Deep Learning for Automatic Tracking of Tongue Surface in Real-time Ultrasound Videos, Landmarks instead of Contours
Table 1: Results of comparison study for several deep learning models on the same database. Except for TongueNet, in order to calculate MSD values, tongue contours were extracted from segmented instances using post-processing method.
['[EMPTY]', 'TongueNet', 'sUNET', 'UNET', 'BowNet', 'IrisNet']
To show the performance ability of TongueNet quantitatively, we first fitted B-spline curves using the OpenCV library on the instances of TongueNet. Then, we compared the value of the mean sum of distance (MSD) Note that there are some approximation errors for the curve-fitting procedure of the TongueNet and skeletonization process for extracting tongue contours from segmentation results of other deep learning models. Although TongueNet has not trained on that database, it could predict favorable instances for video frames with different data distributions. This shows the capability of TongueNet for managing of the over-fitting. From Table. However, in terms of speed, TongueNet outperforms other deep learning models while post-processing time is not considered for them. Although TongueNet has not trained on that database, it could predict favorable instances for that novel video frames with different data distributions. This shows the capability of TongueNet for managing of the over-fitting. From Table. However, in terms of speed performance, TongueNet outperforms other deep learning models while post-processing time was not included for calculation of frame rates.