Training cnns for image registration from few samples with model-based data augmentation
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Cites
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Each is checked against this paper’s own text: does it say what the citing paper says it does?
Uzunova et al. designed a network for registration between 2D brain MRI and 2D cardiac MRI, with ground truth generated using statistical appearance models (SAMs), adapted FlowNet architecture, and obtained outperforming results.
“Uzunova et al. (78) designed a network for the registration between 2D brain MRI and 2D cardiac MRI. Their ground truth is generated utilizing statistical appearance models (SAMs). They adapted FlowNet (79) architecture and obtained outperforming results.”
From A review of deep learning-based deformable medical image registration (2022) · cites this paper as [78]
Cites
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155 · see citing works
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 · see citing works
Flownet: Learning optical flow with convolutional networks
Dosovitskiy, A., Fischer, P., Ilg, E., Hausser, P., Hazirbas, C., Golkov, V., Smagt, P. V. D., Cremers, D., & Brox, T. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.316 · Cited by 4,313 · see citing works
Deformable medical image registration: a survey
Sotiras, A., Davatzikos, C., & Paragios, N. (2013) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2013.2265603 · Cited by 1,587 · see citing works
Flagged first, then by citations
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Sorted by citations
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 · see citing works
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 · see citing works
Medical image registration using deep neural networks: A comprehensive review
Boveiri, H. R., Khayami, R., Javidan, R., & Mehdizadeh, A. (2020) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2020.106767 · Cited by 179 · see citing works
A review of deep learning-based deformable medical image registration
Zou, J., Gao, B., Song, Y., & Qin, J. (2022) · Frontiers in Oncology · doi:10.3389/fonc.2022.1047215 · Cited by 91 · see citing works
“(78) designed a network for the registration between 2D brain MRI and 2D cardiac MRI.”
Background
- Authors
- Uzunova, H., Wilms, M., Handels, H., & Ehrhardt, J.
- Affiliations
- University of Lübeck (DE)
- Corresponding author
- Ehrhardt, Jan
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2017
- Pages
- 223-231
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2750752294
- Cited by
- 110
- References
- 4
- Citations by year
- 2026: 1 · 2025: 3 · 2024: 12 · 2023: 14 · 2022: 20 · 2021: 20 · 2020: 20 · 2019: 16 · 2018: 4
- FWCI
- 3.20
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Lecture notes in computer science (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- medical image registration, few-shot learning, generative models, optical flow, FlowNet, dense correspondence estimation, synthetic training data, displacement field, convolutional neural networks
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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