BAGLS, a multihospital Benchmark for Automatic Glottis Segmentation

Laryngeal videoendoscopy is one of the main tools in clinical examinations for voice disorders and voice research. Using high-speed videoendoscopy, it is possible to fully capture the vocal fold oscillations, however, processing the recordings typically involves a time-consuming segmentation of the...

Full description

Saved in:

Bibliographic Details
Published in	Scientific data Vol. 7; no. 1; p. 186
Main Authors	Gómez, Pablo, Kist, Andreas M., Schlegel, Patrick, Berry, David A., Chhetri, Dinesh K., Dürr, Stephan, Echternach, Matthias, Johnson, Aaron M., Kniesburges, Stefan, Kunduk, Melda, Maryn, Youri, Schützenberger, Anne, Verguts, Monique, Döllinger, Michael
Format	Journal Article
Language	English
Published	London Nature Publishing Group UK 19.06.2020 Nature Publishing Group
Subjects	692/308 692/698 Data Descriptor Deep learning Endoscopy Glottis - diagnostic imaging Glottis - physiology Humanities and Social Sciences Humans Image processing multidisciplinary Neural networks Oscillations Researchers Science Science (multidisciplinary) Segmentation Vocal Cords - diagnostic imaging Vocal Cords - physiology Voice Disorders - diagnosis
Online Access	Get full text
ISSN	2052-4463 2052-4463
DOI	10.1038/s41597-020-0526-3

Cover

Abstract	Laryngeal videoendoscopy is one of the main tools in clinical examinations for voice disorders and voice research. Using high-speed videoendoscopy, it is possible to fully capture the vocal fold oscillations, however, processing the recordings typically involves a time-consuming segmentation of the glottal area by trained experts. Even though automatic methods have been proposed and the task is particularly suited for deep learning methods, there are no public datasets and benchmarks available to compare methods and to allow training of generalizing deep learning models. In an international collaboration of researchers from seven institutions from the EU and USA, we have created BAGLS, a large, multihospital dataset of 59,250 high-speed videoendoscopy frames with individually annotated segmentation masks. The frames are based on 640 recordings of healthy and disordered subjects that were recorded with varying technical equipment by numerous clinicians. The BAGLS dataset will allow an objective comparison of glottis segmentation methods and will enable interested researchers to train their own models and compare their methods. Measurement(s) glottis • Image Segmentation Technology Type(s) Endoscopic Procedure • neural network model Factor Type(s) age • sex • healthy versus disordered subjects • recording conditions Sample Characteristic - Organism Homo sapiens Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.12387890
AbstractList	Laryngeal videoendoscopy is one of the main tools in clinical examinations for voice disorders and voice research. Using high-speed videoendoscopy, it is possible to fully capture the vocal fold oscillations, however, processing the recordings typically involves a time-consuming segmentation of the glottal area by trained experts. Even though automatic methods have been proposed and the task is particularly suited for deep learning methods, there are no public datasets and benchmarks available to compare methods and to allow training of generalizing deep learning models. In an international collaboration of researchers from seven institutions from the EU and USA, we have created BAGLS, a large, multihospital dataset of 59,250 high-speed videoendoscopy frames with individually annotated segmentation masks. The frames are based on 640 recordings of healthy and disordered subjects that were recorded with varying technical equipment by numerous clinicians. The BAGLS dataset will allow an objective comparison of glottis segmentation methods and will enable interested researchers to train their own models and compare their methods. Measurement(s) glottis • Image Segmentation Technology Type(s) Endoscopic Procedure • neural network model Factor Type(s) age • sex • healthy versus disordered subjects • recording conditions Sample Characteristic - Organism Homo sapiens Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.12387890 Laryngeal videoendoscopy is one of the main tools in clinical examinations for voice disorders and voice research. Using high-speed videoendoscopy, it is possible to fully capture the vocal fold oscillations, however, processing the recordings typically involves a time-consuming segmentation of the glottal area by trained experts. Even though automatic methods have been proposed and the task is particularly suited for deep learning methods, there are no public datasets and benchmarks available to compare methods and to allow training of generalizing deep learning models. In an international collaboration of researchers from seven institutions from the EU and USA, we have created BAGLS, a large, multihospital dataset of 59,250 high-speed videoendoscopy frames with individually annotated segmentation masks. The frames are based on 640 recordings of healthy and disordered subjects that were recorded with varying technical equipment by numerous clinicians. The BAGLS dataset will allow an objective comparison of glottis segmentation methods and will enable interested researchers to train their own models and compare their methods.Measurement(s)glottis • Image SegmentationTechnology Type(s)Endoscopic Procedure • neural network modelFactor Type(s)age • sex • healthy versus disordered subjects • recording conditionsSample Characteristic - OrganismHomo sapiensMachine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.12387890 Laryngeal videoendoscopy is one of the main tools in clinical examinations for voice disorders and voice research. Using high-speed videoendoscopy, it is possible to fully capture the vocal fold oscillations, however, processing the recordings typically involves a time-consuming segmentation of the glottal area by trained experts. Even though automatic methods have been proposed and the task is particularly suited for deep learning methods, there are no public datasets and benchmarks available to compare methods and to allow training of generalizing deep learning models. In an international collaboration of researchers from seven institutions from the EU and USA, we have created BAGLS, a large, multihospital dataset of 59,250 high-speed videoendoscopy frames with individually annotated segmentation masks. The frames are based on 640 recordings of healthy and disordered subjects that were recorded with varying technical equipment by numerous clinicians. The BAGLS dataset will allow an objective comparison of glottis segmentation methods and will enable interested researchers to train their own models and compare their methods.Laryngeal videoendoscopy is one of the main tools in clinical examinations for voice disorders and voice research. Using high-speed videoendoscopy, it is possible to fully capture the vocal fold oscillations, however, processing the recordings typically involves a time-consuming segmentation of the glottal area by trained experts. Even though automatic methods have been proposed and the task is particularly suited for deep learning methods, there are no public datasets and benchmarks available to compare methods and to allow training of generalizing deep learning models. In an international collaboration of researchers from seven institutions from the EU and USA, we have created BAGLS, a large, multihospital dataset of 59,250 high-speed videoendoscopy frames with individually annotated segmentation masks. The frames are based on 640 recordings of healthy and disordered subjects that were recorded with varying technical equipment by numerous clinicians. The BAGLS dataset will allow an objective comparison of glottis segmentation methods and will enable interested researchers to train their own models and compare their methods. Laryngeal videoendoscopy is one of the main tools in clinical examinations for voice disorders and voice research. Using high-speed videoendoscopy, it is possible to fully capture the vocal fold oscillations, however, processing the recordings typically involves a time-consuming segmentation of the glottal area by trained experts. Even though automatic methods have been proposed and the task is particularly suited for deep learning methods, there are no public datasets and benchmarks available to compare methods and to allow training of generalizing deep learning models. In an international collaboration of researchers from seven institutions from the EU and USA, we have created BAGLS, a large, multihospital dataset of 59,250 high-speed videoendoscopy frames with individually annotated segmentation masks. The frames are based on 640 recordings of healthy and disordered subjects that were recorded with varying technical equipment by numerous clinicians. The BAGLS dataset will allow an objective comparison of glottis segmentation methods and will enable interested researchers to train their own models and compare their methods. Measurement(s) glottis • Image Segmentation Technology Type(s) Endoscopic Procedure • neural network model Factor Type(s) age • sex • healthy versus disordered subjects • recording conditions Sample Characteristic - Organism Homo sapiens Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.12387890 Laryngeal videoendoscopy is one of the main tools in clinical examinations for voice disorders and voice research. Using high-speed videoendoscopy, it is possible to fully capture the vocal fold oscillations, however, processing the recordings typically involves a time-consuming segmentation of the glottal area by trained experts. Even though automatic methods have been proposed and the task is particularly suited for deep learning methods, there are no public datasets and benchmarks available to compare methods and to allow training of generalizing deep learning models. In an international collaboration of researchers from seven institutions from the EU and USA, we have created BAGLS, a large, multihospital dataset of 59,250 high-speed videoendoscopy frames with individually annotated segmentation masks. The frames are based on 640 recordings of healthy and disordered subjects that were recorded with varying technical equipment by numerous clinicians. The BAGLS dataset will allow an objective comparison of glottis segmentation methods and will enable interested researchers to train their own models and compare their methods.Measurement(s)glottis • Image SegmentationTechnology Type(s)Endoscopic Procedure • neural network modelFactor Type(s)age • sex • healthy versus disordered subjects • recording conditionsSample Characteristic - OrganismHomo sapiensMachine-accessible metadata file describing the reported data: 10.6084/m9.figshare.12387890 Laryngeal videoendoscopy is one of the main tools in clinical examinations for voice disorders and voice research. Using high-speed videoendoscopy, it is possible to fully capture the vocal fold oscillations, however, processing the recordings typically involves a time-consuming segmentation of the glottal area by trained experts. Even though automatic methods have been proposed and the task is particularly suited for deep learning methods, there are no public datasets and benchmarks available to compare methods and to allow training of generalizing deep learning models. In an international collaboration of researchers from seven institutions from the EU and USA, we have created BAGLS, a large, multihospital dataset of 59,250 high-speed videoendoscopy frames with individually annotated segmentation masks. The frames are based on 640 recordings of healthy and disordered subjects that were recorded with varying technical equipment by numerous clinicians. The BAGLS dataset will allow an objective comparison of glottis segmentation methods and will enable interested researchers to train their own models and compare their methods.
ArticleNumber	186
Author	Dürr, Stephan Kunduk, Melda Maryn, Youri Berry, David A. Schützenberger, Anne Döllinger, Michael Verguts, Monique Kist, Andreas M. Echternach, Matthias Johnson, Aaron M. Gómez, Pablo Schlegel, Patrick Chhetri, Dinesh K. Kniesburges, Stefan
Author_xml	– sequence: 1 givenname: Pablo orcidid: 0000-0002-5631-8240 surname: Gómez fullname: Gómez, Pablo email: pablo.gomez@tum.de organization: Division of Phoniatrics and Pediatric Audiology, Department of Otorhinolaryngology, Head and Neck Surgery, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nürnberg – sequence: 2 givenname: Andreas M. orcidid: 0000-0003-3643-7776 surname: Kist fullname: Kist, Andreas M. email: andreas.kist@uk-erlangen.de organization: Division of Phoniatrics and Pediatric Audiology, Department of Otorhinolaryngology, Head and Neck Surgery, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nürnberg – sequence: 3 givenname: Patrick surname: Schlegel fullname: Schlegel, Patrick organization: Division of Phoniatrics and Pediatric Audiology, Department of Otorhinolaryngology, Head and Neck Surgery, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nürnberg – sequence: 4 givenname: David A. surname: Berry fullname: Berry, David A. organization: Department of Head and Neck Surgery, David Geffen School of Medicine at the University of California, Los Angeles – sequence: 5 givenname: Dinesh K. surname: Chhetri fullname: Chhetri, Dinesh K. organization: Department of Head and Neck Surgery, David Geffen School of Medicine at the University of California, Los Angeles – sequence: 6 givenname: Stephan surname: Dürr fullname: Dürr, Stephan organization: Division of Phoniatrics and Pediatric Audiology, Department of Otorhinolaryngology, Head and Neck Surgery, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nürnberg – sequence: 7 givenname: Matthias orcidid: 0000-0003-0095-5360 surname: Echternach fullname: Echternach, Matthias organization: Division of Phoniatrics and Pediatric Audiology, Department of Otorhinolaryngology, Munich University Hospital (LMU) – sequence: 8 givenname: Aaron M. orcidid: 0000-0002-7577-0267 surname: Johnson fullname: Johnson, Aaron M. organization: NYU Voice Center, Department of Otolaryngology – Head and Neck Surgery, New York University School of Medicine – sequence: 9 givenname: Stefan surname: Kniesburges fullname: Kniesburges, Stefan organization: Division of Phoniatrics and Pediatric Audiology, Department of Otorhinolaryngology, Head and Neck Surgery, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nürnberg – sequence: 10 givenname: Melda surname: Kunduk fullname: Kunduk, Melda organization: Department of Communication Sciences and Disorders, Louisiana State University – sequence: 11 givenname: Youri surname: Maryn fullname: Maryn, Youri organization: European Institute for ORL-HNS, Department of Otorhinolaryngology and Head & Neck Surgery, Sint-Augustinus GZA, Department of Speech, Language and Hearing sciences, University of Ghent, Faculty of Education, Health and Social Work, University College Ghent, Faculty of Psychology and Educational Sciences, School of Logopedics, Université Catholique de Louvain, Faculty of Medicine and Health Sciences, University of Antwerp – sequence: 12 givenname: Anne surname: Schützenberger fullname: Schützenberger, Anne organization: Division of Phoniatrics and Pediatric Audiology, Department of Otorhinolaryngology, Head and Neck Surgery, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nürnberg – sequence: 13 givenname: Monique surname: Verguts fullname: Verguts, Monique organization: European Institute for ORL-HNS, Department of Otorhinolaryngology and Head & Neck Surgery, Sint-Augustinus GZA, Department of Otorhinolaryngology and Voice Disorders, Diest General Hospital – sequence: 14 givenname: Michael surname: Döllinger fullname: Döllinger, Michael organization: Division of Phoniatrics and Pediatric Audiology, Department of Otorhinolaryngology, Head and Neck Surgery, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nürnberg
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/32561845$$D View this record in MEDLINE/PubMed
BookMark	eNp9kU9v1DAQxS1UREvpB-CCInHhQGD8N_EFabuCBWklDoWz5TjeXZfEXmynEt8eRymlVKInj-zfm3nj9xyd-OAtQi8xvMNA2_eJYS6bGgjUwImo6RN0RkpVMyboyb36FF2kdA0AmDLgDTxDp5RwgVvGz9D6crXZXr2tdDVOQ3aHkI4u66G6tN4cRh1_VLsQq9WUw6izM9VmCDm7VF3Z_Wh9LnfBv0BPd3pI9uL2PEffP338tv5cb79uvqxX29ow2eaa9VJ0whLBmo4Z2RMgFDA3ErQUQttydMZ2lvSi7Gc61lvWQG_bZicZ7zE9Rx-WvsepG21vyvyoB3WMrhj9pYJ26t8X7w5qH25UQ4FjYKXBm9sGMfycbMpqdMnYYdDehikpUr60oITM6OsH6HWYoi_rFaqVElrW0McpzCRIzuZer-77vjP8J4UC4AUwMaQU7e4OwaDmsNUStiphqzlsNY9uHmiMW-Ioq7vhUSVZlKlM8Xsb_5r-v-g3qmq7fg
CitedBy_id	crossref_primary_10_4015_S1016237221500277 crossref_primary_10_1016_j_jvoice_2024_03_035 crossref_primary_10_1007_s00405_024_08659_0 crossref_primary_10_1109_JTEHM_2023_3237859 crossref_primary_10_1016_j_dib_2025_111376 crossref_primary_10_1016_j_jvoice_2021_08_011 crossref_primary_10_1016_j_jvoice_2025_01_001 crossref_primary_10_1016_j_medengphy_2025_104298 crossref_primary_10_1044_2022_JSLHR_22_00053 crossref_primary_10_1055_a_2089_5741 crossref_primary_10_1016_j_otc_2024_05_005 crossref_primary_10_1038_s41598_022_17764_1 crossref_primary_10_1016_j_bspc_2022_103878 crossref_primary_10_3390_bioengineering11050443 crossref_primary_10_1044_2021_JSLHR_20_00498 crossref_primary_10_1371_journal_pone_0266989 crossref_primary_10_1002_lary_30930 crossref_primary_10_1016_j_jvoice_2022_02_014 crossref_primary_10_1038_s41598_020_77216_6 crossref_primary_10_1155_2022_4248938 crossref_primary_10_3389_fsurg_2022_933297 crossref_primary_10_1016_j_jvoice_2024_01_016 crossref_primary_10_1039_D2RA00809B crossref_primary_10_1016_j_jvoice_2022_08_022 crossref_primary_10_3390_bioengineering11100989 crossref_primary_10_1038_s41598_021_93149_0 crossref_primary_10_1002_lary_29886 crossref_primary_10_1016_j_bspc_2023_105159 crossref_primary_10_1016_j_bspc_2023_105335 crossref_primary_10_1016_j_bspc_2024_106047 crossref_primary_10_1038_s41593_022_01023_7 crossref_primary_10_1371_journal_pcbi_1011774 crossref_primary_10_3390_app11031179 crossref_primary_10_3390_app12199791 crossref_primary_10_1016_j_procs_2023_10_633 crossref_primary_10_1038_s41467_021_26988_0 crossref_primary_10_5604_01_3001_0015_9501 crossref_primary_10_1109_ACCESS_2023_3284388
Cites_doi	10.1159/000111802 10.1007/s004050000299 10.1038/s41598-019-45758-z 10.1177/000348940010900501 10.1002/lary.23189 10.5281/zenodo.3377544 10.1097/01.mlg.0000179174.32345.41 10.1016/j.jvoice.2017.02.010 10.1002/lary.23568 10.1121/1.4747007 10.1016/j.anl.2014.11.001 10.1016/j.jvoice.2004.08.009 10.1109/TBME.2014.2364862 10.1016/j.jvoice.2014.01.016 10.1016/S0892-1997(96)80047-6 10.1109/TMI.2014.2377694 10.1016/j.jvoice.2016.11.010 10.1109/JTEHM.2018.2886021 10.1371/journal.pone.0227791 10.1016/j.jvoice.2007.09.008 10.2307/1932409 10.1016/S0892-1997(98)80060-X 10.1016/j.jvoice.2008.03.003 10.1046/j.1365-2273.2002.00559.x 10.1109/TMI.2016.2553401 10.1097/MLG.0b013e318161f9e1 10.3109/14015439.2012.731083 10.1007/s11517-017-1652-8 10.1109/ACCESS.2019.2917620 10.1016/j.jvoice.2015.07.009 10.1016/j.media.2007.04.005 10.1126/science.aaa8415 10.1177/000348940311200406 10.1044/1058-0360(2012/12-0014) 10.1016/j.jvoice.2014.02.008 10.1109/TMI.2016.2528162 10.1109/TBME.2002.800755 10.1177/000348940811700603 10.1001/jama.2016.17216 10.1002/lary.25511 10.1016/j.media.2017.07.005 10.3390/info11020125 10.1007/978-3-319-24574-4_28 10.4000/corela.3783 10.1007/978-3-642-19335-4_31 10.1007/s11548-018-01910-0 10.1007/s11517-019-01965-4 10.1609/aaai.v33i01.3301590 10.1371/journal.pone.0187486 10.1016/j.jvoice.2018.04.011 10.1002/lary.28475 10.1109/EuroSP.2016.36 10.1109/WACV.2017.58 10.1007/978-1-4612-4380-9_41 10.1109/ICASSP.2012.6287953 10.1145/2939672.2945386
ContentType	Journal Article
Copyright	The Author(s) 2020 The Author(s) 2020. 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) 2020 – notice: The Author(s) 2020. 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 CGR CUY CVF ECM EIF NPM 3V. 7X7 7XB 88E 8FE 8FH 8FI 8FJ 8FK ABUWG AFKRA AZQEC BBNVY BENPR BHPHI CCPQU DWQXO FYUFA GHDGH GNUQQ HCIFZ K9. LK8 M0S M1P M7P PHGZM PHGZT PIMPY PJZUB PKEHL PPXIY PQEST PQGLB PQQKQ PQUKI PRINS 7X8 5PM
DOI	10.1038/s41597-020-0526-3
DatabaseName	Springer Nature OA Free Journals CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed ProQuest Central (Corporate) Health & Medical Collection ProQuest Central (purchase pre-March 2016) Medical Database (Alumni Edition) ProQuest SciTech Collection ProQuest Natural Science Collection Hospital Premium Collection Hospital Premium Collection (Alumni Edition) ProQuest Central (Alumni) (purchase pre-March 2016) ProQuest Central (Alumni) ProQuest Central UK/Ireland ProQuest Central Essentials Biological Science Collection ProQuest Central Database Suite (ProQuest) Natural Science Collection (ProQuest) ProQuest One Community College ProQuest Central Korea Health Research Premium Collection Health Research Premium Collection (Alumni) ProQuest Central Student SciTech Collection (ProQuest) ProQuest Health & Medical Complete (Alumni) Biological Sciences Health & Medical Collection (Alumni) Medical 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 ProQuest One Academic UKI Edition ProQuest Central China MEDLINE - Academic PubMed Central (Full Participant titles)
DatabaseTitle	CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) 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 Central ProQuest One Applied & Life Sciences 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 Biological Science Collection 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	Publicly Available Content Database MEDLINE - Academic Publicly Available Content Database MEDLINE CrossRef
Database_xml	– sequence: 1 dbid: C6C name: Springer Nature OA Free Journals url: http://www.springeropen.com/ sourceTypes: Publisher – sequence: 2 dbid: NPM name: PubMed url: https://proxy.k.utb.cz/login?url=http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 3 dbid: EIF name: MEDLINE url: https://proxy.k.utb.cz/login?url=https://www.webofscience.com/wos/medline/basic-search sourceTypes: Index Database – sequence: 4 dbid: BENPR name: ProQuest Central url: https://www.proquest.com/central sourceTypes: Aggregation Database
DeliveryMethod	fulltext_linktorsrc
Discipline	Sciences (General)
EISSN	2052-4463
ExternalDocumentID	PMC7305104 32561845 10_1038_s41597_020_0526_3
Genre	Research Support, Non-U.S. Gov't Dataset Journal Article
GrantInformation_xml	– fundername: Deutsche Forschungsgemeinschaft (German Research Foundation) grantid: EC409/1-2; DO1247/8-1 funderid: https://doi.org/10.13039/501100001659 – fundername: Bundesministerium für Wirtschaft und Energie (Federal Ministry for Economic Affairs and Energy) grantid: ZF4010105BA8; ZF4010105BA8 funderid: https://doi.org/10.13039/501100006360 – fundername: U.S. Department of Health & Human Services \| NIH \| National Institute on Deafness and Other Communication Disorders (NIDCD) grantid: R01 DC013323 funderid: https://doi.org/10.13039/100000055 – fundername: Joachim Herz Stiftung (Joachim Herz Foundation) grantid: Addon-Fellowship funderid: https://doi.org/10.13039/100008662 – fundername: NIDCD NIH HHS grantid: R01 DC015570 – fundername: NIDCD NIH HHS grantid: R01 DC011300 – fundername: NIDCD NIH HHS grantid: R01 DC013323 – fundername: ; grantid: R01 DC013323 – fundername: ; grantid: Addon-Fellowship – fundername: ; grantid: EC409/1-2; DO1247/8-1 – fundername: ; grantid: ZF4010105BA8; ZF4010105BA8
GroupedDBID	0R~ 3V. 53G 5VS 7X7 88E 8FE 8FH 8FI 8FJ AAJSJ ABUWG ACGFS ACSFO ACSMW ADBBV ADRAZ AFKRA AGHDO AJTQC ALIPV ALMA_UNASSIGNED_HOLDINGS AOIJS BBNVY BCNDV BENPR BHPHI BPHCQ BVXVI C6C CCPQU DIK EBLON EBS EJD FYUFA GROUPED_DOAJ HCIFZ HMCUK HYE KQ8 LK8 M1P M48 M7P M~E NAO OK1 PGMZT PIMPY PQQKQ PROAC PSQYO RNT RNTTT RPM SNYQT UKHRP AASML AAYXX CITATION PHGZM PHGZT CGR CUY CVF ECM EIF NPM 7XB 8FK AARCD AZQEC DWQXO GNUQQ K9. PJZUB PKEHL PPXIY PQEST PQGLB PQUKI PRINS 7X8 PUEGO 5PM
ID	FETCH-LOGICAL-c498t-4d96b6e2647b4c9d2023015c90a966ae0a9bcebe2d6038cb4de470de87f945d13
IEDL.DBID	7X7
ISSN	2052-4463
IngestDate	Thu Aug 21 17:50:55 EDT 2025 Sun Aug 24 04:14:03 EDT 2025 Wed Aug 13 04:32:42 EDT 2025 Wed Aug 13 10:56:24 EDT 2025 Thu Apr 03 07:04:22 EDT 2025 Tue Jul 01 03:48:58 EDT 2025 Thu Apr 24 23:06:23 EDT 2025 Fri Feb 21 02:37:30 EST 2025
IsDoiOpenAccess	true
IsOpenAccess	true
IsPeerReviewed	true
IsScholarly	true
Issue	1
Language	English
License	The Creative Commons Public Domain Dedication waiver http://creativecommons.org/publicdomain/zero/1.0/ applies to the metadata files associated with this article. 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-c498t-4d96b6e2647b4c9d2023015c90a966ae0a9bcebe2d6038cb4de470de87f945d13
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 ObjectType-Article-2 ObjectType-Undefined-1 ObjectType-Feature-3 content type line 23
ORCID	0000-0002-7577-0267 0000-0002-5631-8240 0000-0003-0095-5360 0000-0003-3643-7776
OpenAccessLink	https://www.proquest.com/docview/2414909544?pq-origsite=%requestingapplication%
PMID	32561845
PQID	2414909544
PQPubID	2041912
ParticipantIDs	pubmedcentral_primary_oai_pubmedcentral_nih_gov_7305104 proquest_miscellaneous_2415305224 proquest_journals_2489908473 proquest_journals_2414909544 pubmed_primary_32561845 crossref_primary_10_1038_s41597_020_0526_3 crossref_citationtrail_10_1038_s41597_020_0526_3 springer_journals_10_1038_s41597_020_0526_3
ProviderPackageCode	CITATION AAYXX
PublicationCentury	2000
PublicationDate	2020-06-19
PublicationDateYYYYMMDD	2020-06-19
PublicationDate_xml	– month: 06 year: 2020 text: 2020-06-19 day: 19
PublicationDecade	2020
PublicationPlace	London
PublicationPlace_xml	– name: London – name: England
PublicationTitle	Scientific data
PublicationTitleAbbrev	Sci Data
PublicationTitleAlternate	Sci Data
PublicationYear	2020
Publisher	Nature Publishing Group UK Nature Publishing Group
Publisher_xml	– name: Nature Publishing Group UK – name: Nature Publishing Group
References	Zraick, Wendel, Smith-Olinde (CR56) 2005; 19 Echternach (CR60) 2017; 31 Lohscheller, Švec, Döllinger (CR12) 2013; 38 Patel, Dailey, Bless (CR10) 2008; 117 CR36 Roy, Merrill, Gray, Smith (CR3) 2005; 115 Litjens (CR19) 2017; 42 Tafiadis (CR41) 2019; 9 Jaccard (CR48) 1902; 38 Greenspan, Van Ginneken, Summers (CR20) 2016; 35 Martins, Pereira, Hidalgo, Tavares (CR5) 2014; 28 Gómez, Schützenberger, Semmler, Döllinger (CR49) 2019; 7 Andrade-Miranda, Godino-Llorente (CR45) 2017; 55 Shin (CR21) 2016; 35 Gulshan (CR29) 2016; 316 Gong, Zhong, Hu (CR22) 2019; 7 Döllinger (CR37) 2002; 49 CR7 Jordan, Mitchell (CR23) 2015; 349 Cohen, Kim, Roy, Asche, Courey (CR2) 2012; 122 CR46 Yamauchi (CR35) 2014; 28 CR44 CR42 Gloger, Lehnert, Schrade, Völzke (CR16) 2015; 62 Doellinger, Lohscheller, McWhorter, Kunduk (CR14) 2009; 23 Gómez (CR61) 2020 Wilson, Deary, Millar, Mackenzie (CR1) 2002; 27 Menze (CR27) 2014; 34 Lohscheller, Eysholdt (CR38) 2008; 118 CR18 Dejonckere (CR40) 2001; 258 Pedersen, Jønsson, Mahmood, Agersted (CR13) 2016; 4 Kreiman (CR33) 2012; 132 Fehling, Grosch, Schuster, Schick, Lohscheller (CR17) 2020; 15 CR58 CR57 CR54 CR53 CR52 Pestana, Vaz-Freitas, Manso (CR6) 2017; 31 CR51 CR50 Deliyski (CR9) 2008; 60 Švec, Schutte (CR31) 1996; 10 Döllinger, Dubrovskiy, Patel (CR15) 2012; 122 Noordzij, Woo (CR34) 2000; 109 Roy, Kim, Courey, Cohen (CR4) 2016; 126 Lohscheller, Toy, Rosanowski, Eysholdt, Döllinger (CR32) 2007; 11 Barsties, De Bodt (CR39) 2015; 42 Roy (CR30) 2013; 22 CR28 Poburka, Bless (CR55) 1998; 12 CR26 CR25 CR24 CR67 Zhang, Bieging, Tsui, Jiang (CR43) 2010; 24 CR66 CR65 CR64 CR63 Dice (CR47) 1945; 26 CR62 Cutler, Cleveland (CR8) 2002; 10 Heman-Ackah (CR11) 2003; 112 Patel, Walker, Sivasankar (CR59) 2016; 30 526_CR54 526_CR57 526_CR7 526_CR51 526_CR50 526_CR53 526_CR52 526_CR18 LR Dice (526_CR47) 1945; 26 N Roy (526_CR4) 2016; 126 526_CR58 H Greenspan (526_CR20) 2016; 35 N Roy (526_CR3) 2005; 115 P Gómez (526_CR49) 2019; 7 M Echternach (526_CR60) 2017; 31 BH Menze (526_CR27) 2014; 34 RR Patel (526_CR59) 2016; 30 G Litjens (526_CR19) 2017; 42 A Yamauchi (526_CR35) 2014; 28 H-C Shin (526_CR21) 2016; 35 JG Švec (526_CR31) 1996; 10 JP Noordzij (526_CR34) 2000; 109 M Döllinger (526_CR37) 2002; 49 526_CR66 526_CR65 526_CR24 526_CR67 526_CR62 Z Gong (526_CR22) 2019; 7 526_CR64 P Jaccard (526_CR48) 1902; 38 526_CR63 526_CR26 526_CR25 V Gulshan (526_CR29) 2016; 316 526_CR28 J Lohscheller (526_CR12) 2013; 38 J Lohscheller (526_CR38) 2008; 118 M Pedersen (526_CR13) 2016; 4 RI Zraick (526_CR56) 2005; 19 G Andrade-Miranda (526_CR45) 2017; 55 RHG Martins (526_CR5) 2014; 28 O Gloger (526_CR16) 2015; 62 J Lohscheller (526_CR32) 2007; 11 N Roy (526_CR30) 2013; 22 PM Pestana (526_CR6) 2017; 31 526_CR36 YD Heman-Ackah (526_CR11) 2003; 112 R Patel (526_CR10) 2008; 117 Y Zhang (526_CR43) 2010; 24 MI Jordan (526_CR23) 2015; 349 DD Deliyski (526_CR9) 2008; 60 M Döllinger (526_CR15) 2012; 122 D Tafiadis (526_CR41) 2019; 9 SM Cohen (526_CR2) 2012; 122 526_CR44 MK Fehling (526_CR17) 2020; 15 526_CR46 P Gómez (526_CR61) 2020 M Doellinger (526_CR14) 2009; 23 PH Dejonckere (526_CR40) 2001; 258 526_CR42 JA Wilson (526_CR1) 2002; 27 B Barsties (526_CR39) 2015; 42 JL Cutler (526_CR8) 2002; 10 J Kreiman (526_CR33) 2012; 132 BJ Poburka (526_CR55) 1998; 12
References_xml	– volume: 60 start-page: 33 year: 2008 end-page: 44 ident: CR9 article-title: Clinical implementation of laryngeal high-speed videoendoscopy: challenges and evolution publication-title: Folia Phoniatr. Logo. doi: 10.1159/000111802 – volume: 258 start-page: 77 year: 2001 end-page: 82 ident: CR40 article-title: A basic protocol for functional assessment of voice pathology, especially for investigating the efficacy of (phonosurgical) treatments and evaluating new assessment techniques publication-title: Eur. Arch. Oto-rhino-l. doi: 10.1007/s004050000299 – volume: 9 start-page: 1 year: 2019 end-page: 9 ident: CR41 article-title: Checking for voice disorders without clinical intervention: The greek and global vhi thresholds for voice disordered patients publication-title: Scientific reports doi: 10.1038/s41598-019-45758-z – volume: 109 start-page: 441 year: 2000 end-page: 446 ident: CR34 article-title: Glottal area waveform analysis of benign vocal fold lesions before and after surgery publication-title: Ann. Oto. Rhinol. Laryn. doi: 10.1177/000348940010900501 – volume: 122 start-page: 1582 year: 2012 end-page: 1588 ident: CR2 article-title: Direct health care costs of laryngeal diseases and disorders publication-title: Laryngoscope doi: 10.1002/lary.23189 – ident: CR51 – year: 2020 ident: CR61 publication-title: Zenodo doi: 10.5281/zenodo.3377544 – volume: 115 start-page: 1988 year: 2005 end-page: 1995 ident: CR3 article-title: Voice disorders in the general population: prevalence, risk factors, and occupational impact publication-title: Laryngoscope doi: 10.1097/01.mlg.0000179174.32345.41 – volume: 31 start-page: 722 year: 2017 end-page: 727 ident: CR6 article-title: Prevalence of voice disorders in singers: Systematic review and meta-analysis publication-title: J. Voice doi: 10.1016/j.jvoice.2017.02.010 – volume: 122 start-page: 2511 year: 2012 end-page: 2518 ident: CR15 article-title: Spatiotemporal analysis of vocal fold vibrations between children and adults publication-title: Laryngoscope doi: 10.1002/lary.23568 – ident: CR54 – volume: 132 start-page: 2625 year: 2012 end-page: 2632 ident: CR33 article-title: Variability in the relationships among voice quality, harmonic amplitudes, open quotient, and glottal area waveform shape in sustained phonation publication-title: J. Acoust. Soc. Am. doi: 10.1121/1.4747007 – volume: 42 start-page: 183 year: 2015 end-page: 188 ident: CR39 article-title: Assessment of voice quality: current state-of-the-art publication-title: Auris Nasus Larynx doi: 10.1016/j.anl.2014.11.001 – ident: CR58 – ident: CR25 – ident: CR42 – volume: 19 start-page: 574 year: 2005 end-page: 581 ident: CR56 article-title: The effect of speaking task on perceptual judgment of the severity of dysphonic voice publication-title: J. Voice doi: 10.1016/j.jvoice.2004.08.009 – volume: 62 start-page: 795 year: 2015 end-page: 806 ident: CR16 article-title: Fully automated glottis segmentation in endoscopic videos using local color and shape features of glottal regions publication-title: IEEE Trans. Biomed. Eng. doi: 10.1109/TBME.2014.2364862 – volume: 28 start-page: 525 year: 2014 end-page: 531 ident: CR35 article-title: Age- and gender-related difference of vocal fold vibration and glottal configuration in normal speakers: analysis with glottal area waveform publication-title: J. Voice doi: 10.1016/j.jvoice.2014.01.016 – ident: CR46 – volume: 10 start-page: 462 year: 2002 end-page: 466 ident: CR8 article-title: The clinical usefulness of laryngeal videostroboscopy and the role of high-speed cinematography in laryngeal evaluation publication-title: Curr. Opin. Otolaryngo. – ident: CR67 – ident: CR50 – volume: 10 start-page: 201 year: 1996 end-page: 205 ident: CR31 article-title: Videokymography: high-speed line scanning of vocal fold vibration publication-title: J. Voice doi: 10.1016/S0892-1997(96)80047-6 – volume: 34 start-page: 1993 year: 2014 end-page: 2024 ident: CR27 article-title: The multimodal brain tumor image segmentation benchmark (brats) publication-title: IEEE Trans. Med. Imaging doi: 10.1109/TMI.2014.2377694 – volume: 31 start-page: 381 year: 2017 end-page: e5 ident: CR60 article-title: Oscillatory characteristics of the vocal folds across the tenor passaggio publication-title: J. Voice doi: 10.1016/j.jvoice.2016.11.010 – volume: 7 start-page: 1 year: 2019 end-page: 11 ident: CR49 article-title: Laryngeal pressure estimation with a recurrent neural network publication-title: IEEE J. Translational Eng. Health Med. doi: 10.1109/JTEHM.2018.2886021 – ident: CR57 – volume: 15 start-page: e0227791 year: 2020 ident: CR17 article-title: Fully automatic segmentation of glottis and vocal folds in endoscopic laryngeal high-speed videos using a deep convolutional lstm network publication-title: Plos one doi: 10.1371/journal.pone.0227791 – volume: 23 start-page: 175 year: 2009 end-page: 181 ident: CR14 article-title: Variability of normal vocal fold dynamics for different vocal loading in one healthy subject investigated by phonovibrograms publication-title: Journal of Voice doi: 10.1016/j.jvoice.2007.09.008 – ident: CR36 – volume: 26 start-page: 297 year: 1945 end-page: 302 ident: CR47 article-title: Measures of the amount of ecologic association between species publication-title: Ecology doi: 10.2307/1932409 – volume: 12 start-page: 513 year: 1998 end-page: 526 ident: CR55 article-title: A multi-media, computer-based method for stroboscopy rating training publication-title: J. Voice doi: 10.1016/S0892-1997(98)80060-X – ident: CR64 – ident: CR26 – volume: 24 start-page: 21 year: 2010 end-page: 29 ident: CR43 article-title: Efficient and effective extraction of vocal fold vibratory patterns from high-speed digital imaging publication-title: J Voice doi: 10.1016/j.jvoice.2008.03.003 – volume: 27 start-page: 179 year: 2002 end-page: 182 ident: CR1 article-title: The quality of life impact of dysphonia publication-title: Clin. Otolaryngol. Allied Sci. doi: 10.1046/j.1365-2273.2002.00559.x – volume: 35 start-page: 1153 year: 2016 end-page: 1159 ident: CR20 article-title: Guest editorial deep learning in medical imaging: Overview and future promise of an exciting new technique publication-title: IEEE Trans. Med. Imaging doi: 10.1109/TMI.2016.2553401 – volume: 118 start-page: 753 year: 2008 end-page: 758 ident: CR38 article-title: Phonovibrogram visualization of entire vocal fold dynamics publication-title: Laryngoscope doi: 10.1097/MLG.0b013e318161f9e1 – volume: 38 start-page: 182 year: 2013 end-page: 192 ident: CR12 article-title: Vocal fold vibration amplitude, open quotient, speed quotient and their variability along glottal length: kymographic data from normal subjects publication-title: Logop. Phoniatr. Voco. doi: 10.3109/14015439.2012.731083 – ident: CR18 – ident: CR66 – ident: CR53 – volume: 55 start-page: 2123 year: 2017 end-page: 2141 ident: CR45 article-title: Glottal gap tracking by a continuous background modeling using inpainting publication-title: Med. Biol. Eng. Comput. doi: 10.1007/s11517-017-1652-8 – volume: 7 start-page: 64323 year: 2019 end-page: 64350 ident: CR22 article-title: Diversity in machine learning publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2917620 – volume: 30 start-page: 427 year: 2016 end-page: 433 ident: CR59 article-title: Spatiotemporal quantification of vocal fold vibration after exposure to superficial laryngeal dehydration: A preliminary study publication-title: J. Voice doi: 10.1016/j.jvoice.2015.07.009 – volume: 11 start-page: 400 year: 2007 end-page: 413 ident: CR32 article-title: Clinically evaluated procedure for the reconstruction of vocal fold vibrations from endoscopic digital high-speed videos publication-title: Med. Image Anal. doi: 10.1016/j.media.2007.04.005 – volume: 349 start-page: 255 year: 2015 end-page: 260 ident: CR23 article-title: Machine learning: Trends, perspectives, and prospects publication-title: Science doi: 10.1126/science.aaa8415 – volume: 112 start-page: 324 year: 2003 end-page: 333 ident: CR11 article-title: Cepstral peak prominence: a more reliable measure of dysphonia publication-title: Ann. Oto. Rhinol. Laryn. doi: 10.1177/000348940311200406 – ident: CR63 – volume: 22 start-page: 212 year: 2013 end-page: 226 ident: CR30 article-title: Evidence-based clinical voice assessment: a systematic review publication-title: Am. J. Speech-Lang. Pat. doi: 10.1044/1058-0360(2012/12-0014) – volume: 28 start-page: 716 year: 2014 end-page: 724 ident: CR5 article-title: Voice disorders in teachers. a review publication-title: J. Voice doi: 10.1016/j.jvoice.2014.02.008 – volume: 38 start-page: 69 year: 1902 end-page: 130 ident: CR48 article-title: Lois de distribution florale dans la zone alpine publication-title: Bulletin de la Société vaudoise des sciences naturelles – ident: CR44 – volume: 35 start-page: 1285 year: 2016 end-page: 1298 ident: CR21 article-title: Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning publication-title: IEEE Trans. Med. Imaging doi: 10.1109/TMI.2016.2528162 – volume: 4 start-page: 2 year: 2016 ident: CR13 article-title: Which mathematical and physiological formulas are describing voice pathology: An overview publication-title: J Gen Pract – volume: 49 start-page: 773 year: 2002 end-page: 781 ident: CR37 article-title: Vibration parameter extraction from endoscopic image series of the vocal folds publication-title: IEEE Trans. Biomed. Eng. doi: 10.1109/TBME.2002.800755 – ident: CR65 – ident: CR52 – volume: 117 start-page: 413 year: 2008 end-page: 424 ident: CR10 article-title: Comparison of high-speed digital imaging with stroboscopy for laryngeal imaging of glottal disorders publication-title: Ana. Oto. Rhinolo. Laryng. doi: 10.1177/000348940811700603 – volume: 316 start-page: 2402 year: 2016 end-page: 2410 ident: CR29 article-title: Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs publication-title: JAMA doi: 10.1001/jama.2016.17216 – volume: 126 start-page: 421 year: 2016 end-page: 428 ident: CR4 article-title: Voice disorders in the elderly: A national database study publication-title: Laryngoscope doi: 10.1002/lary.25511 – ident: CR7 – ident: CR28 – ident: CR62 – ident: CR24 – volume: 42 start-page: 60 year: 2017 end-page: 88 ident: CR19 article-title: A survey on deep learning in medical image analysis publication-title: Med. Image Anal. doi: 10.1016/j.media.2017.07.005 – volume: 132 start-page: 2625 year: 2012 ident: 526_CR33 publication-title: J. Acoust. Soc. Am. doi: 10.1121/1.4747007 – volume: 38 start-page: 69 year: 1902 ident: 526_CR48 publication-title: Bulletin de la Société vaudoise des sciences naturelles – volume: 122 start-page: 2511 year: 2012 ident: 526_CR15 publication-title: Laryngoscope doi: 10.1002/lary.23568 – ident: 526_CR63 doi: 10.3390/info11020125 – volume: 7 start-page: 1 year: 2019 ident: 526_CR49 publication-title: IEEE J. Translational Eng. Health Med. doi: 10.1109/JTEHM.2018.2886021 – volume: 15 start-page: e0227791 year: 2020 ident: 526_CR17 publication-title: Plos one doi: 10.1371/journal.pone.0227791 – volume: 115 start-page: 1988 year: 2005 ident: 526_CR3 publication-title: Laryngoscope doi: 10.1097/01.mlg.0000179174.32345.41 – volume: 22 start-page: 212 year: 2013 ident: 526_CR30 publication-title: Am. J. Speech-Lang. Pat. doi: 10.1044/1058-0360(2012/12-0014) – ident: 526_CR24 – volume: 117 start-page: 413 year: 2008 ident: 526_CR10 publication-title: Ana. Oto. Rhinolo. Laryng. doi: 10.1177/000348940811700603 – volume: 4 start-page: 2 year: 2016 ident: 526_CR13 publication-title: J Gen Pract – ident: 526_CR28 – volume: 55 start-page: 2123 year: 2017 ident: 526_CR45 publication-title: Med. Biol. Eng. Comput. doi: 10.1007/s11517-017-1652-8 – ident: 526_CR57 – volume: 19 start-page: 574 year: 2005 ident: 526_CR56 publication-title: J. Voice doi: 10.1016/j.jvoice.2004.08.009 – ident: 526_CR62 doi: 10.1007/978-3-319-24574-4_28 – volume: 35 start-page: 1285 year: 2016 ident: 526_CR21 publication-title: IEEE Trans. Med. Imaging doi: 10.1109/TMI.2016.2528162 – volume: 316 start-page: 2402 year: 2016 ident: 526_CR29 publication-title: JAMA doi: 10.1001/jama.2016.17216 – ident: 526_CR64 – volume: 31 start-page: 381 year: 2017 ident: 526_CR60 publication-title: J. Voice doi: 10.1016/j.jvoice.2016.11.010 – ident: 526_CR50 doi: 10.4000/corela.3783 – volume: 23 start-page: 175 year: 2009 ident: 526_CR14 publication-title: Journal of Voice doi: 10.1016/j.jvoice.2007.09.008 – volume: 31 start-page: 722 year: 2017 ident: 526_CR6 publication-title: J. Voice doi: 10.1016/j.jvoice.2017.02.010 – volume: 112 start-page: 324 year: 2003 ident: 526_CR11 publication-title: Ann. Oto. Rhinol. Laryn. doi: 10.1177/000348940311200406 – ident: 526_CR53 doi: 10.1007/978-3-642-19335-4_31 – volume: 34 start-page: 1993 year: 2014 ident: 526_CR27 publication-title: IEEE Trans. Med. Imaging doi: 10.1109/TMI.2014.2377694 – volume: 26 start-page: 297 year: 1945 ident: 526_CR47 publication-title: Ecology doi: 10.2307/1932409 – volume: 10 start-page: 201 year: 1996 ident: 526_CR31 publication-title: J. Voice doi: 10.1016/S0892-1997(96)80047-6 – ident: 526_CR46 doi: 10.1007/s11548-018-01910-0 – volume: 109 start-page: 441 year: 2000 ident: 526_CR34 publication-title: Ann. Oto. Rhinol. Laryn. doi: 10.1177/000348940010900501 – volume: 49 start-page: 773 year: 2002 ident: 526_CR37 publication-title: IEEE Trans. Biomed. Eng. doi: 10.1109/TBME.2002.800755 – ident: 526_CR42 doi: 10.1007/s11517-019-01965-4 – ident: 526_CR26 doi: 10.1609/aaai.v33i01.3301590 – year: 2020 ident: 526_CR61 doi: 10.5281/zenodo.3377544 – ident: 526_CR54 – ident: 526_CR7 doi: 10.1371/journal.pone.0187486 – ident: 526_CR65 – ident: 526_CR36 doi: 10.1016/j.jvoice.2018.04.011 – ident: 526_CR58 doi: 10.1002/lary.28475 – ident: 526_CR25 doi: 10.1109/EuroSP.2016.36 – volume: 35 start-page: 1153 year: 2016 ident: 526_CR20 publication-title: IEEE Trans. Med. Imaging doi: 10.1109/TMI.2016.2553401 – ident: 526_CR66 doi: 10.1109/WACV.2017.58 – volume: 349 start-page: 255 year: 2015 ident: 526_CR23 publication-title: Science doi: 10.1126/science.aaa8415 – volume: 122 start-page: 1582 year: 2012 ident: 526_CR2 publication-title: Laryngoscope doi: 10.1002/lary.23189 – volume: 42 start-page: 183 year: 2015 ident: 526_CR39 publication-title: Auris Nasus Larynx doi: 10.1016/j.anl.2014.11.001 – volume: 7 start-page: 64323 year: 2019 ident: 526_CR22 publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2917620 – volume: 24 start-page: 21 year: 2010 ident: 526_CR43 publication-title: J Voice doi: 10.1016/j.jvoice.2008.03.003 – volume: 11 start-page: 400 year: 2007 ident: 526_CR32 publication-title: Med. Image Anal. doi: 10.1016/j.media.2007.04.005 – volume: 126 start-page: 421 year: 2016 ident: 526_CR4 publication-title: Laryngoscope doi: 10.1002/lary.25511 – ident: 526_CR51 – volume: 10 start-page: 462 year: 2002 ident: 526_CR8 publication-title: Curr. Opin. Otolaryngo. – volume: 28 start-page: 525 year: 2014 ident: 526_CR35 publication-title: J. Voice doi: 10.1016/j.jvoice.2014.01.016 – volume: 12 start-page: 513 year: 1998 ident: 526_CR55 publication-title: J. Voice doi: 10.1016/S0892-1997(98)80060-X – volume: 60 start-page: 33 year: 2008 ident: 526_CR9 publication-title: Folia Phoniatr. Logo. doi: 10.1159/000111802 – volume: 118 start-page: 753 year: 2008 ident: 526_CR38 publication-title: Laryngoscope doi: 10.1097/MLG.0b013e318161f9e1 – volume: 9 start-page: 1 year: 2019 ident: 526_CR41 publication-title: Scientific reports doi: 10.1038/s41598-019-45758-z – volume: 38 start-page: 182 year: 2013 ident: 526_CR12 publication-title: Logop. Phoniatr. Voco. doi: 10.3109/14015439.2012.731083 – volume: 28 start-page: 716 year: 2014 ident: 526_CR5 publication-title: J. Voice doi: 10.1016/j.jvoice.2014.02.008 – ident: 526_CR18 – ident: 526_CR67 doi: 10.1007/978-1-4612-4380-9_41 – volume: 27 start-page: 179 year: 2002 ident: 526_CR1 publication-title: Clin. Otolaryngol. Allied Sci. doi: 10.1046/j.1365-2273.2002.00559.x – volume: 62 start-page: 795 year: 2015 ident: 526_CR16 publication-title: IEEE Trans. Biomed. Eng. doi: 10.1109/TBME.2014.2364862 – volume: 258 start-page: 77 year: 2001 ident: 526_CR40 publication-title: Eur. Arch. Oto-rhino-l. doi: 10.1007/s004050000299 – ident: 526_CR44 doi: 10.1109/ICASSP.2012.6287953 – volume: 30 start-page: 427 year: 2016 ident: 526_CR59 publication-title: J. Voice doi: 10.1016/j.jvoice.2015.07.009 – volume: 42 start-page: 60 year: 2017 ident: 526_CR19 publication-title: Med. Image Anal. doi: 10.1016/j.media.2017.07.005 – ident: 526_CR52 doi: 10.1145/2939672.2945386
SSID	ssj0001340570
Score	2.4495754
Snippet	Laryngeal videoendoscopy is one of the main tools in clinical examinations for voice disorders and voice research. Using high-speed videoendoscopy, it is...
SourceID	pubmedcentral proquest pubmed crossref springer
SourceType	Open Access Repository Aggregation Database Index Database Enrichment Source Publisher
StartPage	186
SubjectTerms	692/308 692/698 Data Descriptor Deep learning Endoscopy Glottis - diagnostic imaging Glottis - physiology Humanities and Social Sciences Humans Image processing multidisciplinary Neural networks Oscillations Researchers Science Science (multidisciplinary) Segmentation Vocal Cords - diagnostic imaging Vocal Cords - physiology Voice Disorders - diagnosis
SummonAdditionalLinks	– databaseName: Scholars Portal Journals: Open Access dbid: M48 link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwfV1LT8MwDI5gXLgg3gwGChIHXoWuTdPmgNBAsAkBlzGJW5WmKZvYOtg6Cf49TpoWjQGnHhJHqe3En-PERuiAxFwI14lAAnXXItxhVkS8xKIcnBEq7CDRB24Pj7TVIXfP3vMcKspbGQaOf3XtVD2pzqh_9vH-eQkL_iJ_Mh6cj8EIqZyijgrrOtRy59ECGCaqfLEHg_b1kYur0EkZ2_yNcto6zUDO2ZuTP8Kn2irdLqMlAydxI5f_CpqT6SpaMQt2jA9NVumjNXR91Wjet08xx_oOYdfUC8FX0LM74KNXDPAVNybZUCdxxc3-MMt6Y9yWLwPzPildR53bm6frlmUqKFiCsCCzSMxoRCWAHj8igsWqVjrYf8FsDm4Ol_CJBIjRiSmwQ0QklsS3Yxn4CSNeXHc3UCUdpnIL4YhxGSRJ7EtHEsIFtx2gJIA-Eg9AgltFdsG5UJj04qrKRT_UYW43CHNmh8DsUDE7BJLjkuQtz63xX-daIY6w0JIQpEwYgERC_mgGb9IG-wvU-2UzrB8VFOGpHE70EB7seYBkqmgzF245GRfwIHjAXhX5U2IvO6jc3NMtaa-rc3TDxgm7HYx5UijI97T-_Mft__9xBy06WlWpVWc1VMlGE7kLkCiL9rSifwEyqAX_ priority: 102 providerName: Scholars Portal – databaseName: Springer Nature HAS Fully OA dbid: AAJSJ link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1LT8MwDLZgXLgg3oyXgsSBV0WXpI8cOwRME3AZSNyqNE3ZxNYh1v1_3CwtGhNInHpIHKV2Yn-JHRvglKdSKUYTlECLOVxS4STcyxxf4mHEV26YmQu3xye_88K7r97rErSqtzAmaN-ktDRquooOu56goSnzhtLSdUt9hy3DSojWjzZgJYq6ve73xQorMUjtwWThIu28DVoAlovxkT-cpMb23K3DmgWNJJpNcwOWdL4JG3ZbTsiZzR19vgU37ej-oXdFJDGRgn1bFYS0sWd_JD_fCYJUEk2LsUnVSu6H46IYTEhPv43sK6R8G17ubp9vOo6tk-AoLsLC4anwE18jtAkSrkRaVkRHK6-EK_EwIzV-EoXCoqmP7FAJTzUP3FSHQSa4l7bYDjTyca73gCRC6jDL0kBTzblU0qVIyRFjZB5CAdYEt-JcrGwS8bKWxTA2zmwWxjNmx8jsuGR2jCQXNcnHLIPGX50PK3HEdjNNYgQZXCAU5PyXZjwzumhlkfqkbsZdUro-ZK7HUzOEh5oN8UoTdmfCrSfDEPXhOddrQjAn9rpDmYF7viUf9E0mblSPqNNwzMtqgXxP69d_3P9X7wNYpWbl-k5LHEKj-JzqI8RBRXJsV_4X4N4BuA priority: 102 providerName: Springer Nature
Title	BAGLS, a multihospital Benchmark for Automatic Glottis Segmentation
URI	https://link.springer.com/article/10.1038/s41597-020-0526-3 https://www.ncbi.nlm.nih.gov/pubmed/32561845 https://www.proquest.com/docview/2414909544 https://www.proquest.com/docview/2489908473 https://www.proquest.com/docview/2415305224 https://pubmed.ncbi.nlm.nih.gov/PMC7305104
Volume	7
hasFullText	1
inHoldings	1
isFullTextHit
isPrint
link	http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwfV1Lb9swDBa29rLLsO6ZrQs0YIe9hDoWLVunwQn6QLAWw7ICuRmyJDfFWrurnf8_SlEcZEV7sQFLMmSSIj-RMknIRzBKax6XyIERZ6BiyUpIKiYUbkaEjrLKO9xOz8TJOUznyTw43NpwrHKtE72iNo12PvIDtDQgEQ8AfL_5y1zVKBddDSU0HpNdn7oM5TmdpxsfC3dwpA9m8uygRXvl0o_GLgIcC8a3zdEdjHn3qOR_8VJvho6ekacBP9J8xfA98sjWz8leWKEt_RTSSH9-QSbj_PjH7BtV1B8aXIQCIXSMPRfX6vYPRbxK82XX-Kyt9Piq6brLls7sxXX4Ial-Sc6PDn9PTlgomcA0yKxjYKQohUWUk5agpXHF0dHgaxkp3Ncoi7dSI99iI5AcugRjIY2MzdJKQmJG_BXZqZvaviG0lMpmVWVSG1sApVUU40hAuFEliAr4gERryhU65BN3ZS2uCh_X5lmxInaBxC4csQsc8qUfcrNKpvFQ5_01O4qwrtpiIwX3NOP2MUKDi6M_9M24YFwURNW2WfpXJKjkELoMyOsVc_vJcASAuOVNBiTdYnvfwSXj3m6pLxc-KTdqSlRv-M6vawHZTOveb3z78De-I09iL6qCjeQ-2elul_Y9YqCuHHpBH5LdPJ_OpngfH579_IVPJ2Iy9H4FvJ5C9g-0awjX
linkProvider	ProQuest
linkToHtml	http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1LT9wwEB7R5dBeKqCvBdq6Uiv1QUTWdl6HqtrltZRlVRWQuKWO7XQRkFCSVdU_1d_YceJktUVw45SDPVY8M5757LFnAN5yJaRkNEEJ9JjDBY2chHup4wvcjPjSDdPqwO1w7A9P-NdT73QB_jZvYcy1ysYmVoZa5dKckW-ip-ER4gHOv1z9ckzVKBNdbUpo1GpxoP_8xi1b8Xl_G-X7jtLdneOtoWOrCjiSR2HpcBX5ia8RCAQJl5Ey9cPRJ8rIFQj9hcZPInFqVPkuC2XCleaBq3QYpBH3VI_huA9gkZsXrR1YHOyMv32fneowA4Da8CkLNwv0kCbhKTUxZ-o7bN4B3kC1Ny9n_hehrRzf7hI8toiV9GsVW4YFna3AsrUJBXlvE1d_eAJbg_7e6GiDCFJdU5zYkiRkgD0nl-L6nCBCJv1pmVd5YsneRV6WZwU50j8v7ROo7Cmc3As7n0EnyzP9AkgSCR2mqQo01ZwLKVyKlBwBTuohDmFdcBvOxdJmMDeFNC7iKpLOwrhmdozMjg2zYyT52JJc1ek77uq83ogjtiu5iGd6d0szblhddPFI_aZtxiVq4i4i0_m0GsJDs4pgqQvPa-G2P8MQcuIm2-tCMCf2toNJ_z3fkp1NqjTgaJvRoOKYnxoFmf3WrXNcvXuOr-Hh8PhwFI_2xwdr8IhWaus7vWgdOuX1VL9EBFYmr6zaE_hx3yvtHxDkQY4
linkToPdf	http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1bTxQxFG4QE-OLAW8soNZEEy80O9t2Ln0wZgEXECQmSLJvpdN2XALMIDMb41_z13nauWxWAm88zUMvmZ7r1572HITecKO0ZjQFDgwY4YoKkvIwI5GCzUikgyTzB27fDqPdY_51HI4X0N_2LYy7VtnaRG-oTaHdGXkfPA0XgAc472fNtYjv26PPl7-IqyDlIq1tOY1aRPbtn9-wfSs_7W0Dr99SOvryY2uXNBUGiOYiqQg3IkojC6AgTrkWxtUSB_-oRaBgG6AsfFINy6QmCliiU24sjwNjkzgTPDQDBvPeQ_djBqgKdCkex7PzHeagUBdIZUm_BF_pUp9SF32mEWHzrvAavr1-TfO_WK13gaMl9KjBrnhYC9syWrD5Y7TcWIcSv2tSWL9_grY2hzsHRxtYYX9hcdIUJ8Gb0HNyoa7OMGBlPJxWhc8Yi3fOi6o6LfGR_XnRPIbKn6LjOyHmM7SYF7ldQTgVyiZZZmJLLedKq4DCSA5QJwsBkbAeClrKSd3kMnclNc6lj6mzRNbElkBs6YgtYciHbshlncjjts7rLTtko9OlnEngDc2wdQ3A2cPo110zKKuLwKjcFlM_RQgGFmBTDz2vmdv9DAPwCdvtsIfiObZ3HVwi8PmW_HTiE4KDlQbTCnN-bAVk9ls3rnH19jW-Qg9Av-TB3uH-GnpIvdRGZCDW0WJ1NbUvAIpV6Usv8xid3LWS_QNx_URV
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=BAGLS%2C+a+multihospital+Benchmark+for+Automatic+Glottis+Segmentation&rft.jtitle=Scientific+data&rft.au=G%C3%B3mez%2C+Pablo&rft.au=Kist%2C+Andreas+M&rft.au=Schlegel%2C+Patrick&rft.au=Berry%2C+David+A&rft.date=2020-06-19&rft.pub=Nature+Publishing+Group&rft.eissn=2052-4463&rft.volume=7&rft.issue=1&rft_id=info:doi/10.1038%2Fs41597-020-0526-3&rft.externalDBID=HAS_PDF_LINK
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2052-4463&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2052-4463&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2052-4463&client=summon