Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/91375
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dc.contributorDepartment of Biomedical Engineering-
dc.contributorDepartment of Chinese and Bilingual Studies-
dc.creatorFeng, S-
dc.creatorShea, QTK-
dc.creatorNg, KY-
dc.creatorTang, CN-
dc.creatorKwong, E-
dc.creatorZheng, Y-
dc.date.accessioned2021-11-03T06:53:07Z-
dc.date.available2021-11-03T06:53:07Z-
dc.identifier.urihttp://hdl.handle.net/10397/91375-
dc.language.isoenen_US
dc.publisherMolecular Diversity Preservation International (MDPI)en_US
dc.rights© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).en_US
dc.rightsThe following publication Feng, S.; Shea, Q.-T.-K.; Ng, K.-Y.; Tang, C.-N.; Kwong, E.; Zheng, Y. Automatic Hyoid Bone Tracking in Real-Time Ultrasound Swallowing Videos Using Deep Learning Based and Correlation Filter Based Trackers. Sensors 2021, 21, 3712 is available at https://doi.org/10.3390/s21113712en_US
dc.subjectCorrelation filtersen_US
dc.subjectDeep learningen_US
dc.subjectDysphagiaen_US
dc.subjectHyoid boneen_US
dc.subjectReal-timeen_US
dc.subjectSiamFCen_US
dc.subjectSwallowingen_US
dc.subjectTrackingen_US
dc.subjectUltrasound videosen_US
dc.titleAutomatic hyoid bone tracking in real-time ultrasound swallowing videos using deep learning based and correlation filter based trackersen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.volume21-
dc.identifier.issue11-
dc.identifier.doi10.3390/s21113712-
dcterms.abstract(1) Background: Ultrasound provides a radiation-free and portable method for assessing swallowing. Hyoid bone locations and displacements are often used as important indicators for the evaluation of swallowing disorders. However, this requires clinicians to spend a great deal of time reviewing the ultrasound images. (2) Methods: In this study, we applied tracking algorithms based on deep learning and correlation filters to detect hyoid locations in ultrasound videos collected during swallowing. Fifty videos were collected from 10 young, healthy subjects for training, evaluation, and testing of the trackers. (3) Results: The best performing deep learning algorithm, Fully-Convo-lutional Siamese Networks (SiamFC), proved to have reliable performance in getting accurate hyoid bone locations from each frame of the swallowing ultrasound videos. While having a real-time frame rate (175 fps) when running on an RTX 2060, SiamFC also achieved a precision of 98.9% at the threshold of 10 pixels (3.25 mm) and 80.5% at the threshold of 5 pixels (1.63 mm). The tracker’s root-mean-square error and average error were 3.9 pixels (1.27 mm) and 3.3 pixels (1.07 mm), re-spectively. (4) Conclusions: Our results pave the way for real-time automatic tracking of the hyoid bone in ultrasound videos for swallowing assessment.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationSensors, June 2021, v. 21, no. 11, 3712-
dcterms.isPartOfSensors-
dcterms.issued2021-06-
dc.identifier.scopus2-s2.0-85106436631-
dc.identifier.pmid34073586-
dc.identifier.eissn1424-8220-
dc.identifier.artn3712-
dc.description.validate202110 bcvc-
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumberOA_Scopus/WOSen_US
dc.description.pubStatusPublisheden_US
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