Towards accessible deep learning for upper-airway segmentation

The human vocal tract is an incredible instrument in our body, playing a vital role in physiological processes such as speech, singing, and breathing. The modulation of various articulators, like the tongue, velum, and lips, enables the smooth production of sound and breathing. Any disturbances in the shape or dynamics of these articulators can result in conditions such as impaired speech, cleft palate, or sleep apnea. Researchers and clinicians have increasingly utilized modalities including Electro-magnetic Articulography (EMA), Endoscopy, Projection X-ray, X-ray Computed Tomography, Ultrasound, and Magnetic Resonance Imaging (MRI). Amongst these, MRI has emerged as a powerful tool due to its non-invasive nature, flexibility in imaging orientation, ability to image deep vocal structures, and suitability for longitudinal studies. However, MRI is relatively slow compared to other modalities, potentially prolonging data acquisition. Recent advancements in accelerated MRI techniques, such as parallel imaging with custom neck coils and compressed sensing, have enabled rapid imaging of the entire vocal tract during sustained speech sounds.