Analysis of anatomical variability using diffeomorphic iterative centroid in patients with Alzheimer's disease

References

• Beg MF, Miller MI, Trouvé A, Younes L. 2005. Computing large deformation metric mappings via geodesic flows of diffeomorphisms. Int J Comput Vision. 61(2):139–157. doi:10.1023/B:VISI.0000043755.93987.aa.
   Web of Science ®Google Scholar
• Chupin M, Hammers A, Liu RSN, Colliot O, Burdett J, Bardinet E, Duncan JS, Garnero L, Lemieux L. 2009. Automatic segmentation of the hippocampus and the amygdala driven by hybrid constraints: Method and validation. NeuroImage. 46(3):749–761. doi:10.1016/j.neuroimage.2009.02.013.
   PubMed Web of Science ®Google Scholar
• Crane PK, Carle A, Gibbons LE, Insel P, Mackin RS, Gross A, Jones RN, Mukherjee S, Curtis SM, Harvey D, et al. 2012. Development and assessment of a composite score for memory in the Alzheimer's disease neuroimaging initiative (ADNI). Brain Imaging Behav. 6(4):502–516. doi:10.1007/s11682-012-9186-z.
   PubMed Web of Science ®Google Scholar
• Cury C, Glaunès JA, Chupin M, Colliot O, et al. 2014. Fast template-based shape analysis using diffeomorphic iterative centroid. In: MIUA 2014 – Medical image understanding and analysis 2014. London, UK: British Library; p. 39–44.
   Google Scholar
• Cury C, Glaunès JA, Colliot O. 2013. Template estimation for large database: a diffeomorphic iterative centroid method using currents. In: Nielsen F, Barbaresco F, editors. GSI13 – Geometric science of information 2013, volume 8085 of Lecture Notes in Computer Science. Paris: Springer Berlin Heidelberg; p. 103–111.
   Google Scholar
• Cury C, Glaunès JA, Colliot O. 2014. Diffeomorphic iterative centroid methods for template estimation on large datasets. In: Geometric theory of information. Springer; p. 273–299.
   Google Scholar
• Durrleman S. 2010. Statistical models of currents for measuring the variability of anatomical curves, surfaces and their evolution [PhD thesis]. University of Nice-Sophia Antipolis.
   Google Scholar
• Durrleman S, Pennec X, Trouvé A, Ayache N, et al. 2008. A forward model to build unbiased atlases from curves and surfaces. In: 2nd medical image computing and computer-assisted intervention (MICCAI) workshop on mathematical foundations of computational anatomy. New York, USA: Springer; p. 68–79.
   Google Scholar
• Durrleman S, Prastawa M, Korenberg JR, Joshi S, Trouvé A, Gerig G. 2012. Topology preserving Atlas construction from shape data without correspondence using sparse parameters. In: MICCAI 2012 – Medical image computing and computer-assisted intervention, LNCS. Nice, France: Springer; p. 223–230.
   Google Scholar
• Emery M, Mokobodzki G. 1991. Sur le barycentre d'une probabilité dans une variété. In: Séminaire de probabilités, volume 1425 of Lecture Notes in Mathematics. Springer; p. 220–233.
   Google Scholar
• Glaunès JA. 2005. Transport par difféomorphismes de points, de mesures et de courants pour la comparaison de formes et l'anatomie numérique [PhD thesis]. Université Paris 13.
   Google Scholar
• Glaunès JA, Joshi S. 2006. Template estimation from unlabeled point set data and surfaces for computational anatomy. In: The international workshop on the MFCA-2006. Copenhagen, Denmark: Springer; p. 29–39.
   Google Scholar
• Grenander U, Miller MI. 1998. Computational anatomy: an emerging discipline. Quarterly of Applied Mathematics. 56(4):617–694.
   Web of Science ®Google Scholar
• Hastie T, Tibshirani R, Friedman J, Hastie T, Friedman J, Tibshirani R. 2009. The elements of statistical learning. New York: Springer-Verlag.
   Google Scholar
• Ma J, Miller MI, Trouvé A, Younes L. 2008. Bayesian template estimation in computational anatomy. NeuroImage. 42(1):252–261. doi:10.1016/j.neuroimage.2008.03.056.
   PubMed Web of Science ®Google Scholar
• Vaillant M, Glaunès JA. 2005. Surface matching via currents. In: Information processing in medical imaging. Glenwood Springs, CO: Springer; p. 381–392.
   Google Scholar
• Vaillant M, Miller MI, Younes L, Trouvé A. 2004. Statistics on diffeomorphisms via tangent space representations. NeuroImage. 23:S161–S169. doi:10.1016/j.neuroimage.2004.07.023.
   PubMed Web of Science ®Google Scholar