Abstract:
Objective: At present, there are no objective techniques to quantify and describe laryngeal obstruction, and the reproducibility of subjective manual quantification metho...Show MoreMetadata
Abstract:
Objective: At present, there are no objective techniques to quantify and describe laryngeal obstruction, and the reproducibility of subjective manual quantification methods is insufficient, resulting in diagnostic inaccuracy and a poor signal-to-noise ratio in medical research. In this work, a workflow is proposed to quantify laryngeal movements from laryngoscopic videos, to facilitate the diagnosis procedure. Methods: The proposed method analyses laryngoscopic videos, and delineates glottic opening, vocal folds, and supraglottic structures, using a convolutional neural networks (CNNs) based algorithm. The segmentation is divided into two steps: A bounding box which indicates the region of interest (RoI) is found, followed by segmentation using fully convolutional networks (FCNs). The segmentation results are statistically quantified along the temporal dimension and processed using singular spectrum analysis (SSA), to extract clear objective information that can be used by the clinicians in diagnosis. Results: The segmentation was validated on 400 images from 20 videos acquired using different endoscopic systems from different patients. The results indicated significant improvements over using FCN only in terms of both processing speed (16 FPS vs. 8 FPS) and segmentation result statistics. Five clinical cases on patients have also been provided to showcase the quantitative analysis results using the proposed method. Conclusion: The proposed method guarantees a robust and fast processing of laryngoscopic videos. Measurements of glottic angles and supraglottic index showed distinctive patterns in the provided clinical cases. Significance: The proposed automated and objective method extracts important temporal laryngeal movement information, which can be used to aid laryngeal closure diagnosis.
Published in: IEEE Transactions on Biomedical Engineering ( Volume: 66, Issue: 4, April 2019)
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- IEEE Keywords
- Index Terms
- Endoscopic Video ,
- Laryngeal Closure ,
- Convolutional Neural Network ,
- Clinical Cases ,
- Processing Speed ,
- Temporal Dimension ,
- Bounding Box ,
- Segmentation Results ,
- Vocal Fold ,
- Fully Convolutional Network ,
- Singular Spectrum Analysis ,
- Time Series ,
- Signal Processing ,
- Input Image ,
- Object Detection ,
- Image Area ,
- Recurrent Neural Network ,
- Data Augmentation ,
- Singular Value Decomposition ,
- Convolutional Neural Network Model ,
- Segmentation Step ,
- Output Pixel ,
- Faster R-CNN ,
- Manual Selection ,
- Positive End-expiratory Pressure ,
- Single Shot Multibox Detector ,
- Mask R-CNN ,
- Original Time Series ,
- Manual Annotation ,
- Amount Of Movement
- Author Keywords
- MeSH Terms
Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Endoscopic Video ,
- Laryngeal Closure ,
- Convolutional Neural Network ,
- Clinical Cases ,
- Processing Speed ,
- Temporal Dimension ,
- Bounding Box ,
- Segmentation Results ,
- Vocal Fold ,
- Fully Convolutional Network ,
- Singular Spectrum Analysis ,
- Time Series ,
- Signal Processing ,
- Input Image ,
- Object Detection ,
- Image Area ,
- Recurrent Neural Network ,
- Data Augmentation ,
- Singular Value Decomposition ,
- Convolutional Neural Network Model ,
- Segmentation Step ,
- Output Pixel ,
- Faster R-CNN ,
- Manual Selection ,
- Positive End-expiratory Pressure ,
- Single Shot Multibox Detector ,
- Mask R-CNN ,
- Original Time Series ,
- Manual Annotation ,
- Amount Of Movement
- Author Keywords
- MeSH Terms