## 4 Experiments and Results
### Image Segmentation  
We used pixel-wise Dice similarity coefficient (DSC) and accuracy (Acc) to assess all stages of the segmentation workflow. Using frames from subset C, we validated the segmentation performance of each of the three blocks from the blue part of Figure 4a: (1) DRLSE initial segmentation, (2) weakly-trained DeepLab v3+ model, and (3) fine-tuned DeepLabv3+. Figure 11 presents the results in all four classes, including the whole mouth. In both cases, the segmentation results consistently improve in subsequent stages except  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(t_{IoU}\) & **Lips** & **Teeth** & **Tongue** & **Overall** \\ \hline \multirow{3}{*}{0.50} & AP & 0.999 & 0.745 & 0.651 & 0.798 \\  & F1 & 0.999 & 0.794 & 0.664 & 0.798 \\ \hline \multirow{3}{*}{0.75} & AP & 0.999 & 0.748 & 0.661 & 0.803 \\  & F1 & 0.999 & 0.788 & 0.659 & 0.798 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of our object detection results using different IoU thresholds \(t_{IoU}\).  
Figure 10: Detection IoU box plots for individual classes (\(t_{IoU}=0.50\)). Each box covers the interquartile range (IQR) with a median given and indicated by a central line. Whiskers refer to \(1.5\cdot\text{IQR}\).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & **Lips** & **Teeth** & **Tongue** & **Overall** \\ \hline \multirow{3}{*}{AP} & YOLOv3 & 0.999 & 0.612 & 0.276 & 0.629 \\  & YOLOv5 & 0.999 & **0.774** & 0.532 & 0.768 \\  & YOLOv6 & 0.999 & 0.745 & **0.651** & **0.798** \\  & YOLOv7 & **1.000** & 0.744 & 0.624 & 0.789 \\ \hline \multirow{3}{*}{F1} & YOLOv3 & **0.999** & 0.645 & 0.336 & 0.641 \\  & YOLOv5 & **0.999** & 0.749 & 0.543 & 0.753 \\ \cline{1-1}  & YOLOv6 & **0.999** & 0.794 & **0.664** & 0.798 \\ \cline{1-1}  & YOLOv7 & **0.999** & **0.822** & 0.605 & **0.800** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of state-of-the-art object detectors to our YOLOv6 approach (\(t_{IoU}=0.50\)). Bold font indicates top values.  
for the tongue, where the efficiency slightly drops in weakly supervised training, and is raised significantly by fine-tuning.  
Figure 12 reports the experiments with four DeepLabv3+ backbones: ResNet-101, ResNet-152, Xception, and MobileNet v2. The top median DSC reaches 0.953 in mouth, 0.899 in lips, 0.864 in teeth, and 0.834 in tongue, with the accuracy above 0.99 in either case. These results were obtained in cases where the detection was successful (the number of true positive pixels is greater than zero). When taking misdetected cases into consideration, the median DSC does not change in lips and mouth, slightly falls in teeth (0.857), and more noticeably decreases in tongue (0.796). In general, the models trade off the efficiency in larger and smaller objects. We discuss this issue and justify our decision to choose the Xception backbone in Section 5. Three sample segmented stereo recordings are added as Supplementary Material to this paper to show the performance of our method.  
Figure 11: Image segmentation results: Dice similarity coefficient (**a**) and accuracy (**b**) in each segmentation stage. Each box covers the interquartile range (IQR) with a median given above and indicated by a central line. Whiskers refer to \(1.5\cdot\text{IQR}\).  
Figure 12: Comparison of image segmentation results: Dice similarity coefficient (**a**) and accuracy (**b**) after full semi-supervised training in various DeepLab v3+ backbone selections. Each box covers the interquartile range (IQR) with a median given above and indicated by a central line. Whiskers refer to \(1.5\cdot\text{IQR}\).