[1] B. F. Han, R. S. Zheng, H. M. Zeng, S. M. Wang, K. X. Sun, R. Chen, L. Li, W. Q. Wei, J. He. Cancer incidence and mortality in China, 2022. J. Natl. Cancer Cent., 4, 47-53(2024).

[2] M. I. Miga, L. W. Clements, J. A. Weis. The Encyclopedia of Medical Robotics, 233-256(2018).

[3] H. G. Xiao et al. Deep learning-based lung image registration: A review. Comput. Biol. Med., 165, 107434(2023).

[4] M. Unser. Fast parametric elastic image registration. IEEE Trans. Image Process., 12, 1427-1442(2003).

[5] M. Sdika. A fast nonrigid image registration with constraints on the Jacobian using large scale constrained optimization. IEEE Trans. Med. Imaging, 27, 271-281(2008).

[6] M. Modat, G. R. Ridgway, Z. A. Taylor, M. Lehmann, J. Barnes, D. J. Hawkes, N. C. Fox, S. Ourselin. Fast free-form deformation using graphics processing units. Comput. Methods Programs Biomed., 98, 278-284(2010).

[7] G. E. Christensen, R. D. Rabbitt, M. I. Miller. Deformable templates using large deformation kinematics. IEEE Trans. Image Process., 5, 1435-1447(1996).

[8] B. Avants, C. Epstein, M. Grossman, J. Gee. Symmetric diffeomorphic image registration with cross-correlation: Evaluating automated labeling of elderly and neurodegenerative brain. Med. Image Anal., 12, 26-41(2008).

[9] L. Dougherty, J. C. Asmuth, W. B. Gefter. Alignment of CT lung volumes with an optical flow method. Acad. Radiol., 10, 249-254(2003).

[10] H. Sokooti, B. De Vos, F. Berendsen, B. P. Lelieveldt, I. Išgum, M. Staring. Nonrigid image registration using multi-scale 3D convolutional neural networks. Int. Conf. Medical Image Computing and Computer-Assisted Intervention, 232-239(2017).

[11] X. Cao, J. Yang, L. Wang, Z. Xue, Q. Wang, D. Shen. Deep learning based inter-modality image registration supervised by intra-modality similarity. Int. Workshop on Machine Learning in Medical Imaging, 55-63(2018).

[12] G. Balakrishnan, A. Zhao, M. R. Sabuncu, J. Guttag, A. V. Dalca. VoxelMorph: A learning framework for deformable medical image registration. IEEE Trans. Med. Imaging, 38, 1788-1800(2019).

[13] S. Zhao, T. Lau, J. Luo, E. I.-C. Chang, Y. Xu. Unsupervised 3D end-to-end medical image registration with volume tweening network. IEEE J. Biomed. Health Inform., 24, 2168-2208(2020).

[14] B. Kim, D. H. Kim, S. H. Park, J. Kim, J. G. Lee, J. C. Ye. Cyclemorph: Cycle consistent unsupervised deformable image registration. Med. Image Anal., 71, 102036(2021).

[15] Y. Wang et al. A transformer-based network for deformable medical image registration. CAAI Int. Conf. Artificial Intelligence, 502-513(2022).

[16] D. Wei, S. Ahmad, Y. Guo, L. Chen, Y. Huang, L. Ma, Z. Wu, G. Li, L. Wang, W. Lin, P. T. Yap, D. Shen, Q. Wang. Recurrent tissue-aware network for deformable registration of infant brain MR images. IEEE Trans. Med. Imaging, 41, 1219-1229(2022).

[17] J. Y. Chen, E. C. Frey, Y. F. He, W. P. Segars, Y. Li, Y. Du. TransMorph: Transformer for unsupervised medical image registration. Med. Image Anal., 82, 1361-8415(2022).

[18] X. Hu, J. Yang, J. Yang. A CNN-based approach for lung 3D-CT registration. IEEE Access, 8, 192835-192843(2020).

[19] S. Zhao, Y. Dong, E. Chang, Y. Xu. Recursive cascaded networks for unsupervised medical image registration. Proc. IEEE/CVF Int. Conf. Computer Vision (ICCV), 10599-10609(2019).

[20] C. Yiqin, Z. Zhenyu, R. Yi, Q. Chenchen, L. Di, D. Qi, N. Dong, W. Yi. Edge-aware pyramidal deformable network for unsupervised registration of brain MR images. Front. Neurosci., 14, 620235(2021).

[21] R. Castillo, E. Castillo, R. Guerra, V. E. Johnson, T. McPhail, A. K. Garg, T. Guerrero. A framework for evaluation of deformable image registration spatial accuracy using large landmark point sets. Phys. Med. Biol., 54, 1849-1870(2009).

[22] E. Castillo, R. Castillo, J. Martinez, M. Shenoy, T. Guerrero. Four-dimensional deformable image registration using trajectory modeling. Phys. Med. Biol., 55, 305-327(2009).

[23] J. Vandemeulebroucke, S. Rit, J. Kybic, P. Clarysse, D. Sarrut. Spatiotemporal motion estimation for respiratory-correlated imaging of the lungs. Med. Phys., 38, 166-178(2011).

[24] A. Hering et al. Learn2Reg challenge: CT lung registration - training data. Med. Comput. Sci.(2020).

[25] D. S. Marcus, T. H. Wang, J. Parker, J. G. Csernansky, J. C. Morris, R. L. Buckner. Open access series of Imaging Studies (OASIS): Cross-sectional MRI data in young, middle aged, nondemented, and demented older adults. J. Cogn. Neurosci., 19, 1498-1507(2007).

[26] C. P. Christoffersen, D. Hansen, P. Poulsen, T. S. Sorensen. Registration-based reconstruction of four-dimensional cone beam computed tomography. IEEE Trans. Med. Imaging, 32, 2064-2077(2013).

[27] Y. Fu, Y. Lei, T. Wang, K. Higgins, J. D. Bradley, W. J. Curran, T. Liu, X. Yang. LungRegNet: An unsupervised deformable image registration method for 4D-CT lung. Med. Phys., 47, 1763-1774(2020).

[28] J. Krebs, T. Mansi, H. Delingette, L. Zhang, F. C. Ghesu, S. Miao, A. K. Maier, N. Ayache, R. Liao, A. Kamen. Robust non-rigid registration through agent-based action learning. Int. Conf. Medical Image Computing and Computer-Assisted Intervention (MICCAI), 344-352(2017).

[29] K. A. J. Eppenhof et al. Deformable image registration using convolutional neural networks. Proc. SPIE, 10574, 105740S(2018).

[30] B. D. de Vos et al. A deep learning framework for unsupervised affine and deformable image registration. Med. Image Anal., 52, 128-143(2019).

[31] J. Y. Lu. Lung CT Image Registration Based on Convolutional Neural Network(2019).

[32] R. Hu, H. Wang, T. Ristaniemi, W. Zhu, X. Sun. Lung CT image registration through landmark-constrained learning with convolutional neural network. 2020 42nd Annual Int. Conf. IEEE Engineering in Medicine & Biology Society (EMBC), 1368-1371(2020).

[33] T. Polzin, J. Rühaak, R. Werner, H. Handels, J. Modersitzki. Lung registration using automatically detected landmarks. Methods Inf. Med., 53, 250-256(2014).

[34] B. Kim, J. Kim, J.-G. Lee, D. H. Kim, S. H. Park, J. C. Ye. Unsupervised deformable image registration using cycle-consistent CNN. International Conference on Medical Image Computing and Computer-Assisted Intervention, 166-174(2019).

[35] X. Yang, R. Kwitt, M. Styner, M. Niethammer. Quicksilver: Fast predictive image registration-a deep learning approach. NeuroImage, 158, 378-396(2017).

[36] G. Balakrishnan, A. Zhao, M. R. Sabuncu, J. Guttag, A. V. Dalca. An unsupervised learning model for deformable medical image registration. Proc. IEEE Conf. Computer Vision and Pattern Recognition, 9252-9260(2018).

[37] B. D. de Vos, F. F. Berendsen, M. A. Viergever, H. Sokooti, M. Staring, I. Išgum. A deep learning framework for unsupervised affine and deformable image registration. Med. Image Anal., 52, 128-143(2019).

[38] J.-Q. Zheng, Z. Wang, B. Huang, N. H. Lim, T. Vincent, B. W. Papiez. Recursive deformable image registration network with mutual attention. Computer Vision and Pattern Recognition, 75-86(2022).

[39] M. Kang, X. Hu, W. Huang, M. R. Scott, M. Reyes. Dual-stream pyramid registration network. Med. Image Anal., 78, 102379(2022).

[40] Y. Liu, L. Zuo, S. Han, Y. Xue, J. L. Prince, A. Carass. Coordinate translator for learning deformable medical image registration. Proc. Int. Workshop Multiscale Multimodal Medical Imaging, 98-109(2022).