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
How to access this content:
For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.
After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.
After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.
Keywords