Open-Source Manually Annotated Vocal Tract Database for Automatic Segmentation from 3D MRI Using Deep Learning: Benchmarking 2D and 3D Convolutional and Transformer Networks
Year: 2025
Type: preprint
Abstract: Accurate segmentation of the vocal tract from magnetic resonance imaging (MRI) data is essential for various voice and speech applications. Manual segmentation is time intensive and susceptible to errors. This study aimed to evaluate the efficacy of deep learning algorithms for automatic vocal tract segmentation from 3D MRI.
Source: arXiv (Cornell University)
Authors: Erattakulangara, Subin, Kelat, Karthika, Burnham, Katie, Balbi, Rachel, Gerard, Sarah E., Meyer, David, Lingala, Sajan Goud
Institutions: University of Iowa, Shenandoah University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6948363237
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