The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
End-to-End Unsupervised Deformable Image Registration with a Convolutional Neural Network
Open accessAbstract
In this work we propose a deep learning network for deformable image registration (DIRNet). The DIRNet consists of a convolutional neural network (ConvNet) regressor, a spatial transformer, and a resampler. The ConvNet analyzes a pair of fixed and moving images and outputs parameters for the spatial transformer, which generates the displacement vector field that enables the resampler to warp the moving image to the fixed image. The DIRNet is trained end-to-end by unsupervised optimization of a similarity metric between input image pairs. A trained DIRNet can be applied to perform registration on unseen image pairs in one pass, thus non-iteratively. Evaluation was performed with registration of images of handwritten digits (MNIST) and cardiac cine MR scans (Sunnybrook Cardiac Data). The results demonstrate that registration with DIRNet is as accurate as a conventional deformable image registration method with short execution times.
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Cites
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Ioffe, S., & Szegedy, C. (2015) · arXiv · doi:10.48550/arxiv.1502.03167 · Cited by 27,818
Nonrigid registration using free-form deformations: application to breast mr images
Rueckert, D., Sonoda, L. I., Hayes, C., Hill, D. L. G., Leach, M. O., & Hawkes, D. J. (1999) · IEEE Transactions on Medical Imaging · doi:10.1109/42.796284 · Cited by 5,265
Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)
Clevert, D.-A., Unterthiner, T., & Hochreiter, S. (2015) · arXiv · doi:10.48550/arxiv.1511.07289 · Cited by 4,513
A CNN Regression Approach for Real-Time 2D/3D Registration
Miao, S., Wang, Z. J., & Liao, R. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2521800 · Cited by 514
An artificial agent for robust image registration
Liao, R., Miao, S., De Tournemire, P., Grbic, S., Kamen, A., Mansi, T., & Comaniciu, D. (2017) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v31i1.11230 · Cited by 199
Real-time 2D/3D registration via CNN regression
Miao, S., Wang, Z. J., Zheng, Y., & Liao, R. (2016) · 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2016.7493536 · Cited by 101
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Image Matching from Handcrafted to Deep Features: A Survey
Ma, J., Jiang, X., Fan, A., Jiang, J., & Yan, J. (2021) · International Journal of Computer Vision · doi:10.1007/s11263-020-01359-2 · Cited by 990
A deep learning framework for unsupervised affine and deformable image registration
de Vos, B. D., Berendsen, F. F., Viergever, M. A., Sokooti, H., Staring, M., & Išgum, I. (2019) · Medical Image Analysis · doi:10.1016/j.media.2018.11.010 · Cited by 818
Self-citationTransMorph: Transformer for unsupervised medical image registration
Chen, J., Frey, E. C., He, Y., Segars, W. P., Li, Y., & Du, Y. (2022) · Medical Image Analysis · doi:10.1016/j.media.2022.102615 · Cited by 804
Deep learning in medical image registration: a survey
Haskins, G., Kruger, U., & Yan, P. (2020) · Machine Vision and Applications · doi:10.1007/s00138-020-01060-x · Cited by 704
Deep learning in medical image registration: a review
Fu, Y., Lei, Y., Wang, T., Curran, W. J., Liu, T., & Yang, X. (2020) · Physics in Medicine & Biology · doi:10.1088/1361-6560/ab843e · Cited by 661
Metadata
- Authors
- de Vos, B. D., Berendsen, F. F., Viergever, M. A., Staring, M., & Išgum, I.
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2017
- Pages
- 204-212
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2608822622
- Cited by
- 450
- References
- 7
- FWCI
- 14.72
- Type
- conference paper
- Language
- en
- Open access
- green
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- de Vos, B. D., Berendsen, F. F., Viergever, M. A., Staring, M., & Išgum, I. (2017). End-to-End Unsupervised Deformable Image Registration with a Convolutional Neural Network. Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-319-67558-9_24
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