A. Bartoli, Y. Gérard, F. Chadebecq, T. Collins, and D. Pizarro, Shape-fromtemplate, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.37, issue.10, pp.2099-2118, 2015.

S. Bayer, A. Maier, M. Ostermeier, and R. Fahrig, Intraoperative imaging modalities and compensation for brain shift in tumor resection surgery, International Journal of Biomedical Imaging, vol.2017, pp.1-18, 2017.

A. Bilger, J. Dequidt, C. Duriez, and S. Cotin, Biomechanical simulation of electrode migration for deep brain stimulation, Medical Image Computing and Computer-Assisted Intervention -MICCAI 2011, pp.339-346, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00685737

S. Cotin, C. Duriez, J. Lenoir, P. Neumann, and S. Dawson, New approaches to catheter navigation for interventional radiology simulation, MICCAI, vol.8, pp.534-576, 2005.
URL : https://hal.archives-ouvertes.fr/hal-01386225

A. Ebrahimi, Mechanical properties of normal and diseased cerebrovascular system, Journal of vascular and interventional neurology, vol.2, pp.155-62, 2009.

D. Eigen and R. Fergus, Predicting depth, surface normals and semantic labels with a common multi-scale convolutional architecture, Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV). p. 2650-2658. ICCV '15, 2015.

C. Essert, C. Haegelen, F. Lalys, A. Abadie, and P. Jannin, Automatic computation of electrode trajectories for deep brain stimulation: A hybrid symbolic and numerical approach, International journal of computer assisted radiology and surgery, vol.7, pp.517-549, 2011.
URL : https://hal.archives-ouvertes.fr/inria-00637291

N. Hamzé, A. Bilger, C. Duriez, S. Cotin, and C. Essert, Anticipation of brain shift in deep brain stimulation automatic planning, 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp.3635-3638, 2015.

N. Haouchine, P. Juvekar, S. Golby, W. Wells, S. Cotin et al., Alignment of cortical vessels viewed through the surgical microscope with preoperative imaging to compensate for brain shift, SPIE Image-Guided Procedures, Robotic Interventions, and Modeling, vol.60, issue.10, pp.11315-60, 2020.

S. Ji, X. Fan, D. W. Roberts, A. Hartov, and K. D. Paulsen, Cortical surface shift estimation using stereovision and optical flow motion tracking via projection image registration, Medical Image Analysis, vol.18, issue.7, pp.1169-1183, 2014.

S. Ji, Z. Wu, A. Hartov, D. W. Roberts, and K. D. Paulsen, Mutual-informationbased image to patient re-registration using intraoperative ultrasound in imageguided neurosurgery, Medical Physics, vol.35, issue.10, pp.4612-4624

J. Jiang, Y. Nakajima, Y. Sohma, T. Saito, T. Kin et al., Marker-less tracking of brain surface deformations by non-rigid registration integrating surface and vessel/sulci features, International journal of computer assisted radiology and surgery, vol.11, 2016.

D. P. Kingma and J. Ba, Adam: A method for stochastic optimization, the 3rd International Conference for Learning Representations, 2014.

D. Kuhnt, M. H. Bauer, and C. Nimsky, Brain shift compensation and neurosurgical image fusion using intraoperative mri: Current status and future challenges, Critical Reviews and trade in Biomedical Engineering, vol.40, issue.3, pp.175-185, 2012.

M. Luo, S. F. Frisken, S. Narasimhan, L. W. Clements, R. C. Thompson et al., A comprehensive model-assisted brain shift correction approach in image-guided neurosurgery: a case study in brain swelling and subsequent sag after craniotomy, Medical Imaging 2019: Image-Guided Procedures, Robotic Interventions, and Modeling, vol.10951, pp.15-24, 2019.

F. M. Marreiros, S. Rossitti, C. Wang, and Ö. Smedby, Non-rigid deformation pipeline for compensation of superficial brain shift, MICCAI 2013, pp.141-148, 2013.

M. I. Miga, K. Sun, I. Chen, L. W. Clements, T. S. Phei?er et al., Clinical evaluation of a model-updated image-guidance approach to brain shift compensation: experience in 16 cases, International Journal of Computer Assisted Radiology and Surgery, vol.11, issue.8, pp.1467-1474, 2016.

A. Mohammadi, A. Ahmadian, A. D. Azar, A. D. Sheykh, F. Amiri et al., Estimation of intraoperative brain shift by combination of stereovision and doppler ultrasound: phantom and animal model study, International Journal of Computer Assisted Radiology and Surgery, vol.10, issue.11, pp.1753-1764, 2015.

V. M. Pereira, I. Smit-ockeloen, O. Brina, D. Babic, M. Breeuwer et al., Volumetric Measurements of Brain Shift Using Intraoperative Cone-Beam Computed Tomography: Preliminary Study, Operative Neurosurgery, vol.12, issue.1, pp.4-13, 2015.

I. Reinertsen, F. Lindseth, C. Askeland, D. H. Iversen, and G. Unsgård, Intraoperative correction of brain-shift, Acta Neurochirurgica, vol.156, issue.7, pp.1301-1310, 2014.

H. Rivaz and D. L. Collins, Deformable registration of preoperative mr, pre-resection ultrasound, and post-resection ultrasound images of neurosurgery, International Journal of Computer Assisted Radiology and Surgery, vol.10, issue.7, pp.1017-1028, 2015.

O. Ronneberger, P. Fischer, and T. Brox, U-net: Convolutional networks for biomedical image segmentation, vol.9351, pp.234-241, 2015.

K. Sun, T. Phei?er, A. Simpson, J. Weis, R. Thompson et al., Near realtime computer assisted surgery for brain shift correction using biomechanical models. Translational Engineering in Health and Medicine, IEEE Journal, vol.2, pp.1-13, 2014.