U-net based automatic segmentation of the vocal tract airspace in speech MRI

We develop a fully automated airway segmentation method to segment the vocal tract airway from surrounding soft tissue in speech MRI. We train a U-net architecture to learn the end to end mapping between a mid-sagittal image, and the manually segmented airway. We base our training on MRI of sustained speech sound database and dynamic speech MRI database. Once trained, our model performs fast airway segmentations on unseen images. We demonstrate the proposed U-NET based airway segmentation to provide considerably improved DICE similarity compared to existing seed-growing segmentation, and minor differences in DICE similarity compared to manual segmentation