[21] D. P. Huttenlocher, G. A. Klanderman, and W. J. Rucklidge. Comparing images using the Hausdorff distance. IEEE Transactions on Pattern Analysis and Machine Intelligence, 15(9):850-863, 1993.
[22] C. R. Jack Jr, M. A. Bernstein, N. C. Fox, P. Thompson, G. Alexander, D. Harvey, B. Borowski, P. J. Britson, J. L. Whitwell, C. Ward, et al. The alzheimer's disease neuroimaging initiative (ADNI): MRI methods. Journal of Magnetic Resonance Imaging: An Official Journal of the International Society for Magnetic Resonance in Medicine, 27(4):685-691, 2008.
[23] D. P. Kingma and J. Ba. Adam: A method for stochastic optimization. In Y. Bengio and Y. LeCun, editors, International Conference on Learning Representations, 2015.
[24] F. Kong and S. C. Shadden. Learning whole heart mesh generation from patient images for computational simulations. IEEE Transactions on Medical Imaging, 2022.
[25] F. Kong, N. Wilson, and S. Shadden. A deep-learning approach for direct whole-heart mesh reconstruction. Medical Image Analysis, 74:102222, 2021.
[26] L. Lebrat, R. Santa Cruz, F. de Gournay, D. Fu, P. Bourgeat, J. Fripp, C. Fookes, and O. Salvado. CorticalFlow: A diffeomorphic mesh transformer network for cortical surface reconstruction. Advances in Neural Information Processing Systems, 34, 2021.
[27] T. Lewiner, H. Lopes, A. W. Vieira, and G. Tavares. Efficient implementation of marching cubes' cases with topological guarantees. Journal of Graphics Tools, 8(2):1-15, 2003.
[28] H. Li, Y. Fan, and A. D. N. Initiative. MDReg-Net: Multi-resolution diffeomorphic image registration using fully convolutional networks with deep self-supervision. Human Brain Mapping, 43(7):2218-2231, 2022.
[29] B. Ma, Z. Han, Y.-S. Liu, and M. Zwicker. Neural-pull: Learning signed distance function from point clouds by learning to pull space onto surface. In International Conference on Machine Learning, pages 7246-7257, 2021.
[30] Q. Ma, L. Li, E. C. Robinson, B. Kainz, D. Rueckert, and A. Alansary. CortexODE: Learning cortical surface reconstruction by neural ODEs. IEEE Transactions on Medical Imaging, 2022.
[31] Q. Ma, E. C. Robinson, B. Kainz, D. Rueckert, and A. Alansary. PialNN: A fast deep learning framework for cortical pial surface reconstruction. In International Workshop on Machine Learning in Clinical Neuroimaging, pages 73-81. Springer, 2021.
[32] J. Maclaren, Z. Han, S. B. Vos, N. Fischbein, and R. Bammer. Reliability of brain volume measurements: a test-retest dataset. Scientific Data, 1(1):1-9, 2014.
[33] D. S. Marcus, T. H. Wang, J. Parker, J. G. Csernansky, J. C. Morris, and R. L. Buckner. Open access series of imaging studies (OASIS): cross-sectional mri data in young, middle aged, nondemented, and demented older adults. Journal of Cognitive Neuroscience, 19(9):1498-1507, 2007.
[34] L. Mescheder, M. Oechsle, M. Niemeyer, S. Nowozin, and A. Geiger. Occupancy networks: Learning 3D reconstruction in function space. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4460-4470, 2019.
[35] M. Modat, D. M. Cash, P. Daga, G. P. Winston, J. S. Duncan, and S. Ourselin. Global image registration using a symmetric block-matching approach. Journal of Medical Imaging, 1(2):024003-024003, 2014.
[36] T. C. Mok and A. Chung. Fast symmetric diffeomorphic image registration with convolutional neural networks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4644-4653, 2020.
[37] D. H. Pak, M. Liu, S. S. Ahn, A. Caballero, J. A. Onofrey, L. Liang, W. Sun, and J. S. Duncan. Weakly supervised deep learning for aortic valve finite element mesh generation from 3D CT images. In International Conference on Information Processing in Medical Imaging, pages 637-648. Springer, 2021.