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Segmentation of tongue muscles from super-resolution magnetic resonance images.

B. Ibragimov, J. Prince, E. Murano, J. Woo, M. Stone, B. Likar, F. Pernus, und T. Vrtovec. Medical Image Analysis, 20 (1): 198-207 (2015)

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L. Ahlgrimm

Lambert Theisen University of Stuttgart

Annika Liebgott

Automated reference-free assessment of MR image quality using an active learning approach: Comparison of Support Vector Machine versus Deep Neural Network classificationS. Gatidis, A. Liebgott, M. Schwartz, P. Martirosian, F. Schick, K. Nikolaou, B. Yang, und T. Küstner. Proceedings of the International Society for Magnetic Resonance in Medicine (ISMRM), Honolulu, Hawaii, USA, (April 2017)

Automated reference-free assessment of MR image quality using an active learning approach: Comparison of Support Vector Machine versus Deep Neural Network classificationS. Gatidis, A. Liebgott, M. Schwartz, P. Martirosian, F. Schick, K. Nikolaou, B. Yang, und T. Küstner. Proceedings of the International Society for Magnetic Resonance in Medicine (ISMRM), Honolulu, Hawaii, USA, (April 2017)An easy-to-use image labeling platform for automatic Magnetic Resonance Image Quality AssessmentT. Küstner, P. Wolf, M. Schwartz, A. Liebgott, F. Schick, S. Gatidis, und B. Yang. Proceedings of the IEEE International Symposium on Biomedical Imaging (ISBI), Melbourne, Australia, (April 2017)Automated image quality assessment in whole-body MRIS. Gatidis, A. Liebgott, H. Schmidt, N. Schwenzer, P. Martirosian, K. Nikolaou, F. Schick, B. Yang, und T. Küstner. Proceedings of the Radiological Society of North America (RSNA), Chicago, USA, (November 2016)

Univ. -Prof. Dr. Remco Ingmar Leine University of Stuttgart

Christian León University of Stuttgart

Weitere Publikationen von Autoren mit dem selben Namen

Incompressible Phase Registration for Motion Estimation from Tagged Magnetic Resonance Images.F. Xing, J. Woo, A. Gomez, D. Pham, P. Bayly, M. Stone, und J. Prince. RAMBO+HVSMR@MICCAI, Volume 10129 von Lecture Notes in Computer Science, Seite 24-33. (2016)Segmentation of tongue muscles from super-resolution magnetic resonance images.B. Ibragimov, J. Prince, E. Murano, J. Woo, M. Stone, B. Likar, F. Pernus, und T. Vrtovec. Medical Image Analysis, 20 (1): 198-207 (2015)MRI analysis of 3D normal and post-glossectomy tongue motion in speech.F. Xing, E. Murano, J. Lee, J. Woo, M. Stone, und J. Prince. ISBI, Seite 816-819. IEEE, (2013)A cine MRI-based study of sibilant fricatives production in post-glossectomy speakers.X. Zhou, J. Woo, M. Stone, und C. Espy-Wilson. ICASSP, Seite 7780-7784. IEEE, (2013)Determining Functional Units of Tongue Motion via Graph-Regularized Sparse Non-negative Matrix Factorization.J. Woo, F. Xing, J. Lee, M. Stone, und J. Prince. MICCAI (2), Volume 8674 von Lecture Notes in Computer Science, Seite 146-153. Springer, (2014)Reconstructing the tongue surface from six cross-sectional contours: ultrasound data.A. Lundberg, und M. Stone. ICSLP, ISCA, (1998)Three-dimensional coarticulatory strategies of tongue movement.M. Stone, A. Lundberg, E. Davis, R. Gullapalli, und M. NessAiver. EUROSPEECH, ISCA, (1997)Tracking tongue motion in three dimensions using tagged MR image.X. Liu, M. Stone, und J. Prince. ISBI, Seite 1372-1375. IEEE, (2006)A Bayesian approach to distinguishing interdigitated tongue muscles from limited diffusion magnetic resonance imaging.C. Ye, E. Murano, M. Stone, und J. Prince. Comp. Med. Imag. and Graph., (2015)Structure and variability in human tongue muscle anatomy.M. Stone, J. Woo, J. Lee, T. Poole, A. Seagraves, M. Chung, E. Kim, E. Murano, J. Prince, und S. Blemker. CMBBE: Imaging & Visualization, 6 (5): 499-507 (2018)