The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.
SVF-Net: Learning Deformable Image Registration Using Shape Matching
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Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Rohe et al. (76) adopted a U-Net-like network to predict the deformation field for 3D cardiac MRI volume registration. The supervisions are transformations generated from mesh segmentations, and SSD between the supervision and prediction is set as the loss function. Their results are comparable with those from traditional registration.”
From A review of deep learning-based deformable medical image registration (2022) · cites this paper as [76]
Citations
Cites
U-Net: Convolutional Networks for Biomedical Image Segmentation
Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404
Fully convolutional networks for semantic segmentation
Long, J., Shelhamer, E., & Darrell, T. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298965 · Cited by 38,834
Computing Large Deformation Metric Mappings via Geodesic Flows of Diffeomorphisms
Beg, M. F., Miller, M. I., Trouvé, A., & Laurent Younes, B. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000043755.93987.aa · Cited by 1,764
A Log-Euclidean Framework for Statistics on Diffeomorphisms
Arsigny, V., Commowick, O., Pennec, X., & Ayache, N. (2006) · Lecture notes in computer science · doi:10.1007/11866565_113 · Cited by 461
Self-citationMorphometry of anatomical shape complexes with dense deformations and sparse parameters
Durrleman, S., Prastawa, M. W., Charon, N., Korenberg, J. R., Joshi, S. C., Gerig, G., & Trouvé, A. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.06.043 · Cited by 288
A physics-based coordinate transformation for 3-D image matching
Davis, M. H., Khotanzad, A. R., Flamig, D. P., & Harms, S. E. (1997) · IEEE Transactions on Medical Imaging · doi:10.1109/42.585766 · Cited by 267
LCC-Demons: A robust and accurate symmetric diffeomorphic registration algorithm
Lorenzi, M., Ayache, N., Frisoni, G. B., & Pennec, X. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.04.114 · Cited by 160
Self-citationFast Predictive Image Registration
Yang, X., Kwitt, R., & Niethammer, M. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46976-8_6 · Cited by 127
Computational analysis of LDDMM for brain mapping
Ceritoglu, C., Tang, X., Chow, M., Hadjiabadi, D. H., Shah, D., Brown, T., Burhanullah, M. H., Trinh, H., Hsu, J. T., Ament, K. A., et al. (2013) · Frontiers in Neuroscience · doi:10.3389/fnins.2013.00151 · Cited by 54
Automatic Segmentation of the Myocardium in Cine MR Images Using Deformable Registration
Jolly, M., Guetter, C., Lu, X., et al. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-28326-0_10 · Cited by 49
Learning-Based Detection and Tracking in Medical Imaging: A Probabilistic Approach
Wang, Y., Georgescu, B., Chen, T., Wu, W. H., Wang, P., Lu, X., Ionasec, R. I., Zheng, Y., & Comaniciu, D. (2012) · Lecture notes in computational vision and biomechanics · doi:10.1007/978-94-007-5446-1_9 · Cited by 18
Cited by
Sorted by citations
Bernard, O., Lalande, A., Zotti, C., Cervenansky, F., Yang, X., Heng, P.-A., Cetin, I., Lekadir, K., Camara, O., Gonzalez Ballester, M. A., et al. (2018) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2018.2837502 · Cited by 2,431
“The registration module implements an encoder-decoder network called SVFNet [56].”
MethodSelf-citationVoxelMorph: A Learning Framework for Deformable Medical Image Registration
Balakrishnan, G., Zhao, A., Sabuncu, M. R., Guttag, J. V., & Dalca, A. V. (2019) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2019.2897538 · Cited by 2,334
TransMorph: 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 883
VoxelMorph: A Learning Framework for Deformable Medical Image RegistrationPreprint
Balakrishnan, G., Zhao, A. T., Sabuncu, M. R., Guttag, J. V., & Dalca, A. V. (2019) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 728
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
“The following year, Rohe et al. [102] also used a U-net [103] inspired network to estimate the deformation field used to register 3D cardiac MR volumes.”
Method
Metadata
- Authors
- Rohé, M.-M., Datar, M., Heimann, T., Sermesant, M., & Pennec, X.
- Affiliations
- Université Côte d'Azur (FR)
- Siemens Healthineers (Germany) (DE)
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2017
- Pages
- 266-274
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2752785527
- Semantic Scholar
- f9a53dcd92bd7c1f4e1689a08ad33dae77209eef
- Cited by
- 330
- References
- 11
- Citations by year
- 2026: 7 · 2025: 25 · 2024: 26 · 2023: 35 · 2022: 54 · 2021: 50 · 2020: 44 · 2019: 60 · 2018: 28 · 2017: 1
- FWCI
- 10.63
- Type
- conference paper
- Language
- en
- Open access
- green
- inria.hal.science
- Licence
- Locations
- Lecture notes in computer science (publishedVersion)
- HAL (submittedVersion, other-oa)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Physical Sciences › Computer Science › Computer Graphics and Computer-Aided Design › 3D Shape Modeling and Analysis
- Physical Sciences › Physics and Astronomy › Radiation › Advanced Radiotherapy Techniques
- Keywords
- deformable image registration, shape matching, fully convolutional network, training strategies, 3D medical image registration, outlier robustness
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