Predicting semantic segmentation quality in laryngeal endoscopy images
Endoscopy is a major tool for assessing the physiology of inner organs. Contemporary artificial intelligence methods are used to fully automatically label medical important classes on a pixel-by-pixel level. This so-called semantic segmentation is for example used to detect cancer tissue or to asses...
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| Published in: | PloS one Vol. 20; no. 7; p. e0314573 |
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| Main Authors: | , , , , |
| Format: | Journal Article |
| Language: | English |
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03.07.2025
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| ISSN: | 1932-6203, 1932-6203 |
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| Abstract | Endoscopy is a major tool for assessing the physiology of inner organs. Contemporary artificial intelligence methods are used to fully automatically label medical important classes on a pixel-by-pixel level. This so-called semantic segmentation is for example used to detect cancer tissue or to assess laryngeal physiology. However, due to the diversity of patients presenting, it is necessary to judge the segmentation quality. In this study, we present a fully automatic system to evaluate the segmentation performance in laryngeal endoscopy images. We showcase on glottal area segmentation that the predicted segmentation quality represented by the intersection over union metric is on par with human raters. Using a traffic light system, we are able to identify problematic segmentation frames to allow human-in-the-loop improvements, important for the clinical adaptation of automatic analysis procedures. |
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| AbstractList | Endoscopy is a major tool for assessing the physiology of inner organs. Contemporary artificial intelligence methods are used to fully automatically label medical important classes on a pixel-by-pixel level. This so-called semantic segmentation is for example used to detect cancer tissue or to assess laryngeal physiology. However, due to the diversity of patients presenting, it is necessary to judge the segmentation quality. In this study, we present a fully automatic system to evaluate the segmentation performance in laryngeal endoscopy images. We showcase on glottal area segmentation that the predicted segmentation quality represented by the intersection over union metric is on par with human raters. Using a traffic light system, we are able to identify problematic segmentation frames to allow human-in-the-loop improvements, important for the clinical adaptation of automatic analysis procedures. Endoscopy is a major tool for assessing the physiology of inner organs. Contemporary artificial intelligence methods are used to fully automatically label medical important classes on a pixel-by-pixel level. This so-called semantic segmentation is for example used to detect cancer tissue or to assess laryngeal physiology. However, due to the diversity of patients presenting, it is necessary to judge the segmentation quality. In this study, we present a fully automatic system to evaluate the segmentation performance in laryngeal endoscopy images. We showcase on glottal area segmentation that the predicted segmentation quality represented by the intersection over union metric is on par with human raters. Using a traffic light system, we are able to identify problematic segmentation frames to allow human-in-the-loop improvements, important for the clinical adaptation of automatic analysis procedures.Endoscopy is a major tool for assessing the physiology of inner organs. Contemporary artificial intelligence methods are used to fully automatically label medical important classes on a pixel-by-pixel level. This so-called semantic segmentation is for example used to detect cancer tissue or to assess laryngeal physiology. However, due to the diversity of patients presenting, it is necessary to judge the segmentation quality. In this study, we present a fully automatic system to evaluate the segmentation performance in laryngeal endoscopy images. We showcase on glottal area segmentation that the predicted segmentation quality represented by the intersection over union metric is on par with human raters. Using a traffic light system, we are able to identify problematic segmentation frames to allow human-in-the-loop improvements, important for the clinical adaptation of automatic analysis procedures. |
| Audience | Academic |
| Author | Kist, Andreas M. Gritsch, Florian Razi, Sina Schützenberger, Anne Groh, René |
| AuthorAffiliation | 2 Division Phoniatrics and Pediatric Audiology, Department Otolaryngology, Head- and Neck-Surgery, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Bavaria, Germany 1 Department Artificial Intelligence in Biomedical Engineering (AIBE), Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Bavaria, Germany Bayer Crop Science United States: Bayer CropScience LP, UNITED STATES OF AMERICA |
| AuthorAffiliation_xml | – name: 1 Department Artificial Intelligence in Biomedical Engineering (AIBE), Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Bavaria, Germany – name: Bayer Crop Science United States: Bayer CropScience LP, UNITED STATES OF AMERICA – name: 2 Division Phoniatrics and Pediatric Audiology, Department Otolaryngology, Head- and Neck-Surgery, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Bavaria, Germany |
| Author_xml | – sequence: 1 givenname: Andreas M. orcidid: 0000-0003-3643-7776 surname: Kist fullname: Kist, Andreas M. – sequence: 2 givenname: Sina surname: Razi fullname: Razi, Sina – sequence: 3 givenname: René surname: Groh fullname: Groh, René – sequence: 4 givenname: Florian surname: Gritsch fullname: Gritsch, Florian – sequence: 5 givenname: Anne surname: Schützenberger fullname: Schützenberger, Anne |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/40608748$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1016/j.jvoice.2017.05.002 10.1109/ACCESS.2020.3012722 10.1109/TBME.2014.2364862 10.1159/000111802 10.3390/app10051556 10.1109/ICCV.2019.00140 10.1109/CVPR.2018.00474 10.1038/s41597-020-0526-3 10.1002/lary.28475 10.3390/bioengineering11050443 10.1109/TMI.2017.2665165 10.1016/j.bspc.2024.106047 10.1109/JTEHM.2023.3237859 10.1038/s41592-023-02150-0 10.1371/journal.pone.0227791 10.1044/2021_JSLHR-20-00498 10.1109/ACCESS.2023.3249759 |
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| Copyright | Copyright: © 2025 Kist et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. COPYRIGHT 2025 Public Library of Science 2025 Kist et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 2025 Kist et al 2025 Kist et al 2025 Kist et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
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| SubjectTerms | Algorithms Analysis Artificial Intelligence Biology and Life Sciences Computer and Information Sciences Datasets Deep learning Endoscopy Engineering and Technology Humans Image processing Image Processing, Computer-Assisted - methods Image quality Image segmentation Laryngoscopy Laryngoscopy - methods Larynx - diagnostic imaging Masks Medical imaging Medicine and Health Sciences Methods Neural networks Physiology Pixels Semantic segmentation Semantics Social Sciences Subject specialists Traffic signals |
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| Title | Predicting semantic segmentation quality in laryngeal endoscopy images |
| URI | https://www.ncbi.nlm.nih.gov/pubmed/40608748 https://www.proquest.com/docview/3227026468 https://www.proquest.com/docview/3227054256 https://pubmed.ncbi.nlm.nih.gov/PMC12225846 https://doaj.org/article/34fba17f233742d7b7cf3ef8735ce8a5 http://dx.doi.org/10.1371/journal.pone.0314573 |
| Volume | 20 |
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