Dataset Information

The glottaltopogram: a method of analyzing high-speed images of the vocal folds.

ABSTRACT: Laryngeal high-speed videoendoscopy is a state-of-the-art technique to examine physiological vibrational patterns of the vocal folds. With sampling rates of thousands of frames per second, high-speed videoendoscopy produces a large amount of data that is difficult to analyze subjectively. In order to visualize high-speed video in a straightforward and intuitive way, many methods have been proposed to condense the three-dimensional data into a few static images that preserve characteristics of the underlying vocal fold vibratory patterns. In this paper, we propose the "glottaltopogram," which is based on principal component analysis of changes over time in the brightness of each pixel in consecutive video images. This method reveals the overall synchronization of the vibrational patterns of the vocal folds over the entire laryngeal area. Experimental results showed that this method is effective in visualizing pathological and normal vocal fold vibratory patterns.

SUBMITTER: Chen G

PROVIDER: S-EPMC4142715 | biostudies-literature | 2014 Sep

REPOSITORIES: biostudies-literature

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The glottaltopogram: a method of analyzing high-speed images of the vocal folds.

Chen Gang G Kreiman Jody J Alwan Abeer A

Computer speech & language 20140901 5

Laryngeal high-speed videoendoscopy is a state-of-the-art technique to examine physiological vibrational patterns of the vocal folds. With sampling rates of thousands of frames per second, high-speed videoendoscopy produces a large amount of data that is difficult to analyze subjectively. In order to visualize high-speed video in a straightforward and intuitive way, many methods have been proposed to condense the three-dimensional data into a few static images that preserve characteristics of th ...[more]

PMID: 25170187

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Project description:The objective investigation of the dynamic properties of vocal fold vibrations demands the recording and further quantitative analysis of laryngeal high-speed video (HSV). Quantification of the vocal fold vibration patterns requires as a first step the segmentation of the glottal area within each video frame from which the vibrating edges of the vocal folds are usually derived. Consequently, the outcome of any further vibration analysis depends on the quality of this initial segmentation process. In this work we propose for the first time a procedure to fully automatically segment not only the time-varying glottal area but also the vocal fold tissue directly from laryngeal high-speed video (HSV) using a deep Convolutional Neural Network (CNN) approach. Eighteen different Convolutional Neural Network (CNN) network configurations were trained and evaluated on totally 13,000 high-speed video (HSV) frames obtained from 56 healthy and 74 pathologic subjects. The segmentation quality of the best performing Convolutional Neural Network (CNN) model, which uses Long Short-Term Memory (LSTM) cells to take also the temporal context into account, was intensely investigated on 15 test video sequences comprising 100 consecutive images each. As performance measures the Dice Coefficient (DC) as well as the precisions of four anatomical landmark positions were used. Over all test data a mean Dice Coefficient (DC) of 0.85 was obtained for the glottis and 0.91 and 0.90 for the right and left vocal fold (VF) respectively. The grand average precision of the identified landmarks amounts 2.2 pixels and is in the same range as comparable manual expert segmentations which can be regarded as Gold Standard. The method proposed here requires no user interaction and overcomes the limitations of current semiautomatic or computational expensive approaches. Thus, it allows also for the analysis of long high-speed video (HSV)-sequences and holds the promise to facilitate the objective analysis of vocal fold vibrations in clinical routine. The here used dataset including the ground truth will be provided freely for all scientific groups to allow a quantitative benchmarking of segmentation approaches in future.

Project description:Whole-body dehydration (i.e., systemic dehydration) leads to vocal fold tissue dehydration. Furosemide, a common diuretic prescribed to treat hypertension and edema-associated conditions, induces systemic dehydration. Furosemide also causes voice changes in human speakers, making this method of systemic dehydration particularly interesting for vocal fold dehydration studies. Our objective was to obtain a comprehensive proteome of vocal folds following furosemide-induced systemic dehydration. New Zealand White rabbits were used as the animal model and randomly assigned to euhydrated (control) or furosemide-dehydrated groups. Systemic dehydration, induced by injectable furosemide, was verified by an average body weight loss of -5.5% and significant percentage changes in blood analytes in the dehydrated rabbits compared to controls. Vocal fold specimens, including mucosa and muscle, were processed for proteomic analysis using label-free quantitation LC-MS/MS. Over 1600 proteins were successfully identified across all vocal fold samples; and associated with a variety of cellular components and ubiquitous cell functions. Protein levels were compared between groups showing 32 proteins differentially regulated (p ≤ 0.05) in the dehydrated vocal folds. These are mainly involved with mitochondrial translation and metabolism. The downregulation of proteins involved in mitochondrial metabolism in the vocal folds suggests a mechanism to prevent oxidative stress associated with systemic dehydration. SIGNIFICANCE: Voice disorders affect different population demographics worldwide with one in 13 adults in the United States reporting voice problems annually. Vocal fold systemic hydration is clinically recognized for preventing and treating voice problems and depends on optimal body hydration primarily achieved by water intake. Herein, we use the rabbit as a translatable animal model, and furosemide as a translatable method of systemic dehydration, to reveal a comprehensive proteomic profile of vocal fold mucosa and muscle in response to systemic dehydration. The significant subset of proteins differentially regulated due to furosemide-induced dehydration offer novel insights into the molecular mechanisms of systemic dehydration in the vocal folds. These findings also deepen our understanding of changes to tissue biology after diuretic administration.

Dataset Information

The glottaltopogram: a method of analyzing high-speed images of the vocal folds.

Publications

The glottaltopogram: a method of analyzing high-speed images of the vocal folds.

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