[42] M. P. Heinrich, H. Handels, and I. J. Simpson, "Estimating large lung motion in COPD patients by symmetric regularised correspondence fields," in Proc. Int. Conf. Med. Image Comput. Comput.-Assist. Intervent. Cham, Switzerland: Springer, 2015, pp. 338-345.

[43] J. Lv et al., "Joint progressive and coarse-to-fine registration of brain MRI via deformation field integration and non-rigid feature fusion," IEEE Trans. Med. Imag., vol. 41, no. 10, pp. 2788-2802, Oct. 2022.

[44] C. Fourcade, M. Rubeaux, and D. Mateus, "Using Elastix to register inhale/exhale intrasubject thorax CT: A unsupervised baseline to the task 2 of the Learn2Reg challenge," in Proc. Int. Conf. Med. Image Comput. Comput.-Assist. Intervent. Cham, Switzerland: Springer, 2020, pp. 100-105.

[45] T. Estienne et al., "Deep learning-based concurrent brain registration and tumor segmentation," Frontiers Comput. Neurosci., vol. 14, p. 17, Mar. 2020.

[46] T. Estienne et al., "Deep learning based registration using spatial gradients and noisy segmentation labels," in Proc. Int. Conf. Med. Image Comput. Comput.-Assist. Intervent., vol. 12587. Cham, Switzerland: Springer, 2020, p. 87.

[47] N. Gunnarsson, J. Sjölund, and T. B. Schön, "Learning a deformable registration pyramid," in Proc. Int. Conf. Med. Image Comput. Comput.-Assist. Intervent. Cham, Switzerland: Springer, 2020, pp. 80-86.

[48] D. Sun, X. Yang, M.-Y. Liu, and J. Kautz, "PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), Jun. 2018, pp. 8934-8943.

[49] M. C. Lee, O. Oktay, A. Schuh, M. Schaap, and B. Glocker, "Image-and-spatial transformer networks for structure-guided image registration," in Proc. Int. Conf. Med. Image Comput. Comput.-Assist. Intervent. Cham, Switzerland: Springer, 2019, pp. 337-345.

[50] T. C. Mok and A. C. Chung, "Large deformation diffeomorphic image registration with Laplacian pyramid networks," in Proc. Int. Conf. Med. Image Comput. Comput.-Assist. Intervent. Cham, Switzerland: Springer, 2020, pp. 211-221.

[51] T. C. Mok and A. Chung, "Conditional deformable image registration with convolutional neural network," in Proc. Int. Conf. Med. Image Comput. Comput.-Assist. Intervent. Cham, Switzerland: Springer, 2021, pp. 35-45.

[52] V. Jaouen, P.-H. Conze, G. Dardenne, J. Bert, and D. Visvikis, "Regularized directional representations for medical image registration," 2021, arXiv:2111.15509.

[53] G. Lifshitz and D. Raviv, "Cost function unrolling in unsupervised optical flow," 2020, arXiv:2011.14814.

[54] L. Liu et al., "Learning by analogy: Reliable supervision from transformations for unsupervised optical flow estimation," in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR), Jun. 2020, pp. 6489-6498.

[55] S. Häger, S. Heldmann, A. Hering, S. Kuckertz, and A. Lange, "Variable Fraunhofer MEVIS RegLib comprehensively applied to Learn2Reg challenge," in Proc. Int. Conf. Med. Image Comput. Comput.-Assist. Intervent. Cham, Switzerland: Springer, 2020, pp. 74-79.

[56] A. Hering, A. Lange, S. Heldmann, S. Häger, and S. Kuckertz, "Fraunhofer MEVIS image registration solutions for the Learn2Reg 2021 challenge," in Proc. Int. Conf. Med. Image Comput. Comput.-Assist. Intervent. Cham, Switzerland: Springer, 2021, pp. 147-152.

[57] M. Brudfors, Y. Balbastre, G. Flandin, P. Nachev, and J. Ashburner, "Flexible Bayesian modelling for nonlinear image registration," in Proc. Int. Conf. Med. Image Comput. Comput.-Assist. Intervent. Cham, Switzerland: Springer, 2020, pp. 253-263.

[58] M. P. Heinrich, "Closing the gap between deep and conventional image registration using probabilistic dense displacement networks," in Proc. Int. Conf. Med. Image Comput. Comput.-Assist. Intervent. Cham, Switzerland: Springer, 2019, pp. 50-58.

[59] M. P. Heinrich and L. Hansen, "Highly accurate and memory efficient unsupervised learning-based discrete CT registration using 2.5 D displacement search," in Medical Image Computing and Computer Assisted Intervention MICCAI 2020. Cham, Switzerland: Springer, 2020, pp. 190-200.

[60] W. Shao et al., "Geodesic density regression for correcting 4DCT pulmonary respiratory motion artifacts," Med. Image Anal., vol. 72, Aug. 2021, Art. no. 102140.

[61] A. Thorley et al., "Nesterov accelerated ADMM for fast diffeomorphic image registration," in Proc. Int. Conf. Med. Image Comput. Comput.-Assist. Intervent. Cham, Switzerland: Springer, 2021, pp. 150-160.

[62] E. Gibson et al., "Automatic multi-organ segmentation on abdominal CT with dense V-networks," IEEE Trans. Med. Imag., vol. 37, no. 8, pp. 1822-1834, Aug. 2018.

[63] O. Jimenez-del-Toro et al., "Cloud-based evaluation of anatomical structure segmentation and landmark detection algorithms: VISCERAL anatomy benchmarks," IEEE Trans. Med. Imag., vol. 35, no. 11, pp. 2459-2475, Nov. 2016.

[64] A. Hering, S. Häger, J. Moltz, N. Lessmann, S. Heldmann, and B. van Ginneken, "CNN-based lung CT registration with multiple anatomical constraints," Med. Image Anal., vol. 72, Aug. 2021, Art. no. 102139.

[65] M. Hoffmann, B. Billot, D. N. Greve, J. E. Iglesias, B. Fischl, and A. V. Dalca, "SynthMorph: Learning contrast-invariant registration without acquired images," IEEE Trans. Med. Imag., vol. 41, no. 3, pp. 543-558, Mar. 2022.

[66] J. Tukey, "Bias and confidence in not quite large samples," Ann. Math. Statist., vol. 29, no. 2, p. 614, 1958.

[67] J. Ruhaak et al., "Estimation of large motion in lung CT by integrating regularized keypoint correspondences into dense deformable registration," IEEE Trans. Med. Imag., vol. 36, no. 8, pp. 1746-1757, Aug. 2017.