PC-SwinMorph: Patch Representation for Unsupervised Medical Image Registration and Segmentation

D. Rueckert, L. I. Sonoda, C. Hayes, D. L. Hill, M. O. Leach, and D. J. Hawkes, “Nonrigid registration using free-form deformations: application to breast mr images,” IEEE transactions on medical imaging , vol. 18, no. 8, pp. 712–721, 1999

1999

Earlier work this paper cites.

M. F. Beg, M. I. Miller, A. Trouvé, and L. Younes, “Computing large deformation metric mappings via geodesic flows of diffeomorphisms,” International journal of computer vision , vol. 61, no. 2, pp. 139–157, 2005

2005

Earlier work this paper cites.

R. Hadsell, S. Chopra, and Y. LeCun, “Dimensionality reduction by learning an invariant mapping,” in 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’06) , vol. 2, 2006, pp. 1735–1742

2006

Earlier work this paper cites.

D. W. Shattuck, M. Mirza, V. Adisetiyo, C. Hojatkashani, G. Salamon, K. L. Narr, R. A. Poldrack, R. M. Bilder, and A. W. Toga, “Construction of a 3D probabilistic atlas of human cortical structures,” NeuroImage , vol. 39, no. 3, 2008. [Online]. Available: http://linkinghub.elsevier.com/retrieve/pii/S1053811907008099

2008

Earlier work this paper cites.

T. Vercauteren, X. Pennec, A. Perchant, and N. Ayache, “Diffeomorphic demons: Efficient non-parametric image registration,” NeuroImage , vol. 45, no. 1, pp. S61–S72, 2009

2009

Earlier work this paper cites.

G. L. Hart, C. Zach, and M. Niethammer, “An optimal control approach for deformable registration,” in Conference on Computer Vision and Pattern Recognition , 2009, pp. 9–16

2009

Earlier work this paper cites.

D. N. Kennedy, C. Haselgrove, S. M. Hodge, P. S. Rane, N. Makris, and J. A. Frazier, “Candishare: a resource for pediatric neuroimaging data.” Neuroinformatics , vol. 10, no. 3, 2012

2012

Earlier work this paper cites.

K. He, X. Zhang, S. Ren, and J. Sun, “Delving deep into rectifiers: Surpassing human-level performance on imagenet classification,” 2015

2015

Earlier work this paper cites.

A. I. Aviles, S. M. Alsaleh, J. K. Hahn, and A. Casals, “Towards retrieving force feedback in robotic-assisted surgery: A supervised neuro-recurrent-vision approach,” IEEE transactions on haptics , vol. 10, no. 3, pp. 431–443, 2016

2016

Earlier work this paper cites.

M. Jaderberg, K. Simonyan, A. Zisserman, and K. Kavukcuoglu, “Spatial transformer networks,” 2016

2016

Earlier work this paper cites.

Y. Fu, S. Liu, H. H. Li, and D. Yang, “Automatic and hierarchical segmentation of the human skeleton in ct images,” Physics in Medicine & Biology , vol. 62, no. 7, p. 2812, 2017

2017

Earlier work this paper cites.

X. Yang, R. Kwitt, M. Styner, and M. Niethammer, “Quicksilver: Fast predictive image registration–a deep learning approach,” NeuroImage , vol. 158, pp. 378–396, 2017

2017

Earlier work this paper cites.

H. Sokooti, B. De Vos, F. Berendsen, B. P. Lelieveldt, I. Išgum, and M. Staring, “Nonrigid image registration using multi-scale 3d convolutional neural networks,” in International conference on medical image computing and computer-assisted intervention . Springer, 2017, pp. 232–239

2017

Earlier work this paper cites.

M.-M. Rohé, M. Datar, T. Heimann, M. Sermesant, and X. Pennec, “Svf-net: Learning deformable image registration using shape matching,” in International conference on medical image computing and computer-assisted intervention . Springer, 2017

2017

Earlier work this paper cites.

X. Cao, J. Yang, J. Zhang, D. Nie, M. Kim, Q. Wang, and D. Shen, “Deformable image registration based on similarity-steered cnn regression,” in International Conference on Medical Image Computing and Computer-Assisted Intervention , 2017, pp. 300–308

2017

Earlier work this paper cites.

B. D. de Vos, F. F. Berendsen, M. A. Viergever, M. Staring, and I. Išgum, “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 . Springer, 2017, pp. 204–212

2017

Cited alongside, same era.

A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” in Advances in neural information processing systems , 2017, pp. 5998–6008

2017

Cited alongside, same era.

X. Cao, J. Yang, J. Zhang, Q. Wang, P.-T. Yap, and D. Shen, “Deformable image registration using a cue-aware deep regression network,” IEEE Transactions on Biomedical Engineering , vol. 65, no. 9, pp. 1900–1911, 2018

2018

Cited alongside, same era.

J. Krebs, T. Mansi, B. Mailhé, N. Ayache, and H. Delingette, “Learning structured deformations using diffeomorphic registration,” arXiv preprint arXiv:1804.07172 , vol. 80, 2018

Original

K. He, H. Fan, Y. Wu, S. Xie, and R. Girshick, “Momentum contrast for unsupervised visual representation learning,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 9729–9738

2020

Later among the works it cites.

Y. Tian, D. Krishnan, and P. Isola, “Contrastive multiview coding,” in Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XI 16 . Springer, 2020, pp. 776–794

2020

Later among the works it cites.

R. Han, A. Uneri, R. C. Vijayan, P. Wu, P. Vagdargi, N. Sheth, S. Vogt, G. Kleinszig, G. Osgood, and J. H. Siewerdsen, “Fracture reduction planning and guidance in orthopaedic trauma surgery via multi-body image registration,” Medical image analysis , vol. 68, p. 101917, 2021

2021

Later among the works it cites.

J. Liu, A. I. Aviles-Rivero, H. Ji, and C.-B. Schönlieb, “Rethinking medical image reconstruction via shape prior, going deeper and faster: Deep joint indirect registration and reconstruction,” Medical Image Analysis , vol. 68, p. 101930, 2021