Adversarial Image Registration with Application for MR and TRUS Image Fusion
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Complex visual patterns can be encoded over two input imaging modalities (3D MR and TRUS) without explicit labels in unsupervised mode.
“In unsupervised mode, it is possible to encode complex visual patterns over two input imaging modalities (3D MR and TRUS) without the need for explicit labels [144].”
From A survey on deep multimodal learning for computer vision: advances, trends, applications, and datasets (2022) · cites this paper as [144]
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474 · see citing works
(2017) · Cited by 11,625 · see citing works
Learning to compare image patches via convolutional neural networks
Zagoruyko, S., & Nikos Komodakis, A. (2015) · 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/cvpr.2015.7299064 · Cited by 1,540 · see citing works
Siddiqui, M. M., Rais‐Bahrami, S., Türkbey, B., George, A. K., Rothwax, J. T., Shakir, N. A., Okoro, C., Raskolnikov, D., Parnes, H. L., Linehan, W. M., et al. (2015) · JAMA · doi:10.1001/jama.2014.17942 · Cited by 1,503 · see citing works
Self-citationA 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 · see citing works
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 · see citing works
A Deep Metric for Multimodal Registration
Simonovsky, M., Gutiérrez-Becker, B., Mateus, D., Navab, N., & Nikos Komodakis, A. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46726-9_2 · Cited by 224 · see citing works
Label-driven weakly-supervised learning for multimodal deformarle image registration
Hu, Y., Marc Modat, A., Gibson, E., Ghavami, N., Bonmati, E., Moore, C. M., Emberton, M., Noble, J. A., Barratt, D. C., & Vercauteren, T. K. (2018) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2018.8363756 · Cited by 148 · see citing works
Deep Learning Based Inter-modality Image Registration Supervised by Intra-modality Similarity
Cao, X., Yang, J., Wang, L., Xue, Z., Wang, Q., & Shen, D. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-00919-9_7 · Cited by 123 · see citing works
Learning-Based Multimodal Image Registration for Prostate Cancer Radiation Therapy
Cao, X., Gao, Y., Yang, J., Guorong Wu, A., & Shen, D. G. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46726-9_1 · Cited by 28 · see citing works
Flagged first, then by citations
1 of the 11 references couldn’t be matched to a known paper.
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Generative adversarial network in medical imaging: A review
Xin, Y., Walia, E., & Babyn, P. (2019) · Medical Image Analysis · doi:10.1016/j.media.2019.101552 · Cited by 1,883 · 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
GANs for medical image analysis
Kazeminia, S., Baur, C., Kuijper, A., van Ginneken, B., Navab, N., Albarqouni, S., & Mukhopadhyay, A. (2020) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2020.101938 · Cited by 548 · see citing works
A review of multimodal image matching: Methods and applications
Jiang, X., Ma, J.-Y., Xiao, G.-B., Shao, Z.-F., & Guo, X.-J. (2021) · Information Fusion · doi:10.1016/j.inffus.2021.02.012 · Cited by 476 · see citing works
Bayoudh, K., Knani, R., Hamdaoui, F., & Mtibaa, A. (2022) · The Visual Computer · doi:10.1007/s00371-021-02166-7 · Cited by 456 · see citing works
Synthetic Data for Deep Learning
Nikolenko, S. (2021) · Springer optimization and its applications · doi:10.1007/978-3-030-75178-4 · Cited by 434 · see citing works
Going Deep in Medical Image Analysis: Concepts, Methods, Challenges, and Future Directions
Altaf, F., Islam, S. M. S., Akhtar, N., & Janjua, N. K. (2019) · IEEE Access · doi:10.1109/access.2019.2929365 · Cited by 293 · 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
Chen, J., Liu, Y., Wei, S., Bian, Z., Subramanian, S., Carass, A., Prince, J. L., & Du, Y. (2024) · Medical Image Analysis · doi:10.1016/j.media.2024.103385 · Cited by 188 · see citing works
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 169 · see citing works
Deep Learning: An Update for Radiologists
Cheng, P. M., Montagnon, E., Yamashita, R., Pan, I., Cadrin-Chênevert, A., Romero, F., Chartrand, G., Kadoury, S., & Tang, A. (2021) · Radiographics · doi:10.1148/rg.2021200210 · Cited by 170 · see citing works
Advances in Deep Learning-Based Medical Image Analysis
Liu, X., Gao, K., Liu, B., Pan, C., Liang, K., Yan, L., Ma, J., He, F., Zhang, S., Pan, S., et al. (2021) · Health Data Science · doi:10.34133/2021/8786793 · Cited by 175 · see citing works
Applications and limitations of machine learning in radiation oncology
Jarrett, D. P., Stride, E., Vallis, K. A., & Gooding, M. J. (2019) · British Journal of Radiology · doi:10.1259/bjr.20190001 · Cited by 164 · see citing works
Mahmood, T., Rehman, A. U., Saba, T., Nadeem, L., & Bahaj, S. A. (2023) · IEEE Access · doi:10.1109/access.2023.3313977 · Cited by 155 · see citing works
LungRegNet: An unsupervised deformable image registration method for 4D‐CT lung
Fu, Y., Lei, Y., Wang, T., Higgins, K. A., Bradley, J. D., Curran, W. J., Liu, T., & Yang, X. (2020) · Medical Physics · doi:10.1002/mp.14065 · Cited by 131 · see citing works
Cross-modal attention for multi-modal image registration
Song, X., Chao, H., Xu, X., Guo, H., Xu, S., Türkbey, B., Wood, B. J., Sanford, T., Wang, G., & Yan, P. (2022) · Medical Image Analysis · doi:10.1016/j.media.2022.102612 · Cited by 133 · see citing works
Self-citationA 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
“(115) GAN-loss Prostate MR-TRUS 3D Wasserstein GAN TRE, D-score MLMI 2018”
Li, X., Jiang, Y., Rodriguez-Andina, J. J., Luo, H., Yin, S., & Kaynak, O. (2021) · Discover Artificial Intelligence · doi:10.1007/s44163-021-00006-0 · Cited by 91 · see citing works
SymReg-GAN: Symmetric Image Registration with Generative Adversarial Networks
Zheng, Y., Sui, X., Jiang, Y., Che, T., Zhang, S., Yang, J., & Li, H. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3083543 · Cited by 80 · see citing works
Speckle Noise Reduction for OCT Images Based on Image Style Transfer and Conditional GAN
Zhou, Y., Yu, K., Wang, M., Ma, Y., Peng, Y., Chen, Z., Zhu, W., Shi, F., & Chen, X. (2021) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2021.3074852 · Cited by 77 · see citing works
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 · see citing works
Machine learning in prostate MRI for prostate cancer: current status and future opportunities
Li, H., Lee, C. H., Chia, D., Lin, Z., Huang, W., & Tan, C. H. (2022) · Diagnostics · doi:10.3390/diagnostics12020289 · Cited by 74 · see citing works
Fu, Y., Lei, Y., Wang, T., Patel, P., Jani, A. B., Mao, H., Curran, W. J., Liu, T., & Yang, X. (2021) · Medical Image Analysis · doi:10.1016/j.media.2020.101845 · Cited by 75 · see citing works
Cross-Modal Attention for MRI and Ultrasound Volume Registration
Song, X., Guo, H., Xu, X., Chao, H., Xu, S., Türkbey, B., Wood, B. J., Wang, G., & Yan, P. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-87202-1_7 · Cited by 75 · see citing works
Self-citationA survey on mutual information based medical image registration algorithms
Sengupta, D., Gupta, P., & Biswas, A. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.11.023 · Cited by 67 · see citing works
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 · see citing works
Kim, K., Cho, K., Jang, R., Kyung, S., Lee, S., Ham, S., Choi, E., Hong, G.-S., & Kim, N. (2024) · Korean Journal of Radiology · doi:10.3348/kjr.2023.0818 · Cited by 54 · see citing works
A deep learning approach for 2D ultrasound and 3D CT/MR image registration in liver tumor ablation
Wei, W., Xu, H., Alpers, J., Rak, M., & Hansen, C. (2021) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2021.106117 · Cited by 46 · see citing works
Linear and Deformable Image Registration with 3D Convolutional Neural Networks
Christodoulidis, S., Sahasrabudhe, M., Vakalopoulou, M., Chassagnon, G., Revel, M., Mougiakakou, S. G., & Nikos Paragios, A. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-00946-5_2 · Cited by 39 · see citing works
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 40 · see citing works
Review of Medical Image Synthesis using GAN Techniques
Devi, M. K. A. A., & Suganthi, K. (2021) · ITM Web of Conferences · doi:10.1051/itmconf/20213701005 · Cited by 39 · see citing works
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 · see citing works
Real-time multimodal image registration with partial intraoperative point-set data
Baum, Z. M. C., Hu, Y., & Barratt, D. C. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.102231 · Cited by 38 · see citing works
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 · see citing works
Synergizing medical imaging and radiotherapy with deep learning
Shan, H., Jia, X., Yan, P., Li, Y., Paganetti, H., & Wang, G. (2020) · Machine Learning Science and Technology · doi:10.1088/2632-2153/ab869f · Cited by 36 · see citing works
Self-citationWhen Deep Learning Meets Data Alignment: A Review on Deep Registration Networks (DRNs)
Villena-Martínez, V., Oprea, S., Saval-Calvo, M., Azorín-López, J., Fuster-Guilló, A., & Fisher, R. B. (2020) · Applied Sciences · doi:10.3390/app10217524 · Cited by 35 · see citing works
He, Y., Li, T., Yang, G., Kong, Y., Chen, Y., Shu, H., Coatrieux, J.-L., Dillenseger, J., & Li, S. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58523-5_45 · Cited by 32 · see citing works
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 · see citing works
Sarmah, M., Neelima, A., & Singh, H. R. (2023) · Visual Computing for Industry Biomedicine and Art · doi:10.1186/s42492-023-00142-7 · Cited by 32 · see citing works
SDOF-GAN: Symmetric Dense Optical Flow Estimation With Generative Adversarial Networks
Che, T., Zheng, Y., Yang, Y., Hou, S., Jia, W., Yang, J., & Gong, C. (2021) · IEEE Transactions on Image Processing · doi:10.1109/tip.2021.3084073 · Cited by 28 · see citing works
Deep learning‐based motion tracking using ultrasound images
Dai, X., Lei, Y., Roper, J. R., Chen, Y., Bradley, J. D., Curran, W. J., Liu, T., & Yang, X. (2021) · Medical Physics · doi:10.1002/mp.15321 · Cited by 28 · see citing works
A review of deep learning and Generative Adversarial Networks applications in medical image analysis
Sindhura, D. N., Pai, R. M., Bhat, S. N., & Pai, M. M. M. (2024) · Multimedia Systems · doi:10.1007/s00530-024-01349-1 · Cited by 34 · see citing works
Biomedical imaging and analysis through deep learning
Drukker, K., Yan, P., Sibley, A., & Wang, G. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-821259-2.00004-1 · Cited by 23 · see citing works
Self-citationArtificial Intelligence for Image Registration in Radiation Oncology
Teuwen, J., Gouw, Z. A. R., & Sonke, J. (2022) · Seminars in Radiation Oncology · doi:10.1016/j.semradonc.2022.06.003 · Cited by 23 · see citing works
Multi-Task Learning for Registering Images With Large Deformation
Du, B., Jiandong, L., Türkbey, B., & Yan, P. (2020) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2020.3016699 · Cited by 22 · see citing works
Self-citationChen, X., Zhou, B., Xie, H., Guo, X., Zhang, J., Duncan, J. S., Miller, E. J., Sinusas, A. J., Onofrey, J. A., & Liu, C. (2023) · Medical Image Analysis · doi:10.1016/j.media.2023.102840 · Cited by 22 · see citing works
Zhai, Y., Zhang, G., Zheng, L., Yang, G., Zhao, K., Gong, Y., Zhang, Z., Zhang, X., Sun, B., & Wang, Z. (2021) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2021.3072246 · Cited by 20 · see citing works
Meta-Learning Initializations for Interactive Medical Image Registration
Baum, Z. M. C., Hu, Y., & Barratt, D. C. (2022) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2022.3218147 · Cited by 21 · see citing works
A survey on artificial intelligence in pulmonary imaging
Saha, P. K., Nadeem, S. A., & Comellas, A. P. (2023) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.1510 · Cited by 22 · see citing works
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 · see citing works
Unsupervised Cross-Modality Domain Adaptation Network for X-Ray to CT Registration
Zheng, S., Yang, X. N., Wang, Y., Ding, M., & Hou, W. W. (2021) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2021.3135890 · Cited by 18 · see citing works
3D deformable registration of longitudinal abdominopelvic CT images using unsupervised deep learning
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Lu, Y., Li, B., Liu, N., Chen, J., Xiao, L., Gou, S., Chen, L., Huang, M., & Zhuang, J. (2020) · Computational and Mathematical Methods in Medicine · doi:10.1155/2020/4942121 · Cited by 14 · see citing works
Multimodal registration of ultrasound and MR images using weighted self-similarity structure vector
Wang, Y., Fu, T., Wu, C., Xiao, J., Fan, J., Song, H., Liang, P., & Yang, J. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.106661 · Cited by 13 · see citing works
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Hu, X., Yang, J., & Yang, J. (2020) · IEEE Access · doi:10.1109/access.2020.3032612 · Cited by 13 · see citing works
3D Visualization, Skeletonization and Branching Analysis of Blood Vessels in Angiogenesis
Ramakrishnan, V., Schönmehl, R., Artinger, A., Winter, L., Böck, H., Schreml, S., Gürtler, F., Daza, J., Schmitt, V. H., Mamilos, A., et al. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms24097714 · Cited by 13 · see citing works
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 · see citing works
WFUMB Commentary Paper on Artificial intelligence in Medical Ultrasound Imaging
Cui, X. W., Goudie, A., Blaivas, M., Chai, Y. J., Chammas, M. C., Dong, Y. W., Stewart, J. E., Jiang, T.-A., Liang, P., Sehgal, C. M., et al. (2024) · Ultrasound in Medicine & Biology · doi:10.1016/j.ultrasmedbio.2024.10.016 · Cited by 15 · see citing works
Review of Generative Adversarial Networks in mono- and cross-modal biomedical image registration
Han, T., Wu, J., Luo, W., Wang, H., Jin, Z., & Qu, L. (2022) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2022.933230 · Cited by 12 · see citing works
Multiscale feature fusion network for 3D head MRI image registration
Yang, S., Li, H., Chen, S., Huang, W., Liu, D., Ruan, G., Huang, Q., Gong, Q., Liu, L., & Chen, H. (2023) · Medical Physics · doi:10.1002/mp.16387 · Cited by 11 · see citing works
Whole‐brain functional MRI registration based on a semi‐supervised deep learning model
Zhu, Q., Sun, Y., Wu, Y., Zhu, H., Lin, G., Zhou, Y., & Feng, Q. (2021) · Medical Physics · doi:10.1002/mp.14777 · Cited by 10 · see citing works
Mbarki, W., Bouchouicha, M., Tshienda, F. T., Moreau, É., & Sayadi, M. (2021) · Electronics · doi:10.3390/electronics10080982 · Cited by 10 · see citing works
A Narrative Review of Image Processing Techniques Related to Prostate Ultrasound
Wang, H., Wu, H., Wang, Z., Yue, P., Ni, D., Heng, P., & Wang, Y. (2024) · Ultrasound in Medicine & Biology · doi:10.1016/j.ultrasmedbio.2024.10.005 · Cited by 10 · see citing works
Zhu, X., Ding, M., & Zhang, X. (2023) · CAAI Transactions on Intelligence Technology · doi:10.1049/cit2.12159 · Cited by 8 · see citing works
Chen, X., Liu, X., Wu, Y., Wang, Z., & Wang, S. H. (2023) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2023.105279 · Cited by 10 · see citing works
Towards Fully Automatic 2D Us to 3D CT/MR Registration: A Novel Segmentation-Based Strategy
Wei, W., Rak, M., Alpers, J., & Hansen, C. (2020) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi45749.2020.9098379 · Cited by 8 · see citing works
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 · see citing works
CT Scan Registration with 3D Dense Motion Field Estimation Using LSGAN
Anas, E. R., Onsy, A., & Matuszewski, B. J. (2020) · Communications in computer and information science · doi:10.1007/978-3-030-52791-4_16 · Cited by 7 · see citing works
Salient Region Matching for Fully Automated MR-TRUS Registration
Feng, Z., Ni, D., & Wang, Y. (2025) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi60581.2025.10981019 · Cited by 6 · see citing works
Zhou, Z., Hong, B., Qian, X., Hu, J., Shen, M., Ji, J., & Dai, Y. (2023) · BioMedical Engineering OnLine · doi:10.1186/s12938-023-01143-6 · Cited by 6 · see citing works
Affine medical image registration with fusion feature mapping in local and global
Ji, W., & Feng, Y. (2024) · Physics in Medicine and Biology · doi:10.1088/1361-6560/ad2717 · Cited by 7 · see citing works
Adversarial Neural Network Classifiers for COVID-19 Diagnosis in Ultrasound Images
Karar, M. E., Shouman, M. A., & Chalopin, C. (2021) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2022.018564 · Cited by 6 · see citing works
An Unsupervised 3D Image Registration Network for Brain MRI Deformable Registration
Huang, M., Ren, G., Zhang, S., Zheng, Q., & Niu, H. (2022) · Computational and Mathematical Methods in Medicine · doi:10.1155/2022/9246378 · Cited by 7 · see citing works
Linear and Deformable Image Registration with 3D Convolutional Neural NetworksPreprint
Christodoulidis, S., Sahasrabudhe, M., Vakalopoulou, M., Chassagnon, G., Revel, M., Mougiakakou, S. G., & Nikos Paragios, A. (2018) · arXiv (Cornell University) · doi:10.48350/120026 · Cited by 6 · see citing works
Hasan, M. K., Zhu, H., Yang, G., & Yap, C. H. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2025.109719 · Cited by 5 · see citing works
Tumpa, T. R., Gregor, J., Acuff, S. N., & Osborne, D. (2023) · Frontiers in Physics · doi:10.3389/fphy.2023.1123315 · Cited by 5 · see citing works
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 · see citing works
AR-UNet: A Deformable Image Registration Network with Cyclic Training
Zhou, H., Leung, H., & Balaji, B. (2023) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2023.3284215 · Cited by 5 · see citing works
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 · see citing works
Triple-Input-Unsupervised neural Networks for deformable image registration
Qu, L., Wan, W., Guo, K., Liu, Y., Tang, J., Li, X., & Wu, J. (2021) · Pattern Recognition Letters · doi:10.1016/j.patrec.2021.08.032 · Cited by 3 · see citing works
The Successive Next Network as Augmented Regularization for Deformable Brain MR Image Registration
Li, M., Hu, S., Li, G., Zhang, F., Li, J., Yang, Y., Zhang, L., Liu, M., Xu, Y. L., Fu, D., et al. (2023) · Sensors · doi:10.3390/s23063208 · Cited by 3 · see citing works
A Deep Generative Model of Neonatal Cortical Surface Development
Fawaz, A., Williams, L. Z. J., Edwards, A. D., & Robinson, E. C. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-12053-4_35 · Cited by 3 · see citing works
Researches advanced in application of medical image analysis based on deep learning
Hu, X., Guo, Z., Yang, S., & Zheng, K. (2022) · International Conference on Computer, Artificial Intelligence and Control Engineering (CAICE) · doi:10.1117/12.2641098 · Cited by 2 · see citing works
Orlando, N., Edirisinghe, C., Gyacskov, I., Vickress, J., Sachdeva, R., Gómez, J. A., D'Souza, D. P., Velker, V. M., Mendez, L. C., Bauman, G. S., et al. (2023) · Brachytherapy · doi:10.1016/j.brachy.2022.11.011 · Cited by 2 · see citing works
Schmidt, C., & Overhoff, H. M. (2022) · Computer Science Research Notes · doi:10.24132/csrn.3201.23 · Cited by 2 · see citing works
41 Deep learning in radiotherapy in 2019: what role for the medical physicist?
Meyer, P., Noblet, V., Lallement, A., Niederst, C., Jarnet, D., Dehaynin, N., & Mazzara, C. (2019) · Physica Medica · doi:10.1016/j.ejmp.2019.09.122 · Cited by 2 · see citing works
Deep Learning Advances for Brain Tumor Diagnosis and Multimodal MRI Registration
Kothapalle, M. S. R., & Nelakuditi, U. R. (2025) · International Conference on Electronic Circuits and Signaling Technologies (ICECST) · doi:10.1109/icecst66106.2025.11307326 · Cited by 1 · see citing works
Sawkmie, L., Samal, S., & Balabantaray, B. K. (2023) · OITS International Conference on Information Technology (OCIT) · doi:10.1109/ocit59427.2023.10431240 · Cited by 1 · see citing works
Progressive GANomaly: anomaly detection with progressively growing GANs
Madzia-Madzou, D. K., & Kuijf, H. J. (2022) · SPIE Medical Imaging: Image Processing · doi:10.1117/12.2611105 · Cited by 1 · see citing works
Deep Cascade Networks for Single 2D US Slice to 3D CT/MRI Image RegistrationPreprint
Wei, W., Xu, H., Rak, M., & Hansen, C. (2021) · Research Square · doi:10.21203/rs.3.rs-1091982/v1 · Cited by 1 · see citing works
Deep learning for registration of high-resolution 3D brain images
Nazib, A. (2020) · Queensland University of Technology · doi:10.5204/thesis.eprints.204293
Deformable Image Registration Using Attentional Generative Adversarial Networks
Zhou, H. (2021) · Schulich School of Engineering · doi:10.11575/prism/38593
Data-Driven Multi-modal Partial Medical Image Preregistration by Template Space Patch Mapping
Xia, D., Yang, X., van Kaick, O. M., Kin, T., & Igarashi, T. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-16446-0_25
Reverse-Net: Few-Shot Learning with Reverse Teaching for Deformable Medical Image Registration
Zhang, X., Yang, T., Zhao, X., & Yang, A. (2023) · Applied Sciences · doi:10.3390/app13021040
Unsupervised brain MRI image registration based on 3D hybrid ViT and convolutional U-netPreprint
Wang, J., Liu, F., Shu, S., Tao, G., & Zhou, F. (2023) · Research Square · doi:10.21203/rs.3.rs-2898722/v1
Zhang, Y. (2024) · Springer eBooks · doi:10.1007/978-981-19-7603-2_9
Challenging the Robustness of Image Registration Similarity Metrics with Adversarial Attacks
Rexeisen, R., & Jiang, X. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73480-9_9
Kerremans, S., Dobney, W., Miranda, S. F. D. S., Manon, J., Cinelli, I., Parisi, A., Hoey, O. V., & Baatout, S. (2026) · Frontiers in Space Technologies · doi:10.3389/frspt.2026.1741121
- Authors
- Yan, P., Xu, S., Rastinehad, A. R., & Wood, B. J.
- Affiliations
- Rensselaer Polytechnic Institute (US)
- National Institutes of Health (US)
- Icahn School of Medicine at Mount Sinai (US)
- Corresponding author
- Yan, Pingkun
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2018
- Volume
- 11046
- Pages
- 197-204
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2963971125
- Cited by
- 100
- References
- 11
- Citations by year
- 2026: 1 · 2025: 3 · 2024: 10 · 2023: 22 · 2022: 13 · 2021: 26 · 2020: 18 · 2019: 5 · 2018: 2
- FWCI
- 3.84
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Lecture notes in computer science (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Lecture notes in computer science
- Funders
- Nvidia
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Physical Sciences › Engineering › Media Technology › Advanced Image Fusion Techniques
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- multimodal medical image fusion, prostate biopsy, transrectal ultrasound, magnetic resonance imaging, generative adversarial networks
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