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We propose a deformable registration algorithm based on unsupervised learning of a low-dimensional probabilistic parameterization of deformations.
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Lorenzi, M., Ayache, N., Frisoni, G.B., et al.: LCC-Demons: a robust and accurate symmetric diffeomorphic registration algorithm. NeuroImage 81, 470–483 (2013)
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Sotiras, A., Davatzikos, C., Paragios, N.: Deformable medical image registration: A survey. IEEE Transactions on Medical Imaging 32(7), 1153–1190 (2013)
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Kingma, D.P., et al.: Semi-supervised learning with deep generative models. In: Advances in Neural Information Processing Systems. pp. 3581–3589 (2014)
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2015
Cited alongside, same era.
Jason, J.Y., et al.: Back to basics: Unsupervised learning of optical flow via brightness constancy and motion smoothness. In: ECCV. pp. 3–10. Springer (2016)
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Yang, X., Kwitt, R., Niethammer, M.: Fast predictive image registration. In: Deep Learning and Data Labeling for Medical Applications, pp. 48–57. Springer (2016)
2016
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Krebs, J., Mansi, T., Delingette, H., et al.: Robust non-rigid registration through agent-based action learning. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 344–352. Springer (2017)
2017
Cited alongside, same era.
Sokooti, H., de Vos, B., et al.: Nonrigid image registration using multi-scale 3d convolutional neural networks. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 232–239. Springer (2017)
2017
Later among the works it cites.
de Vos, B.D., Berendsen, F.F., Viergever, M.A., Staring, M., Išgum, I.: End-to-end unsupervised deformable image registration with a convolutional neural network. In: Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, pp. 204–212. Springer (2017)
2017
Later among the works it cites.
Balakrishnan, G., et al.: An unsupervised learning model for deformable medical image registration. In: Proceedings of the IEEE CVPR. pp. 9252–9260 (2018)
2018
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