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Towards Accessible Deep Learning for...

Erattakulangara, Subin,

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Towards Accessible Deep Learning for Upper-Airway Segmentation: Open-Source Tool Development and Application in Voice and Sleep Apnea Studies /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Towards Accessible Deep Learning for Upper-Airway Segmentation: Open-Source Tool Development and Application in Voice and Sleep Apnea Studies // Subin Erattakulangara.
作者:	Erattakulangara, Subin,
面頁冊數:	1 electronic resource (128 pages)
附註:	Source: Dissertations Abstracts International, Volume: 86-12, Section: B.
Contained By:	Dissertations Abstracts International86-12B.
標題:	Biomedical engineering. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31993768
ISBN:	9798286433803

Towards Accessible Deep Learning for Upper-Airway Segmentation: Open-Source Tool Development and Application in Voice and Sleep Apnea Studies /
Erattakulangara, Subin,

Towards Accessible Deep Learning for Upper-Airway Segmentation: Open-Source Tool Development and Application in Voice and Sleep Apnea Studies /Subin Erattakulangara. - 1 electronic resource (128 pages)

Source: Dissertations Abstracts International, Volume: 86-12, Section: B.

The human vocal tract is an incredible instrument in our body, playing a vital role in physiological processes such as speech, singing, and breathing. The modulation of various articulators, like the tongue, velum, and lips, enables the smooth production of sound and breathing. Any disturbances in the shape or dynamics of these articulators can result in conditions such as impaired speech, cleft palate, or sleep apnea. Researchers and clinicians have increasingly utilized modalities including Electro-magnetic Articulography (EMA), Endoscopy, Projection X-ray, X-ray Computed Tomography, Ultrasound, and Magnetic Resonance Imaging (MRI). Amongst these, MRI has emerged as a powerful tool due to its non-invasive nature, flexibility in imaging orientation, ability to image deep vocal structures, and suitability for longitudinal studies. However, MRI is relatively slow compared to other modalities, potentially prolonging data acquisition. Recent advancements in accelerated MRI techniques, such as parallel imaging with custom neck coils and compressed sensing, have enabled rapid imaging of the entire vocal tract during sustained speech sounds.Quantitative assessment of vocal tract posture modulation, such as through 3D vocal tract area functions and 2D mid-sagittal vocal-tract area change analysis, has informed numerous speech applications. These include understanding speaker-to-speaker variability, developing and optimizing vocal tract models for voice synthesis, and conducting acoustic and aerodynamic studies of 3D-printed vocal tracts derived from MRI data and research on sleep apnea. Accurate segmentation of the vocal tract from MRI data is crucial for these analyses. Typically, they are segmented via manual annotation. However, this method is time and labor-intensive as well as error prone. Accurate segmentations in 3D can take up to 90 minutes of manual editing for 64 slices, and studies of manually segmented vocal tracts typically have small sample sizes and weak statistical power. In addition to that lack of open-source datasets in this domain has slowed down the development of Machine learning based segmentation methods.This thesis addresses these challenges by presenting various segmentation methods for multimodal MRI data that are both data-efficient and robust to different MRI protocols. It also involves creating open-source datasets with expert segmentations for 2D and 3D MRI scans. The neural networks developed in this work are specifically modified to overcome the scarcity of large datasets in the domain of upper airway MRI. The immediate applications of these studies include understanding changes in the dynamics of the upper airway during speech and in sleep apnea.

English

ISBN: 9798286433803Subjects--Topical Terms:

535387
Biomedical engineering.
Subjects--Index Terms:

Deep learning

Towards Accessible Deep Learning for Upper-Airway Segmentation: Open-Source Tool Development and Application in Voice and Sleep Apnea Studies /
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520 $a The human vocal tract is an incredible instrument in our body, playing a vital role in physiological processes such as speech, singing, and breathing. The modulation of various articulators, like the tongue, velum, and lips, enables the smooth production of sound and breathing. Any disturbances in the shape or dynamics of these articulators can result in conditions such as impaired speech, cleft palate, or sleep apnea. Researchers and clinicians have increasingly utilized modalities including Electro-magnetic Articulography (EMA), Endoscopy, Projection X-ray, X-ray Computed Tomography, Ultrasound, and Magnetic Resonance Imaging (MRI). Amongst these, MRI has emerged as a powerful tool due to its non-invasive nature, flexibility in imaging orientation, ability to image deep vocal structures, and suitability for longitudinal studies. However, MRI is relatively slow compared to other modalities, potentially prolonging data acquisition. Recent advancements in accelerated MRI techniques, such as parallel imaging with custom neck coils and compressed sensing, have enabled rapid imaging of the entire vocal tract during sustained speech sounds.Quantitative assessment of vocal tract posture modulation, such as through 3D vocal tract area functions and 2D mid-sagittal vocal-tract area change analysis, has informed numerous speech applications. These include understanding speaker-to-speaker variability, developing and optimizing vocal tract models for voice synthesis, and conducting acoustic and aerodynamic studies of 3D-printed vocal tracts derived from MRI data and research on sleep apnea. Accurate segmentation of the vocal tract from MRI data is crucial for these analyses. Typically, they are segmented via manual annotation. However, this method is time and labor-intensive as well as error prone. Accurate segmentations in 3D can take up to 90 minutes of manual editing for 64 slices, and studies of manually segmented vocal tracts typically have small sample sizes and weak statistical power. In addition to that lack of open-source datasets in this domain has slowed down the development of Machine learning based segmentation methods.This thesis addresses these challenges by presenting various segmentation methods for multimodal MRI data that are both data-efficient and robust to different MRI protocols. It also involves creating open-source datasets with expert segmentations for 2D and 3D MRI scans. The neural networks developed in this work are specifically modified to overcome the scarcity of large datasets in the domain of upper airway MRI. The immediate applications of these studies include understanding changes in the dynamics of the upper airway during speech and in sleep apnea.
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