Ultrasound Tongue Contour Extraction using Dilated Convolutional Neural Network One application of medical ultrasound imaging is to visualize and characterize human tongue shape and motion to study healthy or impaired speech production. Due to the lowcontrast characteristic and noisy nature of ultrasound images, it requires knowledge about the tongue structure and ultrasound data interpretation for users to recognize tongue gestures. Moreover, quantitative analysis of tongue motion needs the tongue contour to be extracted, tracked and visualized automatically. This paper presents two novel deep neural networks that benefit from the ability of global prediction of encodingdecoding fully convolutional networks and the capability of fullresolution extraction of dilated convolutions. Assessment studies over datasets from different ultrasound machines disclosed the outstanding performances of the proposed models in terms of accuracy and robustness.

Automatic Tongue Image Segmentation For RealTime Remote Diagnosis Tongue diagnosis, one of the essential diagnostic methods of Traditional Chinese Medicine (TCM), is considered an ideal candidate for remote diagnosis methods because of its convenience and noninvasiveness. However, the tradeoff between accuracy and efficiency and the variation of tongue images pose great challenges in realtime tongue image segmentation. To remedy these problems, in this paper, a light weight architecture based on the encoderdecoder structure is proposed. The tongue image feature extraction (TIFE) module is designed to generate features with larger receptive fields without sacrificing spatial resolution. The context module is used to increase the performance by aggregating multiscale contextual information. The decoder is designed as a simple yet efficient feature upsampling module to fuse different depth features and refine the segmentation results along tongue boundaries. The loss module is proposed to deal with misclassifications causing by class imbalance. A new tongue image dataset (FDUSHUTCM) is constructed for model training and testing, which contains 5,600 tongue images and their corresponding high quality masks. We demonstrate the effectiveness of the proposed model on BioHit, PolyUHIT, and our datasets, achieving the performance of 99.15%, 95.69%, and 99.03% IoU accuracy, respectively. Segmentation of a  513times 513  image takes 165 ms on CPU.