## 4 Discussion  
Several methods for re-training purposes were discussed and applied to HSV data. In summary, the results showed that diverse training data already enables the model to deal with new modalities to a large amount. Here, this was achieved by random ROI selection and image augmentation. Although smaller than achieved by the new ROI preprocessing method, the subsequent re-training methods showed further improvements. When re-training is performed, the phenomenon of catastrophic forgetting should be kept in mind. Results showed that finetuning with dynamic knowledge distillation seems most promising for re-training with laryngeal HSV data, even outperforming re-training from scratch. Further, this re-training strategy is rather convenient, since no old data are necessary for re-training and therefore do not have to be stored. However, it is also evident that re-training with new data, being not significantly different than the existing training data, can be avoided when the first model training is already based on data with great variety. As Figure 10 shows, re-training for HSV data provided the best results when new data with significant modality changes were considered, e.g., flexible endoscopic HSV data with honeycomb patterns induced by the light fibers. Figure 10: Segmentation performance (IoU) of the baseline U-Net and the U-Net after finetuning with knowledge distillation (b = 100%) for each camera system within the BAGLS and BAGLS-RT datasets. Regarding future model adjustments, finetuning with knowledge distillation can be adapted depending on the use case. Results showed that using FWD with a static or dynamic teacher model seems to be beneficial towards old or new data due to the respective adjustment procedure for the teacher model. Therefore, depending on whether users prioritize old or new camera systems, a static or dynamic model should be selected. Secondly, the overall influence of the teacher model is controlled by the parameter \(\alpha\). For this paper, we chose a balanced value of \(\alpha\) = 0.5. However, if the performance of the model is to be increased primarily for old or new data, this value of \(\alpha\) can be decreased or increased accordingly. The segmentation models resulting from this work will be integrated in the Glottis-Analysis-Tools (GAT) [26] and OpenHSV [11] and made available for other research groups. A limitation of the study is that the used U-Net may have too few parameters (i.e., the model is too simple) to achieve further performance improvements by incorporating new training data. Hence, future work may concentrate on more complex deep learning models containing more parameters [42] that may then lead to further improvement of segmentation performance utilizing additional information in the HSV images that has not been considered by the U-Net. Additionally, deep learning approaches should be applied to the three-dimensional dynamics of the vocal folds [43], potentially enabling an improved insight on the correlation of vocal fold dynamics and acoustic voice quality. Conceptualization, M.D., T.S. and A.S.; methodology, L.A.H. and T.S. ; software, L.A.H. ; validation, M.D., M.S. and T.S. ; formal analysis, L.A.H., T.S. and M.D. ; investigation, M.D., T.S. and L.A.H. ; resources, M.E., A.M.J., M.K., Y.M., R.R.P., R.S. and A.S.; data curation, M.D., T.S. and L.A.H. ; writing--original draft preparation, M.D. and T.S. ; writing--review and editing, M.D., T.S., D.C., M.E., A.M.J., M.K., Y.M., R.R.P., R.S., M.S. and A.S.; visualization, M.D., L.A.H. and T.S. ; supervision, M.D. and T.S. ; project administration, M.D. and A.S.; funding acquisition, M.D. and A.S. All authors have read and agreed to the published version of the manuscript. This research was funded by Deutsche Forschungsgemeinschaft (DFG), grant numbers DO1247/8-2 and SCHU 3441/3-2. This work was supported in part by DFG (EC409/1-2, DO1247/12-1), the National Institutes of Health grant R01 DC015570 and R21 DC016356 from the National Institute on Deafness and Other Communication Disorders.