Cited by 123Medical Image Segmentation TechniquesDomain Adaptation and Few-Shot LearningMedical Imaging and Analysis

The DOI matches its Crossref record; 9 of the 10 references it cites were found in a registry or an index.

Deep Learning Based Inter-modality Image Registration Supervised by Intra-modality Similarity

Open access

Cao, Xiaohuan, Yang, Jianhuan, Wang, Li, Xue, Zhong, Wang, Qian, Shen, Dinggang·2018·Lecture Notes in Computer Science

DOI 10.1007/978-3-030-00919-9_7PMC PMC6516490

Open this paper in CoChat — free account

Abstract

Non-rigid inter-modality registration can facilitate accurate information fusion from different modalities, but it is challenging due to the very different image appearances across modalities. In this paper, we propose to train a non-rigid inter-modality image registration network, which can directly predict the transformation field from the input multimodal images, such as CT and MR images. In particular, the training of our inter-modality registration network is supervised by intra-modality similarity metric based on the available paired data, which is derived from a pre-aligned CT and MR dataset. Specifically, in the training stage, to register the input CT and MR images, their similarity is evaluated on the warped MR image and the MR image that is paired with the input CT. So that, the intra-modality similarity metric can be directly applied to measure whether the input CT and MR images are well registered. Moreover, we use the idea of dual-modality fashion, in which we measure the similarity on both CT modality and MR modality. In this way, the complementary anatomies in both modalities can be jointly considered to more accurately train the inter-modality registration network. In the testing stage, the trained inter-modality registration network can be directly applied to register the new multimodal images without any paired data. Experimental results have shown that, the proposed method can achieve promising accuracy and efficiency for the challenging non-rigid inter-modality registration task and also outperforms the state-of-the-art approaches.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Integrity signals

Registry

Crossref · 10.1007/978-3-030-00919-9_7

Cites

9 of 9 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

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
Advances in neural information processing systems 7
Cios, K. J., & Shields, M. E. (1997) · Neurocomputing · doi:10.1016/s0925-2312(97)90018-7 · Cited by 22,093
A global optimisation method for robust affine registration of brain images
Jenkinson, M., & Smith, S. M. (2001) · Medical Image Analysis · doi:10.1016/s1361-8415(01)00036-6 · Cited by 7,018
Symmetric diffeomorphic image registration with cross-correlation: Evaluating automated labeling of elderly and neurodegenerative brain
Avants, B., Epstein, C. L., Grossman, M., & Gee, J. C. (2007) · Medical Image Analysis · doi:10.1016/j.media.2007.06.004 · Cited by 5,924
Mutual-information-based registration of medical images: a survey
Pluim, J. P. W., Maintz, J. B. A., & Viergever, M. A. (2003) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2003.815867 · Cited by 2,882
End-to-End Unsupervised Deformable Image Registration with a Convolutional Neural Network
de Vos, B. D., Berendsen, F. F., Viergever, M. A., Staring, M., & Išgum, I. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-67558-9_24 · Cited by 456
Non-parametric Diffeomorphic Image Registration with the Demons Algorithm
Vercauteren, T. K., Pennec, X., Aymeric Perchant, B., & Ayache, N. (2007) · Lecture notes in computer science · doi:10.1007/978-3-540-75759-7_39 · Cited by 380
Deformable Image Registration Using a Cue-Aware Deep Regression Network
Cao, X., Yang, J., Zhang, J., Wang, Q., Yap, P., & Shen, D. G. (2018) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2018.2822826 · Cited by 123
Self-citation
Region-Adaptive Deformable Registration of CT/MRI Pelvic Images via Learning-Based Image Synthesis
Cao, X., Yang, J., Gao, Y., Wang, Q., & Shen, D. G. (2018) · IEEE Transactions on Image Processing · doi:10.1109/tip.2018.2820424 · Cited by 51
Self-citation

Show all 10 references

Sorted by citations

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
AI in Medical Imaging Informatics: Current Challenges and Future Directions
Panayides, A. S., Amini, A., Filipovic, N. D., Sharma, A., Tsaftaris, S. A., Young, A., Foran, D., Do, N., Golemati, S., Kurc, T., et al. (2020) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2020.2991043 · Cited by 713
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
A review of multimodal image matching: Methods and applications
Jiang, X., Ma, J., Xiao, G., Shao, Z., & Guo, X. (2021) · Information Fusion · doi:10.1016/j.inffus.2021.02.012 · Cited by 561
CycleMorph: Cycle consistent unsupervised deformable image registration
Kim, B., Kim, D. H., Park, S. H., Kim, J., Lee, J., & Ye, J. C. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.102036 · Cited by 346
Unsupervised Misaligned Infrared and Visible Image Fusion via Cross-Modality Image Generation and Registration
Wang, D., Liu, J., Fan, X., & Liu, R. (2022) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2022/487 · Cited by 374
Infrared and Visible Image Fusion: From Data Compatibility to Task Adaption
Liu, J., Wu, G., Liu, Z., Wang, D., Jiang, Z., Ma, L., Zhong, W., Fan, X., & Liu, R. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3521416 · Cited by 238
Multi-Modal Remote Sensing Image Matching Considering Co-Occurrence Filter
Yao, Y., Zhang, Y., Wan, Y., Liu, X., Yan, X., & Li, J. (2022) · IEEE Transactions on Image Processing · doi:10.1109/tip.2022.3157450 · Cited by 185
Unsupervised Multi-Modal Image Registration via Geometry Preserving Image-to-Image Translation
Arar, M., Ginger, Y., Danon, D., Bermano, A. H., & Cohen‐Or, D. (2020) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr42600.2020.01342 · Cited by 194
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
Adversarial learning for mono- or multi-modal registration
Fan, J., Cao, X., Wang, Q., Yap, P., & Shen, D. G. (2019) · Medical Image Analysis · doi:10.1016/j.media.2019.101545 · Cited by 174
Self-citation
Recent Advancements and Future Prospects in Active Deep Learning for Medical Image Segmentation and Classification
Mahmood, T., Rehman, A. U., Saba, T., Nadeem, L., & Bahaj, S. A. (2023) · IEEE Access · doi:10.1109/access.2023.3313977 · Cited by 160
Deep Learning in Medical Image Registration
Chen, X., Diaz-Pinto, A., Ravikumar, N., & Frangi, A. (2020) · Progress in Biomedical Engineering · doi:10.1088/2516-1091/abd37c · Cited by 106
DiffuseMorph: Unsupervised Deformable Image Registration Using Diffusion Model
Kim, B., Han, I., & Ye, J. C. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19821-2_20 · Cited by 102
Adversarial Image Registration with Application for MR and TRUS Image Fusion
Yan, P., Xu, S., Rastinehad, A. R., & Wood, B. J. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-00919-9_23 · Cited by 100
Review and recommendations on deformable image registration uncertainties for radiotherapy applications
Nenoff, L., Amstutz, F., Murr, M., Archibald‐Heeren, B. R., Fusella, M., Hussein, M., Lechner, W., Zhang, Y., Sharp, G., & Osorio, E. M. V. (2023) · Physics in Medicine and Biology · doi:10.1088/1361-6560/ad0d8a · Cited by 99
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
Improving Misaligned Multi-Modality Image Fusion With One-Stage Progressive Dense Registration
Wang, D., Liu, J., Ma, L., Liu, R., & Fan, X. (2024) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2024.3412743 · Cited by 82
A Coarse-to-Fine Deformable Transformation Framework for Unsupervised Multi-Contrast MR Image Registration with Dual Consistency Constraint
Huang, W., Yang, H., Liu, X., Li, C., Zhang, I., Wang, R., Zheng, H., & Wang, S. (2021) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2021.3059282 · Cited by 81
A review of deep learning-based three-dimensional medical image registration methods
Xiao, H., Teng, X., Liu, C., Li, T., Ren, G., Yang, R., Shen, D., & Cai, J. (2021) · Quantitative Imaging in Medicine and Surgery · doi:10.21037/qims-21-175 · Cited by 77
Self-citation
Artificial Intelligence in Radiation Therapy
Fu, Y., Zhang, H., Morris, E. D., Glide‐Hurst, C. K., Pai, S., Traverso, A., Wee, L., Hadžić, I., Lønne, P.-I., Shen, C., et al. (2021) · IEEE Transactions on Radiation and Plasma Medical Sciences · doi:10.1109/trpms.2021.3107454 · Cited by 75
Adversarial Uni- and Multi-modal Stream Networks for Multimodal Image Registration
Xu, Z., Luo, J., Yan, J., Pulya, R., Li, X., Wells, W. M., & Jayender, J. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-59716-0_22 · Cited by 69
Deformable MR-CT image registration using an unsupervised, dual-channel network for neurosurgical guidance
Han, R., Jones, C. K., Lee, J., Wu, P., Vagdargi, P., Uneri, A., Helm, P. A., Luciano, M., Anderson, W. S., & Siewerdsen, J. H. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.102292 · Cited by 66
Multimodal imaging and machine learning to enhance microscope images of shale
Anderson, T. I., Vega, B., & Kovscek, A. R. (2020) · Computers & Geosciences · doi:10.1016/j.cageo.2020.104593 · Cited by 63
Machine learning for radiomics-based multimodality and multiparametric modeling
Wei, L., Osman, S. O. S., Hatt, M., & Naqa, I. E. (2019) · The Quarterly Journal of Nuclear Medicine and Molecular Imaging · doi:10.23736/s1824-4785.19.03213-8 · Cited by 64
AMNet: Adaptive multi-level network for deformable registration of 3D brain MR images
Che, T., Wang, X., Zhao, K., Zhao, Y., Zeng, D., Li, Q., Zheng, Y., Yang, N., Wang, J., & Li, S. (2023) · Medical Image Analysis · doi:10.1016/j.media.2023.102740 · Cited by 52
Recent Applications of Artificial Intelligence in Radiotherapy: Where We Are and Beyond
Santoro, M., Strolin, S., Paolani, G., Gala, G. D., Bartoloni, A., Giacometti, C., Ammendolia, I., MORGANTI, A. G., & Strigari, L. (2022) · Applied Sciences · doi:10.3390/app12073223 · Cited by 51
Overview of Noninterpretive Artificial Intelligence Models for Safety, Quality, Workflow, and Education Applications in Radiology Practice
Tadavarthi, Y., Makeeva, V., Wagstaff, W., Zhan, H. T., Podlasek, A., Bhatia, N. K., Heilbrun, M. E., Krupinski, E. A., Safdar, N. M., Banerjee, I., et al. (2022) · Radiology Artificial Intelligence · doi:10.1148/ryai.210114 · Cited by 50
Artificial intelligence in radiotherapy: a technological review
Sheng, K. (2020) · Frontiers of Medicine · doi:10.1007/s11684-020-0761-1 · Cited by 44
MASS: Modality-collaborative semi-supervised segmentation by exploiting cross-modal consistency from unpaired CT and MRI images
Chen, X., Zhou, H.-Y., Liu, F., Guo, J., Wang, L., & Yu, Y. (2022) · Medical Image Analysis · doi:10.1016/j.media.2022.102506 · Cited by 41
Self-citation
An application independent review of multimodal 3D registration methods
Saiti, E., & Theoharis, T. (2020) · Computers & Graphics · doi:10.1016/j.cag.2020.07.012 · Cited by 42
Anatomically constrained and attention-guided deep feature fusion for joint segmentation and deformable medical image registration
Khor, H. G., Ning, G., Sun, Y., Xu, L., Zhang, X., & Liao, H. (2023) · Medical Image Analysis · doi:10.1016/j.media.2023.102811 · Cited by 41
Application of Image Fusion in Diagnosis and Treatment of Liver Cancer
Li, C., & Zhu, A. X. (2020) · Applied Sciences · doi:10.3390/app10031171 · Cited by 38
Geometry-Consistent Adversarial Registration Model for Unsupervised Multi-Modal Medical Image Registration
Liu, Y., Wang, W., Li, Y., Lai, H., Huang, S., & Yang, X. N. (2023) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2023.3270199 · Cited by 37
Joint Registration and Segmentation via Multi-Task Learning for Adaptive Radiotherapy of Prostate Cancer
Elmahdy, M. S., Beljaards, L., Yousefi, S., Sokooti, H., Verbeek, F. J., van der Heide, U. A., & Staring, M. (2021) · IEEE Access · doi:10.1109/access.2021.3091011 · Cited by 41
Artificial general intelligence for radiation oncology
Liu, C., Liu, Z., Holmes, J., Zhang, L., Zhang, L., Ding, Y., Shu, P., Wu, Z., Dai, H., Li, Y., et al. (2023) · Meta-Radiology · doi:10.1016/j.metrad.2023.100045 · Cited by 39
Self-citation
CoCycleReg: Collaborative cycle-consistency method for multi-modal medical image registration
Lian, C., Li, X., Kong, L., Wang, J., Zhang, W., Huang, X., & Wang, L. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.05.113 · Cited by 33
Self-citation
MR to ultrasound image registration with segmentation‐based learning for HDR prostate brachytherapy
Chen, Y., Xing, L., Yu, L., Liu, W., Fahimian, B., Niedermayr, T. R., Bagshaw, H. P., Buyyounouski, M. K., & Han, B. (2021) · Medical Physics · doi:10.1002/mp.14901 · Cited by 32
NBR-Net: A Nonrigid Bidirectional Registration Network for Multitemporal Remote Sensing Images
Xu, Y., Jun, L., Du, C., & Chen, H. (2022) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2022.3162094 · Cited by 32
Image Registration in Medical Robotics and Intelligent Systems: Fundamentals and Applications
Liu, J., Singh, G., Alaref, S. J., Lee, B., Oleru, O. O., Min, J. K., Dunham, S. N., Sabuncu, M. R., & Mosadegh, B. (2019) · Advanced Intelligent Systems · doi:10.1002/aisy.201900048 · Cited by 29
Deep learning for improving PET/CT attenuation correction by elastic registration of anatomical data
Schaefferkoetter, J. D., Shah, V. P., Hayden, C., Prior, J. O., & Zuehlsdorff, S. (2023) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-023-06181-9 · Cited by 28
Deep Group-Wise Registration for Multi-Spectral Images From Fundus Images
Che, T., Zheng, Y., Cong, J., Jiang, Y., Niu, Y., Jiao, W., Zhao, B., & Ding, Y. (2019) · IEEE Access · doi:10.1109/access.2019.2901580 · Cited by 27
Modal-invariant progressive representation for multimodal image registration
Ding, J., Zhao, Y., Pei, L., Shan, Y., Du, Y., & Li, W. (2024) · Information Fusion · doi:10.1016/j.inffus.2024.102903 · Cited by 23
MultiGradICON: A Foundation Model for Multimodal Medical Image Registration
Demir, B., Tian, L., Greer, H., Kwitt, R., Vialard, F., Estépar, R. S. J., Sylvain Bouix, A., Rushmore, J., Ebrahim, E., & Niethammer, M. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73480-9_1 · Cited by 22
Transfer Learning for Nonrigid 2D/3D Cardiovascular Images Registration
Guan, S., Wang, T., Sun, K., & Cai, M. (2020) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2020.3045977 · Cited by 22
A review on 3D deformable image registration and its application in dose warping
Xiao, H., Ren, G., & Cai, J. (2020) · Radiation Medicine and Protection · doi:10.1016/j.radmp.2020.11.002 · Cited by 20
A variational Bayesian method for similarity learning in non-rigid image registration
Grzech, D., Azampour, M. F., Glocker, B., Schnabel, J. A., Navab, N., Kainz, B., & Folgoc, L. L. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.00022 · Cited by 19
A Transformer-Based Network for Deformable Medical Image Registration
Wang, Y., Qian, W., Li, M., & Zhang, X. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-20497-5_41 · Cited by 19
An artificial‐intelligence‐based age‐specific template construction framework for brain structural analysis using magnetic resonance images
Gu, D., Shi, F., Hua, R., Wei, Y., Li, Y., Zhu, J., Zhang, W., Zhang, H., Yang, Q., Huang, P., et al. (2022) · Human Brain Mapping · doi:10.1002/hbm.26126 · Cited by 17
Self-citation
AU-Net: Adaptive Unified Network for Joint Multi-Modal Image Registration and Fusion
Lu, M., Jiang, M., Tao, X., & Kong, J. (2025) · IEEE Transactions on Image Processing · doi:10.1109/tip.2025.3586507 · Cited by 15
Multitask Weakly Supervised Generative Network for MR-US Registration
Azampour, M. F., Mach, K., Fatemizadeh, E., Demiray, B., Westenfelder, K. M., Steiger, K., Eiber, M., Wendler, T., Kainz, B., & Navab, N. (2024) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2024.3400899 · Cited by 15
Data-Efficient Unsupervised Interpolation Without Any Intermediate Frame for 4D Medical Images
Kim, J., Yoon, H., Park, G., Kim, K., & Yang, E. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.01079 · Cited by 15
DGR-Net: Deep Groupwise Registration of Multispectral Images
Che, T., Zheng, Y., Sui, X., Jiang, Y., Cong, J., Jiao, W., & Zhao, B. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-20351-1_55 · Cited by 14
A deep learning approach for automatic tumor delineation in stereotactic radiotherapy for non-small cell lung cancer using diagnostic PET-CT and planning CT
Yu, X., He, L. L., Wang, Y., Dong, Y., Song, Y., Yuan, Z., Yan, Z., & Wang, W. (2023) · Frontiers in Oncology · doi:10.3389/fonc.2023.1235461 · Cited by 13
Unsupervised registration for liver CT-MR images based on the multiscale integrated spatial-weight module and dual similarity guidance
Zhou, Z., Wang, S., Hu, J., Liu, A., Qian, X., Chen, G., Zheng, J., Chen, G., Ji, J., & Dai, Y. (2023) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2023.102260 · Cited by 12
2D-to-3D image translation of complex nanoporous volumes using generative networks
Anderson, T. I., Vega, B., Mckinzie, J., Aryana, S. A., & Kovscek, A. R. (2021) · Scientific Reports · doi:10.1038/s41598-021-00080-5 · Cited by 12
Weak texture remote sensing image matching based on hybrid domain features and adaptive description method
Yang, W., Yao, Y., Zhang, Y., & Wan, Y. (2023) · The Photogrammetric Record · doi:10.1111/phor.12464 · Cited by 12
Robust One-Stop Multi-Modality Image Registration-Fusion-Segmentation Framework Against Misalignments and Adversarial Attacks
Wang, D., Jiao, X., Liu, J., & Fan, X. (2025) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2025.3535291 · Cited by 11
Unsupervised Image Registration towards Enhancing Performance and Explainability in Cardiac and Brain Image Analysis
Wang, C., Yang, G., & Papanastasiou, G. (2022) · Sensors · doi:10.3390/s22062125 · Cited by 11
General cross-modality registration framework for visible and infrared UAV target image registration
Luo, Y., Cha, H., Zuo, L., Cheng, P., & Zhao, Q. (2023) · Scientific Reports · doi:10.1038/s41598-023-39863-3 · Cited by 10
PET/CT-based 3D multi-class semantic segmentation of ovarian cancer and the stability of the extracted radiomics features
Sadeghi, M. H., Sina, S., Alavi, M., Giammarile, F., & Yeong, C. H. (2024) · Physical and Engineering Sciences in Medicine · doi:10.1007/s13246-024-01485-y · Cited by 10
Deep multispectral image registration network
Sui, X., Zheng, Y., Jiang, Y., Jiao, W., & Ding, Y. (2020) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2020.101815 · Cited by 10
UTR: A UNet-like transformer for efficient unsupervised medical image registration
Qiu, W., Xiong, L., Li, N., Wang, Y., & Zhang, Y. (2024) · Image and Vision Computing · doi:10.1016/j.imavis.2024.105209 · Cited by 10
A Novel Computer-Assisted System for Long Bone Fracture Reduction With a Hexapod External Fixator
Liu, C., Liu, S., Wang, Y., Fu, X., & Sun, T. (2024) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2024.3494756 · Cited by 9
Firefly-Based Approaches of Image Recognition
COCIANU, C. L., Stan, A., & Avramescu, M. (2020) · Symmetry · doi:10.3390/sym12060881 · Cited by 9
Unsupervised Multi-modal Medical Image Registration via Invertible Translation
Guo, M. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72751-1_2 · Cited by 8
W-NetPan: Double-U network for inter-sensor self-supervised pan-sharpening
Fernández-Beltrán, R., Fernandez, R., Kang, J. S., & Pla, F. (2023) · Neurocomputing · doi:10.1016/j.neucom.2023.02.002 · Cited by 7
Deep learning-based medical image registration
Cao, X., Xue, P., Fan, J., Liu, D., Sun, K., Xue, Z., & Shen, D. G. (2024) · Elsevier eBooks · doi:10.1016/b978-0-32-385124-4.00023-4 · Cited by 7
Self-citation
Two-Stage Unsupervised Learning Method for Affine and Deformable Medical Image Registration
Gu, D., Liu, G., Tian, J., & Zhan, Q. (2019) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2019.8803794 · Cited by 7
Unimodal Cyclic Regularization For Training Multimodal Image Registration Networks
Xu, Z., Yan, J., Luo, J., Wells, W. M., Li, X., & Jayender, J. (2021) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi48211.2021.9433926 · Cited by 7
A Dual-Branch Network of Strip Convolution and Swin Transformer for Multimodal Remote Sensing Image Registration
Mu, K., Wang, W., Liu, H., Liang, L., & Zhang, S. (2025) · Remote Sensing · doi:10.3390/rs17061071 · Cited by 6
Attention-enhanced Dual-stream Registration Network via Mixed Attention Transformer and Gated Adaptive Fusion
Chang, Y., & Li, Z. (2025) · Medical Image Analysis · doi:10.1016/j.media.2025.103713 · Cited by 6
Cyclic deformable medical image registration with prompt: deep fusion of diffeomorphic and transformer methods
Li, L., Li, L., Zhang, Y., Bao, F., Yao, X., & Zhang, C. (2025) · Applied Intelligence · doi:10.1007/s10489-025-06232-8 · Cited by 6
Multidisciplinary Approach of Artificial Intelligence over Medical Imaging: A Review, Challenges, Recent Opportunities for Research
Tiwari, A. S., Chaudhari, M., & Rai, A. (2019) · International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC) · doi:10.1109/i-smac47947.2019.9032566 · Cited by 6
An Unsupervised Deep Learning Method for Diffeomorphic Mono-and Multi-modal Image Registration
Theljani, A., & Chen, K. (2020) · Communications in computer and information science · doi:10.1007/978-3-030-39343-4_27 · Cited by 6
Multi-temporal Registration of Environmental Imagery Using Affine Invariant Convolutional Features
Khan, A. A., Ulhaq, A., & Robinson, R. W. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-34879-3_21 · Cited by 5
DPCN++: Differentiable Phase Correlation Network for Versatile Pose Registration
Chen, Z., Liao, Y., Du, H., Zhang, H., Xu, X., Lu, H., Xiong, R., & Wang, Y. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3317501 · Cited by 5
An Unsupervised Approach for 3D Medical Image Registration
Ma, Y., Zhang, J., Niu, D., Hassan, M. U., & Zhao, X. (2019) · International Conference on Information Technology: IoT and Smart City (ICIT) · doi:10.1145/3377170.3377273 · Cited by 5
Artificial Intelligence in Radiation Therapy
Sumida, I. (2022) · Institute of Physics eBooks · doi:10.1088/978-0-7503-3339-9 · Cited by 5
End-to-End Multi-Slice-to-Volume Concurrent Registration and Multimodal Generation
Leroy, A., Lerousseau, M., Henry, T., Cafaro, A., Nikos Paragios, A., Grégoire, V., & Deutsch, É. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-16446-0_15 · Cited by 5
Unsupervised deep learning for geometric feature detection and multilevel-multimodal image registration
Lajili, M., Belhachmi, Z., Moakher, M., & Theljani, A. (2024) · Applied Intelligence · doi:10.1007/s10489-024-05585-w · Cited by 4
Enhancing 3D medical image registration with cross attention, residual skips, and cascade attention
Anwar, M., He, Z., & Cao, W. (2024) · Intelligent Data Analysis · doi:10.3233/ida-230692 · Cited by 4
Segmentation outperforms registration in quantitative analysis of brain iron
He, J., Fu, B., Wang, B., Xiong, Z., Peng, Y., & Wang, R. (2025) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2024.107446 · Cited by 4
Neonatal White Matter Damage Analysis Using DTI Super-Resolution and Multi-Modality Image Registration
Wang, Y., Zhang, Y., Ma, C., Wang, R., Guo, Z., Shen, Y., Wang, M., & Meng, H. (2023) · International Journal of Neural Systems · doi:10.1142/s0129065724500011 · Cited by 4
CrossModalNet: exploiting quality preoperative images for multimodal image registration
Sun, J., Liu, C., Li, C., Lu, Z., He, M., Gao, L., Lin, T., Sui, J., Xie, K., & Ni, X. (2021) · Physics in Medicine and Biology · doi:10.1088/1361-6560/ac195e · Cited by 4
FAMAFuse: Functional-Anatomical Multiscale Attention for Multimodal Image Fusion
Kamara, A. A., He, S., & Fofanah, A. J. (2025) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2025.3626562 · Cited by 3
Advances in TEE-Centric Intraprocedural Multimodal Image Guidance for Congenital and Structural Heart Disease
Zhang, X., Gosnell, J. M., Nainamalai, V., Page, S., Huang, S., Haw, M. P., Peng, B., Vettukattil, J. J., & Jiang, J. (2023) · Diagnostics · doi:10.3390/diagnostics13182981 · Cited by 3
Dual-pyramid framework for robust infrared-visible image registration and fusion via convolutional dictionary learning
Wu, C., & Li, Z. (2025) · The Visual Computer · doi:10.1007/s00371-025-04183-2 · Cited by 3
Unpaired Cross-Modal Interaction Learning for COVID-19 Segmentation on Limited CT Images
Guan, Q., Xie, Y., Yang, B., Zhang, J., Liao, Z., Wu, Q., & Xia, Y. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-43898-1_58 · Cited by 3
Intraoperative CT Augmentation for Needle-Based Liver Interventions
Hadramy, S. E., Verde, J. M., Padoy, N., & Cotin, S. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-43996-4_28 · Cited by 3
Fusion-Based Multimodal Medical Image Registration Combining Inter-Modality Metric and Disentanglement
Ji, Y., Zhu, Z., & Wei, Y. (2022) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi52829.2022.9761527 · Cited by 3
Communal Domain Learning for Registration in Drifted Image Spaces
Mansoor, A., & Linguraru, M. G. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-32692-0_55 · Cited by 3
Modern Approaches for Thoracic Image Registration and Respiratory Motion Management in Oncology
Jank, E., Liu, E. Y., Delery, W., Boyle, P., Miller, C. R., Andosca, R., Naumann, L., Upadhyay, R., Kadambi, A., Low, D. A., et al. (2025) · Radiology Imaging Cancer · doi:10.1148/rycan.250023 · Cited by 2
Improving data-driven gated (DDG) PET and CT registration in thoracic lesions: a comparison of AI registration and DDG CT
Pan, T., Thomas, M. A., Lü, Y., & Luo, D. (2025) · EJNMMI Physics · doi:10.1186/s40658-025-00797-6 · Cited by 2
A context-adaptive and dual-dimensional collaboration method for infrared-visible image registration and fusion
Jiang, M., Gao, S., Zhang, X., Wang, X., Hu, Q., & Wang, W. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.112684 · Cited by 2
Self-supervised Probe Pose Regression via Optimized Ultrasound Representations for US-CT Fusion
Azampour, M. F., Velikova, Y., Fatemizadeh, E., Dakua, S. P., & Navab, N. (2024) · Lecture notes in electrical engineering · doi:10.1007/978-981-97-1335-6_11 · Cited by 2
MA-VoxelMorph: Multi-scale attention-based VoxelMorph for nonrigid registration of thoracoabdominal CT images
Huang, Q., Ren, L., Quan, T., Yang, M., Yuan, H., & Cao, K. (2024) · Journal of Innovative Optical Health Sciences · doi:10.1142/s1793545824500226 · Cited by 2
Exploring the Prospect of Enhancing Cancer Radiotherapy in Hospitals and Health Care Centers in Nigeria Through Artificial Intelligence: A Promising Frontier
Momoh, H., Ibrahim, H. A., Abdulmalik, M. O., & Bello, A. I. (2024) · Journal of applied science and environmental management · doi:10.4314/jasem.v28i4.8 · Cited by 2
New Attempts in Solving Image Recognition Tasks
COCIANU, C. L., Stan, A., & Avramescu, M. (2019) · Communications in computer and information science · doi:10.1007/978-3-030-30275-7_36 · Cited by 2

Authors: Cao, X., Yang, J., Wang, L., Xue, Z., Wang, Q., & Shen, D.
Affiliations: University of North Carolina at Chapel Hill (US)
Northwestern Polytechnical University (CN)
United Imaging Intelligence (China) (CN)
Shanghai Jiao Tong University (CN)
Corresponding author: Shen, Dinggang
ORCID: Cao, Xiaohuan
Wang, Li
Xue, Zhong
Wang, Qian
Shen, Dinggang
Venue: Lecture Notes in Computer Science
Publisher: Springer International Publishing
Published: 2018
Volume: 11046
Pages: 55-63
ISSN: 0302-9743 (ISSN-L)
1611-3349
DOI: 10.1007/978-3-030-00919-9_7
PMC: PMC6516490
OpenAlex: W2798741430
Cited by: 123
References: 10
Citations by year: 2026: 7 · 2025: 16 · 2024: 19 · 2023: 21 · 2022: 18 · 2021: 14 · 2020: 16 · 2019: 11 · 2018: 1
FWCI: 3.16
Type: conference paper
Language: en
Open access: green
www.ncbi.nlm.nih.gov
Licence: www.springer.com/tdm (tdm)
www.springer.com/tdm (vor)
Locations: Lecture notes in computer science (publishedVersion)
PubMed (publishedVersion)
Europe PMC (PubMed Central) (acceptedVersion)
PubMed Central (acceptedVersion)
arXiv (Cornell University) (submittedVersion)
arXiv (Cornell University)
Funders: National Institute on Aging (RF1AG053867)
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 › Engineering › Biomedical Engineering › Medical Imaging and Analysis
Keywords: non-rigid registration, CT-MRI registration, paired training data, multimodal image fusion, deep learning registration

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.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Deep Learning Based Inter-modality Image Registration Supervised by Intra-modality Similarity

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks