Endoscopic Evaluation of the Pediatric Larynx

Patel, Rita R.; Romeo, S; Beek-King, Jessica Van; Braden, Maia N.

doi:10.1007/978-3-030-26191-7_14

Cited by 4 publications

(1 citation statement)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Clinical impact. Quantitative, symmetry axis prediction dependent parameters, have an impact on diagnosis and treatment options [47][48][49] . Especially clinically relevant phonovibrograms are dependent on the correct detection of the midline 11 .…”

Section: Definition Of Glottal Midlinementioning

confidence: 99%

Rethinking glottal midline detection

Kist

Zilker

Gómez

et al. 2020

Sci Rep

View full text Add to dashboard Cite

A healthy voice is crucial for verbal communication and hence in daily as well as professional life. The basis for a healthy voice are the sound producing vocal folds in the larynx. A hallmark of healthy vocal fold oscillation is the symmetric motion of the left and right vocal fold. Clinically, videoendoscopy is applied to assess the symmetry of the oscillation and evaluated subjectively. High-speed videoendoscopy, an emerging method that allows quantification of the vocal fold oscillation, is more commonly employed in research due to the amount of data and the complex, semi-automatic analysis. In this study, we provide a comprehensive evaluation of methods that detect fully automatically the glottal midline. We used a biophysical model to simulate different vocal fold oscillations, extended the openly available BAGLS dataset using manual annotations, utilized both, simulations and annotated endoscopic images, to train deep neural networks at different stages of the analysis workflow, and compared these to established computer vision algorithms. We found that classical computer vision perform well on detecting the glottal midline in glottis segmentation data, but are outperformed by deep neural networks on this task. We further suggest GlottisNet, a multi-task neural architecture featuring the simultaneous prediction of both, the opening between the vocal folds and the symmetry axis, leading to a huge step forward towards clinical applicability of quantitative, deep learning-assisted laryngeal endoscopy, by fully automating segmentation and midline detection.

show abstract

Section: Definition Of Glottal Midlinementioning

confidence: 99%

Rethinking glottal midline detection

Kist

Zilker

Gómez

et al. 2020

Sci Rep

View full text Add to dashboard Cite

show abstract

Multimodal Analysis of Dysphonia in Smokers: A Two Year Comprehensive Study

Raghuwanshi

Mundra

Dubey

et al. 2021

Indian J Otolaryngol Head Neck Surg

View full text Add to dashboard Cite

Rethinking glottal midline detection

Kist

Zilker

Gómez

et al. 2020

Preprint

View full text Add to dashboard Cite

A healthy voice is crucial for verbal communication and hence in daily as well as professional life. The basis for a healthy voice are the sound producing vocal folds in the larynx. A hallmark of healthy vocal fold oscillation is the symmetric motion of the left and right vocal fold. Clinically, videoendoscopy is applied to assess the symmetry of the oscillation and evaluated subjectively. High-speed videoendoscopy, an emerging method that allows quantification of the vocal fold oscillation, is more commonly employed in research due to the amount of data and the complex, semi-automatic analysis. In this study, we provide a comprehensive evaluation of methods that detect fully automatically the glottal midline. We use a biophysical model to simulate different vocal fold oscillations, extended the openly available BAGLS dataset using manual annotations, utilized both, simulations and annotated endoscopic images, to train deep neural networks at different stages of the analysis workflow, and compared these to established computer vision algorithms. We found that classical computer vision perform well on detecting the glottal midline in glottis segmentation data, but are outperformed by deep neural networks on this task. We further suggest GlottisNet, a multi-task neural architecture featuring the simultaneous prediction of both, the opening between the vocal folds and the symmetry axis, leading to a huge step forward towards clinical applicability of quantitative, deep learning-assisted laryngeal endoscopy, by fully automating segmentation and midline detection.

show abstract

Endoscopic Evaluation of the Pediatric Larynx

Cited by 4 publications

References 36 publications

Rethinking glottal midline detection

Rethinking glottal midline detection

Multimodal Analysis of Dysphonia in Smokers: A Two Year Comprehensive Study

Rethinking glottal midline detection

Contact Info

Product

Resources

About