Online Learning for the Hyoid Bone Tracking During Swallowing With Neck Movement Adjustment Using Semantic Segmentation

Swallowing difficulty is a major health concern of the elderly population. The gold standard examination to assess swallowing function is videofluoroscopic swallowing study (VFSS). Hyoid kinematic parameters extracted from VFSS images can be quantitative indicators of swallowing difficulty. In previous studies, its tracking failures are still not resolved when passing through the mandible. Furthermore, it is difficult to be applied in kinematic analysis because the hyoid trajectories can be susceptible to irrelevant neck movements during swallowing. The aim of this study is to develop a robust algorithm for obtaining high-accuracy trajectories of the hyoid bone during swallowing with adjustment of the neck movements. We propose a CNN-based hyoid tracking algorithm which consists of single-domain networks for hyoid tracking and an attention U-Net with conditional random fields for semantic segmentation of the hyoid bone and the cervical vertebrae. The results show that the proposed method can track the hyoid bone robustly compared to the previous methods as measured by a success plot of one-pass evaluation. In addition, the proposed semantic segmentation method achieved the highest dice coefficient for the hyoid bone and the cervical vertebrae. Finally, the obtained hyoid trajectories were evaluated by a root mean squared error, relative error of range of motion, and Pearson's correlation analysis. The proposed algorithm can provide ability to automatically analyze the hyoid motions during swallowing in clinical practice and will potentially enable physician's decision making on diagnostic and therapeutic modalities based on quantitative swallowing assessments.