L. Sakka, G. Coll, and J. Chazal, Anatomy and physiology of cerebrospinal fluid, European Annals of Otorhinolaryngology, Head and Neck Diseases, vol.128, issue.6, pp.309-316, 2011.
DOI : 10.1016/j.anorl.2011.03.002

C. Rosén, O. Hansson, K. Blennow, and H. Zetterberg, Fluid biomarkers in Alzheimers disease ? current concepts, Molecular Neurodegeneration, vol.8, 1920.

A. Lebret, J. Hodel, A. Rahmouni, P. Decq, and E. Petit, Cerebrospinal fluid volume analysis for hydrocephalus diagnosis and clinical research, Computerized Medical Imaging and Graphics, vol.37, issue.3, pp.224-233, 2013.
DOI : 10.1016/j.compmedimag.2013.03.005

URL : https://hal.archives-ouvertes.fr/hal-00813015

H. L. Rekate, T. D. Nadkarni, and D. Wallace, The importance of the cortical subarachnoid space in understanding hydrocephalus, Journal of Neurosurgery: Pediatrics, vol.2, issue.1, pp.1-11, 2008.
DOI : 10.3171/PED/2008/2/7/001

J. Hodel, J. Silvera, O. Bekaert, A. Rahmouni, S. Bastuji-garin et al., Intracranial cerebrospinal fluid spaces imaging using a pulse-triggered three-dimensional turbo spin echo MR sequence with variable flip-angle distribution, European Radiology, vol.27, issue.2, pp.402-410, 2011.
DOI : 10.1007/s00330-010-1925-1

D. S. Marcus, T. H. Wang, J. Parker, J. G. Csernansky, J. C. Morris et al., Open Access Series of Imaging Studies (OASIS): Cross-sectional MRI Data in Young, Middle Aged, Nondemented, and Demented Older Adults, Journal of Cognitive Neuroscience, vol.58, issue.9, pp.498-507, 2007.
DOI : 10.1109/42.906424

J. M. Lötjönen, R. Wolz, J. R. Koikkalainen, L. Thurfjell, G. Waldemar et al., Fast and robust multi-atlas segmentation of brain magnetic resonance images, NeuroImage, vol.49, issue.3, pp.2352-2365, 2010.
DOI : 10.1016/j.neuroimage.2009.10.026

K. P. Andriole, J. M. Wolfe, R. Khorasani, S. Treves, D. J. Getty et al., Optimizing Analysis, Visualization, and Navigation of Large Image Data Sets: One 5000-Section CT Scan Can Ruin Your Whole Day, Radiology, vol.259, issue.2, pp.346-362, 2011.
DOI : 10.1148/radiol.11091276

I. Holländer, Cerebral cartography???A method for visualizing cortical structures, Computerized Medical Imaging and Graphics, vol.19, issue.5, pp.397-415, 1995.
DOI : 10.1016/0895-6111(95)00027-5

M. K. Hurdal, K. Stephenson, P. Bowers, D. Sumners, and D. A. Rottenberg, Coordinate systems for conformal cerebellar flat maps, NeuroImage, vol.11, issue.5, p.467, 2000.
DOI : 10.1016/S1053-8119(00)91398-3

M. E. Rettmann, D. Tosun, X. Tao, S. M. Resnick, and J. L. Prince, Program for Assisted Labeling of Sulcal Regions (PALS): description and reliability, NeuroImage, vol.24, issue.2, pp.398-416, 2005.
DOI : 10.1016/j.neuroimage.2004.08.014

D. Tosun, M. E. Rettmann, X. Han, X. Tao, C. Xu et al., Cortical surface segmentation and mapping, NeuroImage, vol.23, pp.108-118, 2004.
DOI : 10.1016/j.neuroimage.2004.07.042

URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4587756

J. D. Foley, A. Van-dam, S. K. Feiner, and J. F. Hughes, Computer graphics: Principles and practice in C, 1995.

L. M. Bugaevskij and J. Snyder, Map projections: A reference manual, 1995.

A. Lebret, Y. Kenmochi, J. Hodel, A. Rahmouni, P. Decq et al., Relief map of the upper cortical subarachnoid space, CARS 2013. Computer Assisted Radiology and Surgery. Proceedings of the 27th International Congress and Exhibition, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00840054

S. S. Erlich and M. L. Apuzzo, The pineal gland: anatomy, physiology, and clinical significance, Journal of Neurosurgery, vol.63, issue.3, pp.321-341, 1985.
DOI : 10.3171/jns.1985.63.3.0321

A. Kimerling, W. Overton, and D. White, Statistical Comparison of Map Projection Distortions Within Irregular Areas, Cartography and Geographic Information Science, vol.22, issue.3, pp.205-221, 1995.
DOI : 10.1559/152304095782540348

R. Klette and A. Rosenfeld, Digital geometry: Geometric methods for digital picture analysis, 2004.

J. Schindelin, I. Arganda-carreras, E. Frise, V. Kaynig, M. Longair et al., Fiji: an open-source platform for biological-image analysis, Nature Methods, vol.27, issue.7, pp.676-682, 2012.
DOI : 10.1038/nmeth.2019

J. Flusser, T. Suk, and B. Zitová, Moments and moment invariants in pattern recognition, 2009.
DOI : 10.1002/9780470684757

M. H. Longair, D. A. Baker, and J. D. Armstrong, Simple Neurite Tracer: open source software for reconstruction, visualization and analysis of neuronal processes, Bioinformatics, vol.27, issue.17, pp.2453-2454, 2011.
DOI : 10.1093/bioinformatics/btr390

P. E. Hart, N. J. Nilsson, and B. Raphael, A formal basis for the heuristic determination of minimal cost paths, IEEE Transactions on Systems Science and Cybernetics, vol.4, p.100107, 1968.

O. Wink, O. Niessen, W. J. Viergever, and M. A. , Minimum cost path determination using a simple heuristic function, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000, pp.998-100, 2000.
DOI : 10.1109/ICPR.2000.903713

X. Tao, X. Han, M. E. Rettmann, J. L. Prince, and C. Davatzikos, Statistical Study on Cortical Sulci of Human Brains, Proceedings of the 17th International Conference on Information Processing in Medical Imaging, pp.475-487, 2001.
DOI : 10.1007/3-540-45729-1_51

H. Yun, K. Im, J. J. Yang, U. Yoon, and J. M. Lee, Automated Sulcal Depth Measurement on Cortical Surface Reflecting Geometrical Properties of Sulci, PLoS ONE, vol.13, issue.1996, p.55977, 2013.
DOI : 10.1371/journal.pone.0055977.s003

F. Benmansour and L. Cohen, Fast Object Segmentation by Growing Minimal Paths from??a??Single Point on 2D or 3D Images, Journal of Mathematical Imaging and Vision, vol.16, issue.1, pp.209-221, 2009.
DOI : 10.1007/s10851-008-0131-0

URL : https://hal.archives-ouvertes.fr/hal-00691876

G. Peyré, M. Péchaud, R. Keriven, and L. D. Cohen, Geodesic Methods in Computer Vision and Graphics, Foundations and Trends?? in Computer Graphics and Vision, vol.5, issue.3-4, pp.3-4197, 2010.
DOI : 10.1561/0600000029

T. Deschamps and L. Cohen, Fast extraction of minimal paths in 3D images and applications to virtual endoscopy11A preliminary version of this work was presented at the ECCV???2000 Conference., Medical Image Analysis, vol.5, issue.4, pp.281-299, 2001.
DOI : 10.1016/S1361-8415(01)00046-9

G. Flandin, F. Kherif, X. Pennec, G. Malandain, N. Ayache et al., Improved Detection Sensitivity in Functional MRI Data Using a Brain Parcelling Technique, Proceedings of the 5th International Conference on Medical Image Computing and Computer- Assisted Intervention, pp.467-474, 2002.
DOI : 10.1007/3-540-45786-0_58

URL : https://hal.archives-ouvertes.fr/inria-00615921

B. Thirion, G. Flandin, P. Pinel, A. Roche, P. Ciuciu et al., Dealing with the shortcomings of spatial normalization: Multi-subject parcellation of fMRI datasets, Human Brain Mapping, vol.22, issue.8, pp.678-693, 2006.
DOI : 10.1002/hbm.20210

T. H. Cormen, C. E. Leiserson, R. L. Rivest, and C. Stein, Introduction to algorithms, 2009.

J. A. Sethian, Level set methods and fast marching methods: Evolving interfaces in computational geometry , fluid mechanics, computer vision, and materials science, 1999.

A. X. Falcão, J. Stolfi, and R. De-alencar-lotufo, The image foresting transform: theory, algorithms, and applications, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.26, issue.1, pp.19-29, 2004.
DOI : 10.1109/TPAMI.2004.1261076

D. M. Grzybowski, E. E. Herderick, K. G. Kapoor, D. W. Holman, and S. E. Katz, Human arachnoid granulations Part I: a technique for quantifying area and distribution on the superior surface of the cerebral cortex, Cerebrospinal Fluid Research, vol.4, issue.1, p.6, 2007.
DOI : 10.1186/1743-8454-4-6

R. Yagel, D. Cohen, and A. Kaufman, Discrete ray tracing, IEEE Computer Graphics and Applications, vol.12, issue.5, pp.19-28, 1992.
DOI : 10.1109/38.156009

D. Cohen-or and A. Kaufman, 3D line voxelization and connectivity control, IEEE Computer Graphics and Applications, vol.17, issue.6, pp.80-87, 1997.
DOI : 10.1109/38.626973