Volumetric vocal tract segmentation using a deep transfer learning 3D U-Net model

The investigation into the 3D airway area is the prerequisite step for quantitatively studying the anatomical structures and function of the upper airway. Segmentation of upper airway can be considered as one of the stepping stones for this investigation. In this work, we propose a transfer learning-based 3D U-Net model with a ResNet encoder for vocal tract segmentation with small datasets training. We demonstrate its utility on sustained volumetric vocal tract MR scans from the recently released French speaker database.