Contour-based 3D tongue motion visualization using ultrasound image sequences

Xu, Kele; Yang, Yin; Leboullenger, Clémence; Roussel, Pierre; Denby, B.

doi:10.1109/icassp.2016.7472705

Cited by 7 publications

(3 citation statements)

References 23 publications

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“…In some individuals, anatomical variation in frenulum morphology may create limitation in tongue movement, such that there is an imbalance between these roles of stability and mobility. Research on task specific tongue biomechanics helps us understand how limitation of movement caused by the lingual frenulum may impact variably on different tongue activities (Jackson, ; Hiiemae et al, ; Green and Wang, ; Perrier et al, ; Geddes et al, , ; Ono et al, ; Stavness et al, ; Elad et al, ; Xu, ). Further research is required to correlate the impact of specific morphological variables of the frenulum into a clinical context.…”

Section: Summary and Discussionmentioning

confidence: 99%

What is a tongue tie? Defining the anatomy of the in‐situ lingual frenulum

et al. 2019

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Surgical release of the lingual frenulum (frenotomy) has become an increasingly common procedure, performed from birth through to adulthood. Surprisingly, detailed anatomy of the in‐situ lingual frenulum has never been described, and no anatomical basis has been proposed for the individual variability in frenulum morphology. The lingual frenulum is frequently referred to as a “cord” or “submucosal band” of connective tissue, yet there is no evidence to support this anatomical construct. This paper aims to describe the anatomy of the in‐situ lingual frenulum and its relationship to floor of mouth structures. Fresh tissue microdissection of the lingual frenulum and floor of mouth was performed on nine adult cadavers with photo‐documentation and description of findings. The lingual frenulum is a dynamic structure, formed by a midline fold in a layer of fascia that inserts around the inner arc of the mandible, forming a diaphragm‐like structure across the floor of mouth. This fascia is located immediately beneath the oral mucosa, fusing centrally with the connective tissue on the tongue's ventral surface. The sublingual glands and submandibular ducts are enveloped by the fascial layer and anterior genioglossus fibers are suspended beneath it. Lingual nerve branches are located superficially on the ventral surface of the tongue, immediately deep to the fascia. The lingual frenulum is not a discrete midline structure. It is formed by dynamic elevation of a midline fold in the floor of mouth fascia. With this study, the clinical concept of ankyloglossia and its surgical management warrant revision. Clin. Anat. 32:749–761, 2019. © 2019 The Authors. Clinical Anatomy published by Wiley Periodicals, Inc. on behalf of American Association of Clinical Anatomists.

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Section: Summary and Discussionmentioning

confidence: 99%

What is a tongue tie? Defining the anatomy of the in‐situ lingual frenulum

et al. 2019

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“…It is even harder for the case of tongue contour tracking in real-time applications (Mozaffari et al, 2018). Various methods have been utilized for the problem of automatic tongue extraction in the last recent years such as image segmentation like active contour models or snakes (Ghrenassia et al, 2014;Laporte and Ménard, 2015;Li et al, 2005;Xu et al, 2016bXu et al, , 2016c, graph-based technique (Tang and Hamarneh, 2010), machine learning-based methods (Berry and Fasel, 2011;Fabre et al, 2015;Fasel and Berry, 2010;L. et al, 2012), and many more.…”

Section: Literature Review and Related Workmentioning

confidence: 99%

BowNet: Dilated convolutional neural network for ultrasound tongue contour extraction

Mozaffari

Lee²

2019

The Journal of the Acoustical Society of America

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Ultrasound imaging is safe, relatively affordable, and capable of real-time performance. This technology has been used for real-time visualization and analyzing the functionality of human organs in many studies. One application of this technology is to visualize and to characterize human tongue shape and motion during a real-time speech to study healthy or impaired speech production. Due to the noisy nature of ultrasound images with low-contrast characteristic, it might require expertise for non-expert users to recognize organ shape such as tongue surface (dorsum). To alleviate this difficulty for quantitative analysis of tongue shape and motion, tongue surface can be extracted, tracked, and visualized instead of the whole tongue region. Delineating the tongue surface from each frame is a cumbersome, subjective, and error-prone task. Furthermore, the rapidity and complexity of tongue gestures have made it a challenging task, and manual segmentation is not a feasible solution for real-time applications. The progress of deep convolutional neural networks has been successfully exploited in various computer vision tasks such as image classification and segmentation. Several end-to-end deep learning segmentation methods provide a promising alternative for previous techniques with higher accuracy and robustness results, without any intervention. Employing the power of highspeed graphics processing unit (GPU) with state-of-the-art deep neural network models and training techniques, it is feasible to implement new fully-automatic, accurate, and robust segmentation methods with the capability of real-time performance, applicable for tracking of the tongue contours during the speech. This paper presents two novel deep neural network models named BowNet and wBowNet benefits from the ability of global prediction of decoding-encoding models, with integrated multi-scale contextual information, and capability of full-resolution (local) extraction of dilated convolutions. Experimental results using several ultrasound tongue image datasets revealed that the combination of both localization and globalization searching could improve prediction result significantly. Assessment of BowNet models using both qualitatively and quantitatively studies showed their outstanding achievements in terms of accuracy and robustness in compare with similar techniques.

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“…Ideally, such tongue models should offer a good compromise between accuracy of the generated shape and the available degrees of freedom (DoF) for manipulating it. This means that biomechanical models such as those presented by Lloyd et al [15], Xu et al [16], Wrench and Balch [17], or Yu et al [18] might be too complex for this purpose. While such models aim to simulate the underlying mechanics of the human tongue as closely as possible, and can be used to visualize existing articulatory data, they can be challenging to control efficiently.…”

Section: A Backgroundmentioning

confidence: 99%

Synthesis of Tongue Motion and Acoustics From Text Using a Multimodal Articulatory Database

Steiner

Maguer

Hewer

2017

IEEE/ACM Trans. Audio Speech Lang. Process.

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Abstract-We present an end-to-end text-to-speech (TTS) synthesis system that generates audio and synchronized tongue motion directly from text. This is achieved by adapting a 3D model of the tongue surface to an articulatory dataset and training a statistical parametric speech synthesis system directly on the tongue model parameters. We evaluate the model at every step by comparing the spatial coordinates of predicted articulatory movements against the reference data. The results indicate a global mean Euclidean distance of less than 2.8 mm, and our approach can be adapted to add an articulatory modality to conventional TTS applications without the need for extra data.

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Contour-based 3D tongue motion visualization using ultrasound image sequences

Cited by 7 publications

References 23 publications

What is a tongue tie? Defining the anatomy of the in‐situ lingual frenulum

What is a tongue tie? Defining the anatomy of the in‐situ lingual frenulum

BowNet: Dilated convolutional neural network for ultrasound tongue contour extraction

Synthesis of Tongue Motion and Acoustics From Text Using a Multimodal Articulatory Database

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