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Existing medical image registration algorithms rely on either dataset specific training or local texture-based features to align images.
Rueckert, D., Sonoda, L.I., Hayes, C., Hill, D.L., Leach, M.O., Hawkes, D.J.: Nonrigid registration using free-form deformations: application to breast mr images. IEEE transactions on medical imaging 18
1999
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Yushkevich, P.A., Piven, J., Cody Hazlett, H., Gimpel Smith, R., Ho, S., Gee, J.C., Gerig, G.: User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 31
2006
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Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: 2009 IEEE conference on computer vision and pattern recognition. pp. 248–255. Ieee (2009)
2009
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Steinbrücker, F., Pock, T., Cremers, D.: Large displacement optical flow computation withoutwarping. In: 2009 IEEE 12th International Conference on Computer Vision. pp. 1609–1614. IEEE (2009)
2009
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Halko, N., Martinsson, P.G., Tropp, J.A.: Finding structure with randomness: Probabilistic algorithms for constructing approximate matrix decompositions. SIAM review 53
2011
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Heinrich, M.P., Jenkinson, M., Bhushan, M., Matin, T., Gleeson, F.V., Brady, M., Schnabel, J.A.: Mind: Modality independent neighbourhood descriptor for multi-modal deformable registration. Medical image analysis 16
2012
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Heinrich, M.P., Jenkinson, M., Brady, M., Schnabel, J.A.: Mrf-based deformable registration and ventilation estimation of lung ct. IEEE transactions on medical imaging 32
2013
Earlier work this paper cites.
2014
Earlier work this paper cites.
Balakrishnan, G., Zhao, A., Sabuncu, M.R., Guttag, J., Dalca, A.V.: Voxelmorph: a learning framework for deformable medical image registration. IEEE transactions on medical imaging 38
2019
Cited alongside, same era.
Haskins, G., Kruecker, J., Kruger, U., Xu, S., Pinto, P.A., Wood, B.J., Yan, P.: Learning deep similarity metric for 3D MR–TRUS image registration. International journal of computer assisted radiology and surgery 14
2019
Cited alongside, same era.
2020
Cited alongside, same era.
Haskins, G., Kruger, U., Yan, P.: Deep learning in medical image registration: a survey. Machine Vision and Applications 31
2020
Cited alongside, same era.
Heinrich, M.P., Hansen, L.: Voxelmorph++ going beyond the cranial vault with keypoint supervision and multi-channel instance optimisation. In: International Workshop on Biomedical Image Registration. pp. 85–95. Springer (2022)
2022
Later among the works it cites.
Hering, A., Hansen, L., Mok, T.C., Chung, A.C., Siebert, H., Häger, S., Lange, A., Kuckertz, S., Heldmann, S., Shao, W., et al.: Learn2reg: comprehensive multi-task medical image registration challenge, dataset and evaluation in the era of deep learning. IEEE Transactions on Medical Imaging 42
2022
Later among the works it cites.
Song, X., Chao, H., Xu, X., Guo, H., Xu, S., Turkbey, B., Wood, B.J., Sanford, T., Wang, G., Yan, P.: Cross-modal attention for multi-modal image registration. Medical Image Analysis 82
2022
Later among the works it cites.
Ye, Y., Zhang, J., Chen, Z., Xia, Y.: DeSD: Self-supervised learning with deep self-distillation for 3D medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 545–555. Springer (2022)
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Mok, T.C., Chung, A.C.: Large deformation diffeomorphic image registration with laplacian pyramid networks. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2020: 23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part III 23. pp. 211–221. Springer (2020)
2020
Cited alongside, same era.
Caron, M., Touvron, H., Misra, I., Jégou, H., Mairal, J., Bojanowski, P., Joulin, A.: Emerging properties in self-supervised vision transformers. In: Proceedings of the IEEE/CVF international conference on computer vision. pp. 9650–9660 (2021)
2021
Cited alongside, same era.
Siebert, H., Hansen, L., Heinrich, M.P.: Fast 3D registration with accurate optimisation and little learning for Learn2Reg 2021. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 174–179. Springer (2021)
2021
Cited alongside, same era.
Song, X., Guo, H., Xu, X., Chao, H., Xu, S., Turkbey, B., Wood, B.J., Wang, G., Yan, P.: Cross-modal attention for mri and ultrasound volume registration. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part IV 24. pp. 66–75. Springer (2021)
2021
Cited alongside, same era.
2022
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
Li, Z., Tian, L., Mok, T.C., Bai, X., Wang, P., Ge, J., Zhou, J., Lu, L., Ye, X., Yan, K., et al.: Samconvex: Fast discrete optimization for ct registration using self-supervised anatomical embedding and correlation pyramid. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 559–569. Springer (2023)
2023
Later among the works it cites.