[2] V. Arsigny. Processing data in lie groups: An algebraic approach. Application to non-linear registration and diffusion tensor MRI. PhD thesis, Citeseer, 2004.
[3] V. Arsigny, O. Commowick, X. Pennec, and N. Ayache. A log-euclidean framework for statistics on diffeomorphisms. In International Conference on Medical Image Computing and Computer-Assisted, pages 924-931. Springer, 2006.
[4] J. Ashburner. A fast diffeomorphic image registration algorithm. Neuroimage, 38(1):95-113, 2007.
[5] B. B. Avants, C. L. Epstein, M. Grossman, and J. C. Gee. Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain. Medical Image Analysis, 12(1):26-41, 2008.
[6] G. Balakrishnan, A. Zhao, M. R. Sabuncu, J. Guttag, and A. V. Dalca. Voxelmorph: a learning framework for deformable medical image registration. IEEE Transactions on Medical Imaging, 38(8):1788-1800, 2019.
[7] P.-L. Bazin and D. L. Pham. Topology correction of segmented medical images using a fast marching algorithm. Computer Methods and Programs in Biomedicine, 88(2):182-190, 2007.
[8] M. F. Beg, M. I. Miller, A. Trouve, and L. Younes. Computing large deformation metric mappings via geodesic flows of diffeomorphisms. International Journal of Computer Vision, 61:139-157, 2005.
[9] M. Bertoux, J. Lagarde, F. Corlier, L. Hamelin, J.-F. Mangin, O. Colliot, M. Chupin, M. N. Braskie, P. M. Thompson, M. Bottlaender, et al. Sulcal morphology in alzheimer's disease: an effective marker of diagnosis and cognition. Neurobiology of Aging, 84:41-49, 2019.
[10] F. Bongratz, A.-M. Rickmann, S. Pölsterl, and C. Wachinger. Vox2Cortex: Fast explicit reconstruction of cortical surfaces from 3D MRI scans with geometric deep neural networks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 20773-20783, 2022.
[11] R. L. Burden, J. D. Faires, and A. M. Burden. Numerical Analysis. Cengage learning, 2015.
[12] R. T. Chen, Y. Rubanova, J. Bettencourt, and D. K. Duvenaud. Neural ordinary differential equations. Advances in Neural Information Processing Systems, 31, 2018.
[13] R. S. Cruz, L. Lebrat, P. Bourgeat, C. Fookes, J. Fripp, and O. Salvado. DeepCSR: A 3D deep learning approach for cortical surface reconstruction. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 806-815, 2021.
[14] A. M. Dale, B. Fischl, and M. I. Sereno. Cortical surface-based analysis: I. segmentation and surface reconstruction. Neuroimage, 9(2):179-194, 1999.
[15] G. Fahim, K. Amin, and S. Zarif. Single-view 3D reconstruction: A survey of deep learning methods. Computers & Graphics, 94:164-190, 2021.
[16] H. Fan, H. Su, and L. J. Guibas. A point set generation network for 3d object reconstruction from a single image. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 605-613, 2017.
[17] B. Fischl. Freesurfer. Neuroimage, 62(2):774-781, 2012.
[18] K. Gopinath, C. Desrosiers, and H. Lombaert. SegRecon: Learning joint brain surface reconstruction and segmentation from images. In International Conference on Medical Image Computing and Computer Assisted Intervention, pages 650–659. Springer, 2021.
[19] Y. Hong, S. Ahmad, Y. Wu, S. Liu, and P.-T. Yap. Vox2Surf: Implicit surface reconstruction from volumetric data. In International Workshop on Machine Learning in Medical Imaging in Conjunction with MICCAI 2021, pages 644-653. Springer, 2021.
[20] A. Hoopes, J. E. Iglesias, B. Fischl, D. Greve, and A. V. Dalca. Topofit: Rapid reconstruction of topologically-correct cortical surfaces. In Medical Imaging with Deep Learning, 2022.