lavis, Betzig (CR3) 2015; 58 Halicek (CR29) 2017; 22 Pepperkok, Ellenberg (CR15) 2006; 7 Roberts, Beer, Bohrer, Sharma, Assaf (CR14) 2015; 92 Geusebroek, Cornelissen, Smeulders, Geerts (CR25) 2000; 39 CR28 CR9 CR23 CR22 Pegoraro, Misteli (CR16) 2017; 33 LeCun, Bengio, Hinton (CR26) 2015; 521 Ge, Qian, Xie (CR13) 2015; 114 FW Schenk (25458_CR18) 2016; 6 SJ Altschuler (25458_CR4) 2010; 141 GC Yuan (25458_CR6) 2017; 18 Y LeCun (25458_CR26) 2015; 521 SK Sadanandan (25458_CR40) 2017; 7 K De (25458_CR37) 2013; 64 H Ge (25458_CR13) 2015; 114 DN Fuller (25458_CR21) 2011; 50 25458_CR22 M Assaf (25458_CR10) 2011; 106 25458_CR23 JM Castillo-Secilla (25458_CR20) 2017; 8 AG Godin (25458_CR2) 2014; 107 E Roberts (25458_CR14) 2015; 92 25458_CR39 M Halicek (25458_CR29) 2017; 22 25458_CR19 S Podlech (25458_CR24) 2016; 22 Z Liu (25458_CR3) 2015; 58 G Pegoraro (25458_CR16) 2017; 33 AA Pollen (25458_CR8) 2014; 32 25458_CR30 A Krizhevsky (25458_CR32) 2012; 25 M Klein (25458_CR17) 2017; 33 25458_CR35 DA Van Valen (25458_CR27) 2016; 12 TK Lee (25458_CR1) 2010; 20 JM Geusebroek (25458_CR25) 2000; 39 25458_CR31 O Symmons (25458_CR5) 2016; 62 25458_CR9 25458_CR33 D Marr (25458_CR38) 1980; 207 GM Suel (25458_CR7) 2006; 440 25458_CR12 25458_CR34 M Abadi (25458_CR41) 2016; 16 R Pepperkok (25458_CR15) 2006; 7 25458_CR28 TM Norman (25458_CR11) 2015; 69 JF Brenner (25458_CR36) 1976; 24 |
| References_xml | – volume: 33 start-page: 3035 year: 2017 ident: CR17 article-title: Biospark: scalable analysis of large numerical datasets from biological simulations and experiments using Hadoop and Spark publication-title: Bioinformatics doi: 10.1093/bioinformatics/btw614 – ident: CR22 – volume: 18 year: 2017 ident: CR6 article-title: Challenges and emerging directions in single-cell analysis publication-title: Genome Biol. doi: 10.1186/s13059-017-1218-y – volume: 58 start-page: 64459 year: 2015 ident: CR3 article-title: Imaging live-cell dynamics and structure at the single-molecule level publication-title: Mol. Cell – volume: 521 start-page: 436444 year: 2015 ident: CR26 article-title: Deep learning publication-title: Nature doi: 10.1038/nature14539 – volume: 32 start-page: 10538 year: 2014 ident: CR8 article-title: Low-coverage single-cell mRNA sequencing reveals cellular heterogeneity and activated signaling pathways in developing cerebral cortex publication-title: Nat. Biotechnol. doi: 10.1038/nbt.2967 – ident: CR39 – volume: 207 start-page: 187 year: 1980 end-page: 217 ident: CR38 article-title: Theory of edge detection publication-title: Proc. R. Soc. Lond. B doi: 10.1098/rspb.1980.0020 – ident: CR12 – ident: CR30 – ident: CR33 – volume: 24 start-page: 100 year: 1976 end-page: 111 ident: CR36 article-title: An automated microscope for cytologic research a preliminary evaluation publication-title: Journal of Histochemistry & Cytochemistry doi: 10.1177/24.1.1254907 – ident: CR35 – volume: 141 start-page: 55963 year: 2010 ident: CR4 article-title: Cellular heterogeneity: do differences make a difference? publication-title: Cell doi: 10.1016/j.cell.2010.04.033 – volume: 106 start-page: 248102 year: 2011 ident: CR10 article-title: Determining the stability of genetic switches: explicitly accounting for mRNA noise publication-title: Phys. Rev. Lett. doi: 10.1103/PhysRevLett.106.248102 – volume: 440 start-page: 54550 year: 2006 ident: CR7 article-title: An excitable gene regulatory circuit induces transient cellular differentiation publication-title: Nature doi: 10.1038/nature04588 – volume: 50 start-page: 49674976 year: 2011 ident: CR21 article-title: Exploiting chromatic aberration for image-based microscope autofocus publication-title: Applied Optics doi: 10.1364/AO.50.004967 – ident: CR23 – volume: 107 start-page: 177784 year: 2014 ident: CR2 article-title: Super-resolution microscopy approaches for live cell imaging publication-title: Biophys. J. doi: 10.1016/j.bpj.2014.08.028 – volume: 69 start-page: 381403 year: 2015 ident: CR11 article-title: Stochastic switching of cell fate in microbes publication-title: Annu. Rev. Microbiol. doi: 10.1146/annurev-micro-091213-112852 – ident: CR19 – volume: 8 start-page: 17311740 year: 2017 ident: CR20 article-title: Autofocus method for automated microscopy using embedded gpus publication-title: Biomedical Optics Express doi: 10.1364/BOE.8.001731 – volume: 64 start-page: 149 year: 2013 end-page: 158 ident: CR37 article-title: Image sharpness measure for blurred images in frequency domain publication-title: Procedia Engineering doi: 10.1016/j.proeng.2013.09.086 – volume: 6 year: 2016 ident: CR18 article-title: High-speed microscopy of continuously moving cell culture vessels publication-title: Scientific reports doi: 10.1038/srep34038 – volume: 22 start-page: 199207 year: 2016 ident: CR24 article-title: Autofocus by bayes spectral entropy applied to optical microscopy publication-title: Microscopy and Microanalysis doi: 10.1017/S1431927615015652 – volume: 20 start-page: 67783 year: 2010 ident: CR1 article-title: High-throughput, single-cell NF- B dynamics publication-title: Curr. Opin. Genet. Dev. doi: 10.1016/j.gde.2010.08.005 – volume: 33 start-page: 60415 year: 2017 ident: CR16 article-title: High-throughput imaging for the discovery of cellular mechanisms of disease publication-title: Trends Genet doi: 10.1016/j.tig.2017.06.005 – volume: 7 year: 2017 ident: CR40 article-title: Automated training of deep convolutional neural networks for cell segmentation publication-title: Sci Rep doi: 10.1038/s41598-017-07599-6 – volume: 7 start-page: 690696 year: 2006 ident: CR15 article-title: High-throughput fluorescence microscopy for systems biology publication-title: Nature reviews Molecular cell biology doi: 10.1038/nrm1979 – volume: 39 start-page: 19 year: 2000 ident: CR25 article-title: Robust autofocusing in microscopy publication-title: Cytometry Part A – ident: CR31 – volume: 62 start-page: 788802 year: 2016 ident: CR5 article-title: Whats luck got to do with it: Single cells, multiple fates, and biological nondeterminism publication-title: Mol. Cell doi: 10.1016/j.molcel.2016.05.023 – ident: CR9 – volume: 92 start-page: 062717 year: 2015 ident: CR14 article-title: Dynamics of simple genenetwork motifs subject to extrinsic fluctuations publication-title: Phys. Rev. E doi: 10.1103/PhysRevE.92.062717 – volume: 22 start-page: 060503 year: 2017 ident: CR29 article-title: Deep convolutional neural networks for classifying head and neck cancer using hyperspectral imaging publication-title: Journal of Biomedical Optics doi: 10.1117/1.JBO.22.6.060503 – ident: CR34 – volume: 16 start-page: 265283 year: 2016 ident: CR41 article-title: Tensorflow: A system for large-scale machine learning publication-title: OSDI – volume: 114 start-page: 078101 year: 2015 ident: CR13 article-title: Stochastic phenotype transition of a single cell in an Intermediate region of gene state switching publication-title: Phys. Rev. Lett. doi: 10.1103/PhysRevLett.114.078101 – volume: 25 start-page: 1097105 year: 2012 ident: CR32 article-title: ImageNet classification with deep convolutional neural networks publication-title: Advances in Neural Information Processing Systems – volume: 12 start-page: e1005177 year: 2016 ident: CR27 article-title: Deep learning automates the quantitative analysis of individual cells in live-cell imaging experiments publication-title: PLoS Comput. Biol. doi: 10.1371/journal.pcbi.1005177 – ident: CR28 – volume: 8 start-page: 17311740 year: 2017 ident: 25458_CR20 publication-title: Biomedical Optics Express doi: 10.1364/BOE.8.001731 – volume: 50 start-page: 49674976 year: 2011 ident: 25458_CR21 publication-title: Applied Optics doi: 10.1364/AO.50.004967 – volume: 58 start-page: 64459 year: 2015 ident: 25458_CR3 publication-title: Mol. Cell – ident: 25458_CR23 – volume: 12 start-page: e1005177 year: 2016 ident: 25458_CR27 publication-title: PLoS Comput. Biol. doi: 10.1371/journal.pcbi.1005177 – volume: 107 start-page: 177784 year: 2014 ident: 25458_CR2 publication-title: Biophys. J. doi: 10.1016/j.bpj.2014.08.028 – volume: 114 start-page: 078101 year: 2015 ident: 25458_CR13 publication-title: Phys. Rev. Lett. doi: 10.1103/PhysRevLett.114.078101 – volume: 92 start-page: 062717 year: 2015 ident: 25458_CR14 publication-title: Phys. Rev. E doi: 10.1103/PhysRevE.92.062717 – ident: 25458_CR12 doi: 10.1080/00018732.2015.1037068 – ident: 25458_CR34 – volume: 24 start-page: 100 year: 1976 ident: 25458_CR36 publication-title: Journal of Histochemistry & Cytochemistry doi: 10.1177/24.1.1254907 – ident: 25458_CR19 doi: 10.1101/170555 – volume: 16 start-page: 265283 year: 2016 ident: 25458_CR41 publication-title: OSDI – volume: 521 start-page: 436444 year: 2015 ident: 25458_CR26 publication-title: Nature doi: 10.1038/nature14539 – ident: 25458_CR30 – volume: 69 start-page: 381403 year: 2015 ident: 25458_CR11 publication-title: Annu. Rev. Microbiol. doi: 10.1146/annurev-micro-091213-112852 – volume: 18 year: 2017 ident: 25458_CR6 publication-title: Genome Biol. doi: 10.1186/s13059-017-1218-y – volume: 207 start-page: 187 year: 1980 ident: 25458_CR38 publication-title: Proc. R. Soc. Lond. B doi: 10.1098/rspb.1980.0020 – volume: 7 start-page: 690696 year: 2006 ident: 25458_CR15 publication-title: Nature reviews Molecular cell biology doi: 10.1038/nrm1979 – ident: 25458_CR22 – volume: 20 start-page: 67783 year: 2010 ident: 25458_CR1 publication-title: Curr. Opin. Genet. Dev. doi: 10.1016/j.gde.2010.08.005 – volume: 440 start-page: 54550 year: 2006 ident: 25458_CR7 publication-title: Nature doi: 10.1038/nature04588 – volume: 62 start-page: 788802 year: 2016 ident: 25458_CR5 publication-title: Mol. Cell doi: 10.1016/j.molcel.2016.05.023 – ident: 25458_CR39 doi: 10.1109/ICCV.2015.178 – ident: 25458_CR28 doi: 10.1038/protex.2017.095 – volume: 6 year: 2016 ident: 25458_CR18 publication-title: Scientific reports doi: 10.1038/srep34038 – volume: 22 start-page: 060503 year: 2017 ident: 25458_CR29 publication-title: Journal of Biomedical Optics doi: 10.1117/1.JBO.22.6.060503 – volume: 33 start-page: 3035 year: 2017 ident: 25458_CR17 publication-title: Bioinformatics doi: 10.1093/bioinformatics/btw614 – volume: 141 start-page: 55963 year: 2010 ident: 25458_CR4 publication-title: Cell doi: 10.1016/j.cell.2010.04.033 – volume: 39 start-page: 19 year: 2000 ident: 25458_CR25 publication-title: Cytometry Part A – volume: 32 start-page: 10538 year: 2014 ident: 25458_CR8 publication-title: Nat. Biotechnol. doi: 10.1038/nbt.2967 – volume: 64 start-page: 149 year: 2013 ident: 25458_CR37 publication-title: Procedia Engineering doi: 10.1016/j.proeng.2013.09.086 – volume: 7 year: 2017 ident: 25458_CR40 publication-title: Sci Rep doi: 10.1038/s41598-017-07599-6 – ident: 25458_CR9 doi: 10.1038/onc.2017.341 – ident: 25458_CR33 – ident: 25458_CR31 doi: 10.1364/DH.2017.W2A.5 – ident: 25458_CR35 – volume: 106 start-page: 248102 year: 2011 ident: 25458_CR10 publication-title: Phys. Rev. Lett. doi: 10.1103/PhysRevLett.106.248102 – volume: 33 start-page: 60415 year: 2017 ident: 25458_CR16 publication-title: Trends Genet doi: 10.1016/j.tig.2017.06.005 – volume: 25 start-page: 1097105 year: 2012 ident: 25458_CR32 publication-title: Advances in Neural Information Processing Systems – volume: 22 start-page: 199207 year: 2016 ident: 25458_CR24 publication-title: Microscopy and Microanalysis doi: 10.1017/S1431927615015652 |
| SSID | ssj0000529419 |
| Score | 2.3977144 |
| Snippet | Live-cell microscopy is quickly becoming an indispensable technique for studying the dynamics of cellular processes. Maintaining the specimen in focus during... Abstract Live-cell microscopy is quickly becoming an indispensable technique for studying the dynamics of cellular processes. Maintaining the specimen in focus... |
| SourceID | doaj pubmedcentral proquest pubmed crossref springer |
| SourceType | Open Website Open Access Repository Aggregation Database Index Database Enrichment Source Publisher |
| StartPage | 7313 |
| SubjectTerms | 14/63 631/1647/245/2186 631/1647/794 Humanities and Social Sciences Microfluidics Microscopy multidisciplinary Neural networks Science Science (multidisciplinary) Yeast |
| SummonAdditionalLinks | – databaseName: DOAJ Directory of Open Access Journals dbid: DOA link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1Nb9QwELWqikpcENACoQW5EjeIasdOHB8BUXFAKw4F9WbFzlhUKtmq2bbqv--Mnd12y9eFq-1dOc_jzExm5g1jb1AJge2FKT2--0sNQPHdaMsojG9CU4UukVV__2Jms_b42H690-qLcsIyPXAG7iCgf9CgbPjQBN300fdoYRjfRu9xTCdvXRh7x5nKrN6V1dJOVTJCtQcjaiqqJpMoGBQsKq_WNFEi7P-dlflrsuS9iGlSRIeP2aPJguTv886fsA0YnrKt3FPyepvNiG4D54ec382nXHQ-jzyS3uLLNC2eKxQ5dZcvT7uzEfhPys6jOpVrWk7f9Mcd9u3w09HHz-XUNKEMiMuirJpOBqF7WfcSrBFB2qgUCIg1sfOFaL3qahFlHRFZGTVU0UMLVUDbpJagnrHNYT7AC8ZxUEf0PyLqUd2Lxre9tnh6fQcKAHTB5BJAFyZGcWpscepSZFu1LoPuEHSXQHdXBXu7-s1Z5tP46-oPdC6rlcSFnQZQQtwkIe5fElKwveWpuumCjo7ir8aoVuBT7K-m8WoRtt0A84u0xqB9aCv8i-dZCFY7qdAPI-r9gpk18Vjb6vrMcPIj0XfXVis0Gwr2bilIt9v6MxQv_wcUu-xhRTeAEjbtHttcnF_AK_YgXC5OxvPX6QrdAOGxISs priority: 102 providerName: Directory of Open Access Journals |
| Title | Neural network control of focal position during time-lapse microscopy of cells |
| URI | https://link.springer.com/article/10.1038/s41598-018-25458-w https://www.ncbi.nlm.nih.gov/pubmed/29743647 https://www.proquest.com/docview/2036773804 https://www.proquest.com/docview/2037056920 https://pubmed.ncbi.nlm.nih.gov/PMC5943362 https://doaj.org/article/c2496b00bc6c46dfbd0577b8fbb0bc40 |
| Volume | 8 |
| WOSCitedRecordID | wos000431736000009&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 2045-2322 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000529419 issn: 2045-2322 databaseCode: DOA dateStart: 20110101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 2045-2322 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000529419 issn: 2045-2322 databaseCode: M~E dateStart: 20110101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVPQU databaseName: Biological Science Database customDbUrl: eissn: 2045-2322 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000529419 issn: 2045-2322 databaseCode: M7P dateStart: 20110101 isFulltext: true titleUrlDefault: http://search.proquest.com/biologicalscijournals providerName: ProQuest – providerCode: PRVPQU databaseName: Health & Medical Collection customDbUrl: eissn: 2045-2322 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000529419 issn: 2045-2322 databaseCode: 7X7 dateStart: 20110101 isFulltext: true titleUrlDefault: https://search.proquest.com/healthcomplete providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 2045-2322 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000529419 issn: 2045-2322 databaseCode: BENPR dateStart: 20110101 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: Publicly Available Content Database customDbUrl: eissn: 2045-2322 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000529419 issn: 2045-2322 databaseCode: PIMPY dateStart: 20110101 isFulltext: true titleUrlDefault: http://search.proquest.com/publiccontent providerName: ProQuest – providerCode: PRVPQU databaseName: Science Database customDbUrl: eissn: 2045-2322 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000529419 issn: 2045-2322 databaseCode: M2P dateStart: 20110101 isFulltext: true titleUrlDefault: https://search.proquest.com/sciencejournals providerName: ProQuest |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1Lb9QwEB7RLki98H4EyipI3CBqnDhxfEIUtQKJriIEaDlFsWOXSiXZbrZU_ffMON5Uy6MXLj7YTmR7Zjxjz_gbgJeohIxsYhEp3Psjbgz5d62MbCxUrvNE1w6s-utHMZsV87ks_YVb78Mq13ui26ibTtMd-R45zIRIi5i_WZxFlDWKvKs-hcYWTNCyYRTSdZSU4x0LebE4k_6tTJwWez3qK3pTxpA9yGUUXWzoIwfb_zdb88-Qyd_8pk4dHd7534nchdveEA3fDpxzD26Y9j7cGlJTXj6AGaF2YHs7hImHPqQ97GxoSf2F62ivcHjoGFKS-ui0XvQm_EFBfvTc5ZK6k2ugfwhfDg8-v3sf-dwLkc6EWEVJXjMd84ZlDTNSxJpJm6YmNjYjkD9tpUrrLLYss3iAY5abxCpTmESjiZMxkz6C7bZrzRMIsZJbPMZYVMe8iXNVNFwiEzS1SY0xPAC2pkClPTA55cc4rZyDPC2qgWoVUq1yVKsuAng1frMYYDmu7b1PhB17EqS2q-iWx5WX0ErjPHLchJTONc8bqxo0ZYUqrFJYx-MAdtf0rLyc99UVMQN4MTajhNLa1q3pzl0fgWamTPAXjwcuGkeS4HGOEPwDEBv8tTHUzZb25LtDAc8kT9H6COD1mhOvhvXvpXh6_SyewU5CwkERnXIXtlfLc_Mcbuqfq5N-OYUtMReuLKYw2T-YlZ-m7hJj6uSOSoHlpPxwVH77BYgPNR0 |
| linkProvider | ProQuest |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9QwEB6VAoILb0qgQJDgBFHzcOL4gBCvqlWXVQ8F9WYSZ0xXapNls2W1f4rfyExe1fLorQeuthPZzueZceabGYDnpIRQFb70cpL9nkBk_65VnvVlnpgkNFmTrPrLSI7H6eGh2l-Dn30sDNMqe5nYCOqiMvyPfIsdZlJGqS_eTL97XDWKvat9CY0WFnu4XNCVrX69-4G-74sw3P548H7H66oKeCaWcu6FSRYYXxRBXASopG8CZaMIfbQxp68zVuVRFvs2iC1dTQIrMLQ5phgaUt5xgBG99xJcFpxZjKmC4f7wT4e9ZiJQXWyOH6VbNelHjmELCI7sovIWK_qvKRPwN9v2T4rmb37aRv1t3_zfNu4W3OgMbfdtezJuwxqWd-BqW3pzeRfGnJWE-suWBu92lH23sq5l9e72bDa3DeR055MT9I6zaY3uCZMYOZxnycPZ9VHfg88Xspj7sF5WJT4AlxqFpWuaJXNDFH6Sp4VQBPIiwwgRhQNB_8W16RKvc_2PY90QAKJUtyjRhBLdoEQvHHg5PDNt046cO_odA2kYySnDm4Zq9k13EkgbWkdCQjY3iRFJYfOCTHWZpzbPqU34Dmz2-NGdHKv1GXgceDZ0kwTivc1KrE6bMZLMaBXSKzZa1A4zCem6yhUKHJAreF6Z6mpPOTlqspzHSkRkXTnwqkf-2bT-vRUPz1_FU7i2c_BppEe7471HcD3kg8nsVbUJ6_PZKT6GK-bHfFLPnjQn24WvF30ifgF4X4qX |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Jb9NAFH4qKSAu7IuhgJHgBFa8jD2eA0JAiYhaohwAtafBHr-BSK0T4pQof41fx3teUoWltx64zoytWb63zLwN4CkJIVSFL72ceL8nENm-a5VnfZknJglNVier_rwvR6P04ECNt-BnFwvDbpUdT6wZdTE1_EbeZ4OZlFHqi75t3SLGu4NXs-8eV5BiS2tXTqOByB6ulnR9q14Od-msn4Xh4N3Ht--9tsKAZ2IpF16YZIHxRRHERYBK-iZQNorQRxtzKjtjVR5lsW-D2NI1JbACQ5tjiqEhQR4HGNF_L8A2qeQi7MH2ePhhfLh-4WEbmghUG6njR2m_ImnJEW0BgZMNVt5yQxrWRQP-pun-6bD5m9W2FoaDa__zNl6Hq60K7r5uaOYGbGF5Ey41RTlXt2DE-Uqov2wc5N3Wmd-dWtey4Hc7Pze3CfF0F5Nj9I6yWYXuMbs3cqDPioezUaS6DZ_OZTF3oFdOS7wHLjUKSxc4S4qIKPwkTwuhCP5FhhEiCgeC7vS1aVOyc2WQI127BkSpbhCjCTG6RoxeOvB8_c2sSUhy5ug3DKr1SE4mXjdM5191y5u0oXUkxH5zkxiRFDYvSImXeWrznNqE78BOhyXdcrhKnwLJgSfrbuJNvLdZidOTeowkBVuF9Iu7DYLXMwnpIsu1CxyQG9jemOpmTzn5Vuc_j5WISO9y4EVHBafT-vdW3D97FY_hMhGC3h-O9h7AlZBplN1a1Q70FvMTfAgXzY_FpJo_asnchS_nTRK_AFQblOA |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Neural+network+control+of+focal+position+during+time-lapse+microscopy+of+cells&rft.jtitle=Scientific+reports&rft.au=Wei%2C+Ling&rft.au=Roberts%2C+Elijah&rft.date=2018-05-09&rft.issn=2045-2322&rft.eissn=2045-2322&rft.volume=8&rft.issue=1&rft.spage=7313&rft_id=info:doi/10.1038%2Fs41598-018-25458-w&rft.externalDBID=NO_FULL_TEXT |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2045-2322&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2045-2322&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2045-2322&client=summon |