LarynxFormer: a transformer-based framework for processing and segmenting laryngeal images
Manual diagnostic methods for assessing exercise-induced laryngeal obstruction (EILO) contain human bias and can lead to subjective decisions. Several studies have proposed machine learning methods for segmenting laryngeal structures to automate and make diagnostic outcomes more objective. Four stat...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2025-07-01
|
| Series: | Frontiers in Digital Health |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fdgth.2025.1459136/full |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849429207144202240 |
|---|---|
| author | Rune Mæstad Abdul Hanan Haakon Kristian Kvidaland Haakon Kristian Kvidaland Hege Clemm Hege Clemm Reza Arghandeh |
| author_facet | Rune Mæstad Abdul Hanan Haakon Kristian Kvidaland Haakon Kristian Kvidaland Hege Clemm Hege Clemm Reza Arghandeh |
| author_sort | Rune Mæstad |
| collection | DOAJ |
| description | Manual diagnostic methods for assessing exercise-induced laryngeal obstruction (EILO) contain human bias and can lead to subjective decisions. Several studies have proposed machine learning methods for segmenting laryngeal structures to automate and make diagnostic outcomes more objective. Four state-of-the-art models for laryngeal image segmentation are implemented, trained, and compared using our pre-processed dataset containing laryngeal images derived from continuous laryngoscopy exercise-test (CLE-test) data. These models include both convolutional-based and transformer-based methods. We propose a new framework called LarynxFormer, consisting of a pre-processing pipeline, transformer-based segmentation, and post-processing of laryngeal images. This study contributes to the investigation of using machine learning as a diagnostic tool for EILO. Furthermore, we show that a transformer-based approach for larynx segmentation outperforms conventional state-of-the-art image segmentation methods in terms of performance metrics and computational speed, demonstrating up to 2x faster inference time compared to the other methods. |
| format | Article |
| id | doaj-art-35cba6921e974cd1b38fc7935dd2db90 |
| institution | Kabale University |
| issn | 2673-253X |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Digital Health |
| spelling | doaj-art-35cba6921e974cd1b38fc7935dd2db902025-08-20T03:28:26ZengFrontiers Media S.A.Frontiers in Digital Health2673-253X2025-07-01710.3389/fdgth.2025.14591361459136LarynxFormer: a transformer-based framework for processing and segmenting laryngeal imagesRune Mæstad0Abdul Hanan1Haakon Kristian Kvidaland2Haakon Kristian Kvidaland3Hege Clemm4Hege Clemm5Reza Arghandeh6Faculty of Engineering and Science, Western Norway University of Applied Sciences, Bergen, Vestland, NorwayFaculty of Engineering and Science, Western Norway University of Applied Sciences, Bergen, Vestland, NorwayFaculty of Health and Social Sciences, Western Norway University of Applied Sciences, Bergen, Vestland, NorwayDepartment of Pediatric and Adolescent Medicine, Haukeland University Hospital, Bergen, Vestland, NorwayDepartment of Clinical Science, University of Bergen, Bergen, Vestland, NorwayDepartment of Head and Neck, Haukeland University Hospital, Bergen, Vestland, NorwayFaculty of Engineering and Science, Western Norway University of Applied Sciences, Bergen, Vestland, NorwayManual diagnostic methods for assessing exercise-induced laryngeal obstruction (EILO) contain human bias and can lead to subjective decisions. Several studies have proposed machine learning methods for segmenting laryngeal structures to automate and make diagnostic outcomes more objective. Four state-of-the-art models for laryngeal image segmentation are implemented, trained, and compared using our pre-processed dataset containing laryngeal images derived from continuous laryngoscopy exercise-test (CLE-test) data. These models include both convolutional-based and transformer-based methods. We propose a new framework called LarynxFormer, consisting of a pre-processing pipeline, transformer-based segmentation, and post-processing of laryngeal images. This study contributes to the investigation of using machine learning as a diagnostic tool for EILO. Furthermore, we show that a transformer-based approach for larynx segmentation outperforms conventional state-of-the-art image segmentation methods in terms of performance metrics and computational speed, demonstrating up to 2x faster inference time compared to the other methods.https://www.frontiersin.org/articles/10.3389/fdgth.2025.1459136/fullexercise-induced laryngeal obstructioncontinuous laryngoscopy exercise testmachine learningartificial intelligenceimage segmentation |
| spellingShingle | Rune Mæstad Abdul Hanan Haakon Kristian Kvidaland Haakon Kristian Kvidaland Hege Clemm Hege Clemm Reza Arghandeh LarynxFormer: a transformer-based framework for processing and segmenting laryngeal images Frontiers in Digital Health exercise-induced laryngeal obstruction continuous laryngoscopy exercise test machine learning artificial intelligence image segmentation |
| title | LarynxFormer: a transformer-based framework for processing and segmenting laryngeal images |
| title_full | LarynxFormer: a transformer-based framework for processing and segmenting laryngeal images |
| title_fullStr | LarynxFormer: a transformer-based framework for processing and segmenting laryngeal images |
| title_full_unstemmed | LarynxFormer: a transformer-based framework for processing and segmenting laryngeal images |
| title_short | LarynxFormer: a transformer-based framework for processing and segmenting laryngeal images |
| title_sort | larynxformer a transformer based framework for processing and segmenting laryngeal images |
| topic | exercise-induced laryngeal obstruction continuous laryngoscopy exercise test machine learning artificial intelligence image segmentation |
| url | https://www.frontiersin.org/articles/10.3389/fdgth.2025.1459136/full |
| work_keys_str_mv | AT runemæstad larynxformeratransformerbasedframeworkforprocessingandsegmentinglaryngealimages AT abdulhanan larynxformeratransformerbasedframeworkforprocessingandsegmentinglaryngealimages AT haakonkristiankvidaland larynxformeratransformerbasedframeworkforprocessingandsegmentinglaryngealimages AT haakonkristiankvidaland larynxformeratransformerbasedframeworkforprocessingandsegmentinglaryngealimages AT hegeclemm larynxformeratransformerbasedframeworkforprocessingandsegmentinglaryngealimages AT hegeclemm larynxformeratransformerbasedframeworkforprocessingandsegmentinglaryngealimages AT rezaarghandeh larynxformeratransformerbasedframeworkforprocessingandsegmentinglaryngealimages |