Conference proceeding
Airway segmentation in speech MRI using the U-net architecture
2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), Vol.2020-, pp.1887-1890
04/2020
DOI: 10.1109/ISBI45749.2020.9098536
Abstract
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 (at the input), and the manually segmented airway (at the output). We base our training on the open source University of Southern California's (USC) speech morphology MRI database consisting of speakers producing a variety of sustained vowel and consonant sounds. Once trained, our model performs fast airway segmentations on unseen images at the order of 210 ms/slice on a modern CPU with 12 cores. Using manual segmentation as a reference, we evaluate the performances of the proposed U-net airway segmentation, against existing seed-growing segmentation, and manual segmentation from a different user. We demonstrate improved DICE similarity with U-net compared to seed-growing, and minor differences in DICE similarity of U-net compared to manual segmentation from the second user.
Details
- Title: Subtitle
- Airway segmentation in speech MRI using the U-net architecture
- Creators
- Subin Erattakulangara - University of IowaSajan Goud Lingala - University of Iowa
- Resource Type
- Conference proceeding
- Publication Details
- 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), Vol.2020-, pp.1887-1890
- Publisher
- IEEE
- DOI
- 10.1109/ISBI45749.2020.9098536
- ISSN
- 1945-7928
- eISSN
- 1945-8452
- Language
- English
- Date published
- 04/2020
- Academic Unit
- Roy J. Carver Department of Biomedical Engineering; Radiology
- Record Identifier
- 9984196973902771
Metrics
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