Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/91375
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Title: Automatic hyoid bone tracking in real-time ultrasound swallowing videos using deep learning based and correlation filter based trackers
Authors: Feng, S 
Shea, QTK 
Ng, KY 
Tang, CN 
Kwong, E 
Zheng, Y 
Issue Date: Jun-2021
Source: Sensors, June 2021, v. 21, no. 11, 3712
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.
Keywords: Correlation filters
Deep learning
Dysphagia
Hyoid bone
Real-time
SiamFC
Swallowing
Tracking
Ultrasound videos
Publisher: Molecular Diversity Preservation International (MDPI)
Journal: Sensors 
EISSN: 1424-8220
DOI: 10.3390/s21113712
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/).
The 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/s21113712
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