A Supporting Platform for Semi-Automatic Hyoid Bone Tracking and Parameter Extraction from Videofluoroscopic Images for the Diagnosis of Dysphagia Patients

Lee, Jun Chang; Nam, Kyoung Won; Jang, Dong Pyo; Paik, Nam–Jong; Ryu, Ju Seok; Kim, In Young

doi:10.1007/s00455-016-9759-x

Cited by 16 publications

(17 citation statements)

References 35 publications

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“…Lee et al developed a software platform for acquiring the trajectory of the hyoid bone using histogram of local binary patterns and of multi-scale local binary patterns. It was applied to 10 healthy people in 19 videos and to 8 patients with swallowing difficulty in 50 videos [13]. However, these approaches have limitations in that the number of subjects was again not enough to validate the method, in addition, the recognition performance for the hyoid bone was relatively low due to the diverse shapes of the patients' hyoid bone features obtained by the descriptor.…”

Section: Related Workmentioning

confidence: 99%

Online Learning for the Hyoid Bone Tracking During Swallowing With Neck Movement Adjustment Using Semantic Segmentation

Lee

Seo

et al. 2020

IEEE Access

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Swallowing difficulty is a major health concern of the elderly population. The gold standard examination to assess swallowing function is videofluoroscopic swallowing study (VFSS). Hyoid kinematic parameters extracted from VFSS images can be quantitative indicators of swallowing difficulty. In previous studies, its tracking failures are still not resolved when passing through the mandible. Furthermore, it is difficult to be applied in kinematic analysis because the hyoid trajectories can be susceptible to irrelevant neck movements during swallowing. The aim of this study is to develop a robust algorithm for obtaining high-accuracy trajectories of the hyoid bone during swallowing with adjustment of the neck movements. We propose a CNN-based hyoid tracking algorithm which consists of single-domain networks for hyoid tracking and an attention U-Net with conditional random fields for semantic segmentation of the hyoid bone and the cervical vertebrae. The results show that the proposed method can track the hyoid bone robustly compared to the previous methods as measured by a success plot of one-pass evaluation. In addition, the proposed semantic segmentation method achieved the highest dice coefficient for the hyoid bone and the cervical vertebrae. Finally, the obtained hyoid trajectories were evaluated by a root mean squared error, relative error of range of motion, and Pearson's correlation analysis. The proposed algorithm can provide ability to automatically analyze the hyoid motions during swallowing in clinical practice and will potentially enable physician's decision making on diagnostic and therapeutic modalities based on quantitative swallowing assessments.

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Section: Related Workmentioning

confidence: 99%

Online Learning for the Hyoid Bone Tracking During Swallowing With Neck Movement Adjustment Using Semantic Segmentation

Lee

Seo

et al. 2020

IEEE Access

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“…Lee et al . developed a software platform that extracted the trajectory of the moving hyoid bone by calculating local binary patterns and multi-scale local binary patterns 19 . Kim et al .…”

Section: Introductionmentioning

confidence: 99%

Automatic hyoid bone detection in fluoroscopic images using deep learning

Zhang

Coyle

Sejdić

2018

Sci Rep

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The displacement of the hyoid bone is one of the key components evaluated in the swallow study, as its motion during swallowing is related to overall swallowing integrity. In daily research settings, experts visually detect the hyoid bone in the video frames and manually plot hyoid bone position frame by frame. This study aims to develop an automatic method to localize the location of the hyoid bone in the video sequence. To automatically detect the location of the hyoid bone in a frame, we proposed a single shot multibox detector, a deep convolutional neural network, which is employed to detect and classify the location of the hyoid bone. We also evaluated the performance of two other state-of-art detection methods for comparison. The experimental results clearly showed that the single shot multibox detector can detect the hyoid bone with an average precision of 89.14% and outperform other auto-detection algorithms. We conclude that this automatic hyoid bone tracking system is accurate enough to be widely applied as a pre-processing step for image processing in dysphagia research, as well as a promising development that may be useful in the diagnosis of dysphagia.

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“…Clinicians repeatedly examine quantitative and spatiotemporal parameters in a VFSS recorded video based on a frame-by-frame analysis 12 . Several software tools provide semi-automatic measurements of various parameters in a VFSS video 13,14 . However, to use these tools, clinicians have to mark the region of interest (ROI) in each frame.…”

mentioning

confidence: 99%

Machine learning analysis to automatically measure response time of pharyngeal swallowing reflex in videofluoroscopic swallowing study

Lee

Park

Hwang

et al. 2020

Sci Rep

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To evaluate clinical features and determine rehabilitation strategies of dysphagia, it is crucial to measure the exact response time of the pharyngeal swallowing reflex in a videofluoroscopic swallowing study (VFSS). However, measuring the response time of the pharyngeal swallowing reflex is labor-intensive and particularly for inexperienced clinicians, it can be difficult to measure the brief instance of the pharyngeal swallowing reflex by VFSS. To accurately measure the response time of the swallowing reflex, we present a novel framework, able to detect quick events. In this study, we evaluated the usefulness of machine learning analysis of a VFSS video for automatic measurement of the response time of a swallowing reflex in a pharyngeal phase. In total, 207 pharyngeal swallowing event clips, extracted from raw VFSS videos, were annotated at the starting point and end point of the pharyngeal swallowing reflex by expert clinicians as ground-truth. To evaluate the performance and generalization ability of our model, fivefold cross-validation was performed. The average success rates of detection of the class “during the swallowing reflex” for the training and validation datasets were 98.2% and 97.5%, respectively. The average difference between the predicted detection and the ground-truth at the starting point and end point of the swallowing reflex was 0.210 and 0.056 s, respectively. Therefore, the response times during pharyngeal swallowing reflex are automatically detected by our novel framework. This framework can be a clinically useful tool for estimating the absence or delayed response time of the swallowing reflex in patients with dysphagia and improving poor inter-rater reliability of evaluation of response time of pharyngeal swallowing reflex between expert and unskilled clinicians.

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A Supporting Platform for Semi-Automatic Hyoid Bone Tracking and Parameter Extraction from Videofluoroscopic Images for the Diagnosis of Dysphagia Patients

Cited by 16 publications

References 35 publications

Online Learning for the Hyoid Bone Tracking During Swallowing With Neck Movement Adjustment Using Semantic Segmentation

Online Learning for the Hyoid Bone Tracking During Swallowing With Neck Movement Adjustment Using Semantic Segmentation

Automatic hyoid bone detection in fluoroscopic images using deep learning

Machine learning analysis to automatically measure response time of pharyngeal swallowing reflex in videofluoroscopic swallowing study

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