D. Angelova and L. Mihaylova, Contour segmentation in 2D ultrasound medical images with particle filtering, Machine Vision and Applications, pp.551-561, 2010.
DOI : 10.1007/s00138-010-0261-4

S. Baker and I. Matthews, Lucas-Kanade 20 Years On: A Unifying Framework, International Journal of Computer Vision, vol.56, issue.3, pp.221-255, 2004.
DOI : 10.1023/B:VISI.0000011205.11775.fd

J. Banerjee, C. Klink, E. D. Peters, W. J. Niessen, A. Moelker et al., Fast and robust 3D ultrasound registration ??? Block and game theoretic matching, Medical Image Analysis, vol.20, issue.1, pp.173-183, 2015.
DOI : 10.1016/j.media.2014.11.004

D. Barbosa, D. Friboulet, J. Dhooge, and O. Bernard, Fast tracking of the left ventricle using global anatomical affine optical flow and local recursive block matching, Proc. of the MICCAI Challenge on Endocardial Threedimensional Ultrasound Segmentation-CETUS, pp.17-24, 2014.

A. Basarab, H. Liebgott, F. Morestin, A. Lyshchik, T. Higashi et al., A method for vector displacement estimation with ultrasound imaging and its application for thyroid nodular disease, Medical Image Analysis, vol.12, issue.3, pp.259-274, 2008.
DOI : 10.1016/j.media.2007.10.007

M. Baumann, P. Mozer, V. Daanen, and J. Troccaz, Prostate biopsy tracking with deformation estimation, Medical Image Analysis, vol.16, issue.3, pp.562-576, 2012.
DOI : 10.1016/j.media.2011.01.008
URL : https://hal.archives-ouvertes.fr/hal-00606407

M. A. Bell, B. C. Byram, E. J. Harris, P. M. Evans, and J. C. Bamber, liver tracking with a high volume rate 4D ultrasound scanner and a 2D matrix array probe, Physics in Medicine and Biology, vol.57, issue.5, pp.1359-1374, 2012.
DOI : 10.1088/0031-9155/57/5/1359

C. S. Berge, A. Kapoor, and N. Navab, Orientation-Driven Ultrasound Compounding Using Uncertainty Information, Proc. of International Conference on Information Processing in Computer-Assisted Interventions, pp.236-245, 2014.
DOI : 10.1007/978-3-319-07521-1_25

R. F. Chang, W. J. Wu, W. K. Moon, and D. R. Chen, Automatic ultrasound segmentation and morphology based diagnosis of solid breast tumors, Breast Cancer Research and Treatment, vol.226, issue.2, pp.179-185, 2005.
DOI : 10.1007/s10549-004-2043-z

P. Chatelain, A. Krupa, and N. Navab, Optimization of ultrasound image quality via visual servoing, 2015 IEEE International Conference on Robotics and Automation (ICRA), pp.5997-6002, 2015.
DOI : 10.1109/ICRA.2015.7140040
URL : https://hal.archives-ouvertes.fr/hal-01121222

B. Cohen and I. H. Dinstein, New maximum likelihood motion estimation schemes for noisy ultrasound images, Pattern Recognition, vol.35, issue.2, pp.455-463, 2002.
DOI : 10.1016/S0031-3203(01)00053-X

D. Luca, V. Benz, T. Kondo, S. Knig, L. Lbke et al., The 2014 liver ultrasound tracking benchmark, Physics in Medicine and Biology, vol.60, issue.14, pp.5571-5599, 2015.
DOI : 10.1088/0031-9155/60/14/5571

D. Luca, V. Tschannen, M. Szkely, G. Tanner, and C. , A learning-based approach for fast and robust vessel tracking in long ultrasound sequences, Proc. of International Conference on Medical Image Computing and Computer-Assisted Intervention, pp.518-525, 2013.

A. Elen, . Hon-fai-choi, D. Loeckx, H. Gao, C. et al., Three-Dimensional Cardiac Strain Estimation Using Spatio???Temporal Elastic Registration of Ultrasound Images: A Feasibility Study, IEEE Transactions on Medical Imaging, vol.27, issue.11, pp.1580-1591, 2008.
DOI : 10.1109/TMI.2008.2004420

P. Hellier, P. Coup, X. Morandi, and D. L. Collins, An automatic geometrical and statistical method to detect acoustic shadows in intraoperative ultrasound brain images, Medical Image Analysis, vol.14, issue.2, pp.195-204, 2010.
DOI : 10.1016/j.media.2009.10.007
URL : https://hal.archives-ouvertes.fr/inria-00432724

B. Heyde, P. Claus, R. Jasaityte, D. Barbosa, S. Bouchez et al., Motion and deformation estimation of cardiac ultrasound sequences using an anatomical B-spline transformation model, 2012 9th IEEE International Symposium on Biomedical Imaging (ISBI), pp.266-269, 2012.
DOI : 10.1109/ISBI.2012.6235535

H. Higgins and D. L. Berger, RFA for Liver Tumors: Does It Really Work? The Oncologist 11, pp.801-808, 2006.

A. Karamalis, W. Wein, T. Klein, and N. Navab, Ultrasound confidence maps using random walks, Medical Image Analysis, vol.16, issue.6, pp.1101-1112, 2012.
DOI : 10.1016/j.media.2012.07.005

M. J. Ledesma-carbayo, J. Kybic, M. Desco, A. Santos, and M. Unser, Cardiac Motion Analysis from Ultrasound Sequences Using Non-rigid Registration, Proc. of International Conference on Medical Image Computing and Computer-Assisted Intervention, pp.889-896, 2001.
DOI : 10.1007/3-540-45468-3_106

M. Lediju, B. C. Byram, E. J. Harris, P. M. Evans, and J. C. Bamber, others, 2010. 3d Liver tracking using a matrix array: Implications for ultrasonic guidance of IMRT, Proc. of IEEE Ultrasonics Symp, pp.1628-1631

D. Lee and A. Krupa, Intensity-based visual servoing for non-rigid motion compensation of soft tissue structures due to physiological motion using 4D ultrasound, 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp.2831-2836, 2011.
DOI : 10.1109/IROS.2011.6094953
URL : https://hal.archives-ouvertes.fr/hal-00639687

M. Loosvelt, P. F. Villard, and M. O. Berger, Using a Biomechanical Model for Tongue Tracking in Ultrasound Images, Proc. of IEEE Symp. on Biomedical Simulation, pp.67-75, 2014.
DOI : 10.1007/978-3-319-12057-7_8
URL : https://hal.archives-ouvertes.fr/hal-01057861

D. Lubke and C. Grozea, High Performance Online Motion Tracking in Abdominal Ultrasound Imaging, Proc. of MICCAI Workshop on Challenge on Liver Ultrasound Tracking, p.29, 2014.

B. Marami, S. Sirouspour, A. Fenster, W. Capson, and D. , Dynamic Tracking of a Deformable Tissue Based on 3d-2d MR-US Image Registration, Proc. of SPIE Medical Imaging, 2014.

M. A. Masum, M. Pickering, A. Lambert, J. Scarvell, and P. Smith, Accuracy assessment of Tri-plane B-mode ultrasound for non-invasive 3D kinematic analysis of knee joints, BioMedical Engineering OnLine, vol.13, issue.1, 2014.
DOI : 10.1016/j.sigpro.2009.04.042

C. Metz, S. Klein, M. Schaap, T. Van-walsum, and W. Niessen, Nonrigid registration of dynamic medical imaging data using nD+t B-splines and a groupwise optimization approach, Medical Image Analysis, vol.15, issue.2, pp.238-249, 2011.
DOI : 10.1016/j.media.2010.10.003

I. Mikic, S. Krucinski, and J. D. Thomas, Segmentation and tracking in echocardiographic sequences: active contours guided by optical flow estimates, IEEE Transactions on Medical Imaging, vol.17, issue.2, pp.274-284, 1998.
DOI : 10.1109/42.700739

R. Mukherjee, C. Sprouse, T. Abraham, B. Hoffmann, E. Mcveigh et al., Myocardial motion computation in 4D ultrasound, 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp.1070-1073, 2011.
DOI : 10.1109/ISBI.2011.5872586

X. Papademetris, A. Sinusas, D. Dione, R. Constable, and J. Duncan, Estimation of 3-D left ventricular deformation from medical images using biomechanical models, IEEE Transactions on Medical Imaging, vol.21, issue.7, pp.786-800, 2002.
DOI : 10.1109/TMI.2002.801163

X. Pennec, P. Cachier, and N. Ayache, Tracking brain deformations in time-sequences of 3d US images, Proc. of International Conference on Information Processing in Medical Imaging, pp.169-175, 2001.
URL : https://hal.archives-ouvertes.fr/inria-00615022

G. Penney, J. Blackall, M. Hamady, T. Sabharwal, A. Adam et al., Registration of freehand 3D ultrasound and magnetic resonance liver images, Medical Image Analysis, vol.8, issue.1, pp.81-91, 2004.
DOI : 10.1016/j.media.2003.07.003

M. Pernot, M. Tanter, and M. Fink, 3-D real-time motion correction in high-intensity focused ultrasound therapy, Ultrasound in Medicine & Biology, vol.30, issue.9, pp.1239-1249, 2004.
DOI : 10.1016/j.ultrasmedbio.2004.07.021
URL : https://hal.archives-ouvertes.fr/inserm-00468901

M. R. Pickering, A. Muhit, J. M. Scarvell, and P. N. Smith, A new multi-modal similarity measure for fast gradient-based 2D-3D image registration, 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp.5821-5824, 2009.
DOI : 10.1109/IEMBS.2009.5335172

F. Preiswerk, V. De-luca, P. Arnold, Z. Celicanin, L. Petrusca et al., Model-guided respiratory organ motion prediction of the liver from 2D ultrasound, Medical Image Analysis, vol.18, issue.5, pp.740-751, 2014.
DOI : 10.1016/j.media.2014.03.006

R. Richa, M. Souza, G. Scandaroli, E. Comunello, and A. Von-wangenheim, Direct visual tracking under extreme illumination variations using the sum of conditional variance, 2014 IEEE International Conference on Image Processing (ICIP), pp.373-377, 2014.
DOI : 10.1109/ICIP.2014.7025074

R. Richa, R. Sznitman, R. Taylor, and G. Hager, Visual tracking using the sum of conditional variance, 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp.2953-2958, 2011.
DOI : 10.1109/IROS.2011.6094650

S. Rothlubbers, J. Schwaab, J. Jenne, and M. Gunther, MICCAI CLUST 2014-Bayesian Real-Time Liver Feature Ultrasound Tracking, Proc. of MICCAI Workshop on Challenge on Liver Ultrasound Tracking, p.45, 2014.

L. Royer, M. Marchal, L. Bras, A. Dardenne, G. Krupa et al., Real-time Tracking of Deformable Target in 3d, Proc. of IEEE International Conference on Robotics and Automation, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01122026

R. J. Schneider, D. P. Perrin, N. V. Vasilyev, G. R. Marx, P. J. Del-nido et al., Real-time image-based rigid registration of three-dimensional ultrasound, Medical Image Analysis, vol.16, issue.2, pp.402-414
DOI : 10.1016/j.media.2011.10.004

W. Schroeder, K. W. Martin, and B. Lorensen, The Visualization Toolkit, 2002.
DOI : 10.1016/B978-012387582-2/50032-0

R. Shekhar and V. Zagrodsky, Mutual information-based rigid and nonrigid registration of ultrasound volumes, IEEE Transactions on Medical Imaging, vol.21, issue.1, pp.9-22, 2002.
DOI : 10.1109/42.981230

H. Si, TetGen, a Delaunay-Based Quality Tetrahedral Mesh Generator, ACM Transactions on Mathematical Software, vol.41, issue.2, 2015.
DOI : 10.1145/2629697

O. Somphone, S. Allaire, B. Mory, and C. Dufour, Live Feature Tracking in Ultrasound Liver Sequences with Sparse Demons, Proc. of MICCAI Workshop on Challenge on Liver Ultrasound Tracking, p.53, 2014.

B. Touil, A. Basarab, P. Delachartre, O. Bernard, and D. Friboulet, Analysis of motion tracking in echocardiographic image sequences: Influence of system geometry and point-spread function, Ultrasonics, vol.50, issue.3, pp.373-386, 2010.
DOI : 10.1016/j.ultras.2009.09.001

F. Veronesi, C. Corsi, E. G. Caiani, and C. Lamberti, Nearly automated left ventricular long axis tracking on real time three-dimensional echocardiographic data, Computers in Cardiology, 2005, pp.5-8, 2005.
DOI : 10.1109/CIC.2005.1588018

S. Vijayan, S. Klein, E. F. Hofstad, F. Lindseth, B. Ystgaard et al., Validation of a non-rigid registration method for motion compensation in 4D ultrasound of the liver, 2013 IEEE 10th International Symposium on Biomedical Imaging, pp.792-795, 2013.
DOI : 10.1109/ISBI.2013.6556594

W. Wein, J. Z. Cheng, and A. Khamene, Ultrasound based respiratory motion compensation in the abdomen, Proc. of MICCAI Worshop on Image Guidance and Computer Assistance for Soft tissue Interventions, p.294, 2008.

F. Yeung, S. F. Levinson, D. Fu, and K. J. Parker, Feature-adaptive motion tracking of ultrasound image sequences using a deformable mesh, IEEE Transactions on Medical Imaging, vol.17, issue.6, pp.945-956, 1998.
DOI : 10.1109/42.746627

Y. Hu, T. J. Carter, H. U. Ahmed, M. Emberton, C. Allen et al., Modelling Prostate Motion for Data Fusion During Image-Guided Interventions, IEEE Transactions on Medical Imaging, vol.30, pp.1887-1900, 2011.

P. A. Yushkevich, J. Piven, H. C. Hazlett, R. G. Smith, S. Ho et al., User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability, NeuroImage, vol.31, issue.3, pp.1116-1128, 2006.
DOI : 10.1016/j.neuroimage.2006.01.015

X. Zhang, M. Gnther, and A. Bongers, Real-Time Organ Tracking in Ultrasound Imaging Using Active Contours and Conditional Density Propagation, Proc. of International Conference on Medical Imaging and Augmented Reality, pp.286-294, 2010.
DOI : 10.1007/978-3-642-15699-1_30