  Ultrasound imaging is safe, relatively affordable, and capable of real-time
performance. 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. Employing the power of
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 them outstanding achievements in terms of accuracy and robustness in
comparison with similar techniques.
