Automatic hyoid bone detection in fluoroscopic images using deep learning
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Summary
This study proposes a single shot multibox detector, a deep convolutional neural network, which is employed to detect and classify the location of the hyoid bone in a frame, and shows that it can outperform other auto-detection algorithms.
- Type
- article
- Published
- 2018-08-17
- Cited by
- 63
- References
- 49
- Access
- Open access
- OpenAlex
- https://openalex.org/W30120314
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:52033837
Keywords
Political science, Humanities, Philosophy
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Cited by
- Automatic Detection of the Pharyngeal Phase in Raw Videos for the Videofluoroscopic Swallowing Study Using Efficient Data Collection and 3D Convolutional Networks
- A Deep Learning Approach to Detect Hyoid Bone in Ultrasound Exam
- Automated Segmentation of Cervical Intervertebral Disks from Videofluorography Using a Convolutional Neural Network and its Performance Evaluation
- Segmentation of cervical intervertebral disks in videofluorography by CNN, multi-channelization and feature selection
- Tracking Hyoid Bone Displacement During Swallowing Without Videofluoroscopy Using Machine Learning of Vibratory Signals
- High-Resolution Cervical Auscultation and Data Science: New Tools to Address an Old Problem.
- Online Learning for the Hyoid Bone Tracking During Swallowing With Neck Movement Adjustment Using Semantic Segmentation
- A preliminary investigation of whether HRCA signals can differentiate between swallows from healthy people and swallows from people with neurodegenerative diseases
- Machine learning analysis to automatically measure response time of pharyngeal swallowing reflex in videofluoroscopic swallowing study
- Automatic Detection of Airway Invasion from Videofluoroscopy via Deep Learning Technology
- How closely do machine ratings of duration of UES opening during videofluoroscopy approximate clinician ratings using temporal kinematic analyses and the MBSImP?
- Estimation of laryngeal closure duration during swallowing without invasive X-rays
- Variations in Hyoid Kinematics Across Liquid Consistencies in Healthy Swallowing
- Hyoid kinematic features for poor swallowing prognosis in patients with post-stroke dysphagia
- Automatic Pharyngeal Phase Recognition in Untrimmed Videofluoroscopic Swallowing Study Using Transfer Learning with Deep Convolutional Neural Networks
- Automatic Hyoid Bone Tracking in Real-Time Ultrasound Swallowing Videos Using Deep Learning Based and Correlation Filter Based Trackers
- Vision-Based Crack Detection of Asphalt Pavement Using Deep Convolutional Neural Network
- Hyoid Bone Tracking in a Videofluoroscopic Swallowing Study Using a Deep-Learning-Based Segmentation Network
- Deep Learning Analysis to Automatically Detect the Presence of Penetration or Aspiration in Videofluoroscopic Swallowing Study
- A novel dysphagia screening method using panoramic radiography
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