Automatic Tongue Delineation from MRI Images with a Convolutional Neural Network Approach

References

• Berger, M.-O., and Y. Laprie. 1996. “Tracking articulators in X-ray images with minimal user interaction: Example of the tongue extraction.” In Proceedings of IEEE International Conference on Image Processing, Lausanne, Switzerland.
   Google Scholar
• Chollet, F. 2015. “Keras.”
   Google Scholar
• Douros, I., J. Felblinger, J. Frahm, K. Isaieva, A. Joseph, Y. Laprie, F. Odille, A. Tsukanova, D. Voit, and P.-A. Vuissoz. 2019. A multimodal real-time MRI articulatory corpus of french for speech research. In Proceedings of Interspeech 2019, 1556-1560. Graz, Austria.
   Google Scholar
• Eslami, M., C. Neuschaefer-Rube, and A. Serrurier. 2019. Automatic vocal tract segmentation based on conditional generative adversarial neural network. In Studientexte Zur Sprachkommunikation: Elektronische Sprachsignalverarbeitung 2019, 263–70. Dresden, Germany. http://www.essv.de/paper.php?id=90
   Google Scholar
• Example of the Automatic Tongue Delineation for S1.
   Google Scholar
• Example of the Automatic Tongue Delineation for S2.
   Google Scholar
• Frahm, J., S. Schätz, M. Untenberger, S. Zhang, K. Dirk Voit, J. M. Dietmar Merboldt, J. L. Sohns, and M. Uecker. 2014. On the temporal fidelity of nonlinear inverse reconstructions for real-time MRI – the motion challenge. Open Medical Imaging Journal 8 (1):1–7. doi:10.2174/1874347101408010001.
   Google Scholar
• Jallon, J. F., and F. Berthommier. 2009. A semi-automatic method for extracting vocal-tract movements from X-ray films. Speech Communication 51 (2):97–115. doi:10.1016/j.specom.2008.06.005.
   Web of Science ®Google Scholar
• Jaumard-Hakoun, A., X. Kele, P. Roussel, G. Dreyfus, M. Stone, and B. Denby. 2015. Tongue contour extraction from ultrasound images based on deep neulral network. In International congress of phonetic sciences. United Kingdom: Glasgow. https://arxiv.org/abs/1605.05912
   Google Scholar
• Kaburagi, T., K. Wakamiya, and M. Honda. 2005. Three-dimensional electromagnetic articulography: A measurement principle. The Journal of the Acoustical Society of America 118 (1):428–43. doi:10.1121/1.1928707.
   PubMed Web of Science ®Google Scholar
• Labrunie, M., P. Badin, D. Voit, A. A. Joseph, J. Frahm, L. Lamalle, C. Vilain, and L.-J. Boe. 2018. Automatic segmentation of speech articulators from real-time Midsagittal MRI based on supervised learning. Speech Communication 99 Elsevier:27–46. doi:10.1016/j.specom.2018.02.004.
   Web of Science ®Google Scholar
• Laprie, Y., M. Loosvelt, S. Maeda, E. Sock, and F. Hirsch. 2013. “Articulatory copy synthesis from cine X-ray films.” In Interspeech 2013 (14th Annual Conference of the International Speech Communication Association). Lyon, France.
   Google Scholar
• Lee, S., E. Bresch, J. Adams, A. Kazemzadeh, and S. Narayanan. 2006. “A study of emotional speech articulation using a fast magnetic resonance imaging technique.” In Ninth International Conference on Spoken Language Processing, Pittsburgh, PA, USA.
   Google Scholar
• Li, M., C. Kambhamettu, and M. Stone. 2005. Automatic contour tracking in ultrasound images. Clinical Linguistics & Phonetics 19 (6–7):545–54. Taylor & Francis. doi:10.1080/02699200500113616.
   PubMed Web of Science ®Google Scholar
• Niebergall, A., S. Zhang, E. Kunay, G. Keydana, M. Job, M. Uecker, and J. Frahm. 2013. Real-time MRI of speaking at a resolution of 33 Ms: Undersampled radial FLASH with nonlinear inverse reconstruction. Magnetic Resonance in Medicine 69 (2):477–85. Wiley Online Library. doi:10.1002/mrm.24276.
   PubMed Web of Science ®Google Scholar
• Ronneberger, O., P. Fischer, and T. Brox. 2015. U-Net: Convolutional networks for biomedical image segmentation. In Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015, ed. N. Navab, J. Hornegger, W. M. Wells, and A. F. Frangi, 234–41. Cham: Springer International Publishing.
   Google Scholar
• Somandepalli, K., A. Toutios, and S. S. Narayanan. 2017. Semantic edge detection for tracking vocal tract air-tissue boundaries in real-time magnetic resonance images. Interspeech, Stockholm, Sweden, 631–35.
   Google Scholar
• Thimm, G. 1999. Tracking articulators in X-ray movies of the vocal tract. In Computer Analysis of Images and Patterns, ed. F. Solina and A. Leonardis, 126–33. Berlin, Heidelberg: Springer Berlin Heidelberg.
   Google Scholar
• Uecker, M., S. Zhang, D. Voit, A. Karaus, K.-D. Merboldt, and J. Frahm. 2010. Real-time MRI at a resolution of 20 Ms. NMR in Biomedicine 23 (8):986–94. Wiley Online Library. doi:10.1002/nbm.1585.
   PubMed Web of Science ®Google Scholar
• Valliappan, C. A., R. Mannem, and P. K. Ghosh. 2018. Air-tissue boundary segmentation in real-time magnetic resonance imaging video using semantic segmentation with fully convolutional networks. Interspeech, Hyderabad, India, 3132–36.
   Google Scholar
• Xie, S., and T. Zhuowen 2015. “Holistically-nested edge detection.” In Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chili, 1395–403.
   Google Scholar
• Xu, K., Y. Yang, M. Stone, A. Jaumard-Hakoun, C. Leboullenger, G. Dreyfus, P. Roussel, and B. Denby. 2016. Robust contour tracking in ultrasound tongue image sequences. Clinical Linguistics & Phonetics 30 (3–5):313–27. Taylor & Francis. doi:10.3109/02699206.2015.1110714.
   PubMed Web of Science ®Google Scholar
• Zhang, T. Y., and C. Y. Suen. 1984. A fast parallel algorithm for thinning digital patterns. Communications of the ACM 27 (3):236–39. ACM New York, NY, USA. doi:10.1145/357994.358023.
   Web of Science ®Google Scholar
• Zhu, J., W. Styler, and I. C. Calloway. 2018. Automatic tongue contour extraction in ultrasound images with convolutional neural networks. The Journal of the Acoustical Society of America 143 (3):1966. Acoustical Society of America. doi:10.1121/1.5036466.
   Google Scholar