F. Morin, H. Courtecuisse, I. Reinertsen, F. L. Lann, O. Palombi et al., Brainshift compensation using intraoperative ultrasound and constraint-based biomechanical simulation, Medical Image Analysis, vol.40, pp.133-153, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01560157

M. I. Miga, D. W. Roberts, F. E. Kennedy, L. A. Platenik, A. Hartov et al., Modeling of retraction and resection for intraoperative updating of images, Neurosurgery, vol.49, issue.1, pp.75-85, 2001.

M. Ferrant, A. Nabavi, B. Macq, P. M. Black, F. A. Jolesz et al., Serial registration of intraoperative mr images of the brain, Med Image Anal, vol.6, pp.337-359, 2002.

M. Bucki, O. Palombi, M. Bailet, and Y. Payan, Doppler Ultrasound Driven Biomechanical Model of the Brain for Intraoperative Brain-Shift Compensation: A Proof of Concept in Clinical Conditions, pp.135-165, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00706803

X. Fan, D. W. Roberts, J. D. Olson, S. Ji, T. J. Schaewe et al., Image updating for brain shift compensation during resection, Operative Neurosurgery, vol.14, issue.4, pp.402-411, 2018.

M. Riva, C. Hennersperger, F. Milletari, A. Katouzian, F. Pessina et al., 3d intra-operative ultrasound and mr image guidance: pursuing an ultrasoundbased management of brainshift to enhance neuronavigation, International Journal of Computer Assisted Radiology and Surgery, vol.12, pp.1711-1725, 2017.

I. Machado, M. Toews, J. Luo, P. Unadkat, W. Essayed et al., Non-rigid registration of 3d ultrasound for neurosurgery using automatic feature detection and matching, International Journal of Computer Assisted Radiology and Surgery, 2018.

Y. Xiao, M. Fortin, G. Unsgård, H. Rivaz, and I. Reinertsen, Resect: a clinical database of pre-operative mri and intra-operative ultrasound in low-grade glioma surgeries, 2016.

H. Noh, S. Hong, and B. Han, Learning deconvolution network for semantic segmentation, 2015.

V. Badrinarayanan, A. Kendall, and R. Cipolla, Segnet: A deep convolutional encoder-decoder architecture for image segmentation, 2015.

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

R. and D. L. , Measures of the amount of ecologic association between species, Ecology, vol.26, issue.3, pp.297-302