## 5 Discussion
The latter are extracted better by ResNets and MobileNet v2 (top mouth DSC of 0.953), but their advantage is tiny in absolute differences. On the other hand, the results indicate the tongue is the most challenging to extract, and the differences between the models are more significant. The Xception model offers the highest and least dispersed results here, so we pick this backbone for our ultimate model. Moreover, of the two most effective backbones in tongue and teeth segmentation, Xception is smaller and faster than ResNet-152. However, such a tradeoff leaves the opportunity to make a selection later, at the image feature specification and extraction stage. Further research can determine whether it is better to correctly define the overall shape of the mouth or the boundaries of the inner organs (e.g., the tongue). In general, the teeth and tongue are the most difficult to detect and segment for several reasons mentioned before (teeth: multiple tiny gray objects; tongue: variable shape and covering by teeth or lips; both: illumination issues). Therefore, we are satisfied with the median segmentation Dice index exceeding 0.83 in both cases with a fully-automated method, as it reaches the accuracy level obtained in our previous study involving better-illuminated and established tongue segmentation [55], being hardly comparable to significantly different TCM approaches to the tongue [49; 50; 51; 52], and having no teeth segmentation benchmarks. Not surprisingly, the tongue requires special attention as it is not always detected correctly, particularly in small, covered, or poorly illuminated appearances. The DSC of over 0.88 in lips and over 0.94 in the mouth are rewarding too, comparable to or better than the state-of-the-art approaches trained on fine-labeled databases [44; 45; 46; 55]. Labor time requirements regarding various parts of the study were different. The manual data annotation is the most time-consuming in such approaches, although the ROI labeling for object detection brings significantly less burden than the precise delineation of articulators. That was the main reason for designing a semi-supervised learning workflow with a small portion of strong ground-truth labels for fine-tuning. The training itself took relatively much time in multi-instance level set segmentation and a standard few hours for training either of the models. The ultimate framework takes 60.5 ms to process a single frame, which enables fast, though not real-time, processing of a 30-fps stereo stream. The stereo processing rate is ca. 3.6\(\times\) the stream frame rate, so our ca. 2 min recordings can be segmented in ca. 7 min without additional implementation optimization. It safely meets asynchronous CASD requirements, and a synchronous mode is not a distant prospect. We can point out some limitations of the study. First, the analysis is sensitive to lips detection, especially if the ROI is underestimated, as the false negative area is highly unwelcome. Thus, we apply outer margins to the YOLO outcome to avoid missing parts of the mouth during segmentation. It is better to segment a too-large patch than a too-narrow one. Second, we noticed some level of sensitivity to scene illumination. Our database contains frames with various brightness and contrast levels due to external light or skin tone. Thus, it is still advisable to retrain the model later using diverse data for higher robustness. In the current study, we addressed this issue with intense data augmentation. Note also that our database is prominent in the field of CASD, where the research often relies on fewer than a dozen cases. Third, we are aware that the tongue is definitely the most challenging of all objects and leaves room for improvement. Further study on feature extraction can bring additional feedback to improve the segmentation. Nonetheless, we believe that the texture analysis of the correctly delineated mouth area can provide relevant information also produced by the tongue appearance.