Deep learning in medical image registration: a review

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

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

Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

Ren, S., He, K., Girshick, R., & Sun, J. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2577031 · Cited by 56,035 · see citing works

Densely Connected Convolutional Networks

Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.243 · Cited by 48,566 · see citing works

Going deeper with convolutions

Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298594 · Cited by 48,562 · see citing works

Human-level control through deep reinforcement learning

Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller, M., Fidjeland, A. K., Ostrovski, G., et al. (2015) · Nature · doi:10.1038/nature14236 · Cited by 30,357 · see citing works

Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation

Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1179 · Cited by 25,545 · see citing works

Unpaired image-to-image translation using cycle-consistent adversarial networks

Zhu, J.-Y., Park, T., Isola, P., & Efros, A. A. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.244 · Cited by 23,602 · see citing works

Generative Adversarial Nets

Labaca-Castro, R. (2023) · Springer eBooks · doi:10.1007/978-3-658-40442-0_9 · Cited by 21,100 · see citing works

GAN（Generative Adversarial Nets）

淳司, 柴. (2017) · Journal of Japan Society for Fuzzy Theory and Intelligent Informatics · doi:10.3156/jsoft.29.5_177_2 · Cited by 19,268 · see citing works

Mastering the game of Go with deep neural networks and tree search

Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., van den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., et al. (2016) · Nature · doi:10.1038/nature16961 · Cited by 15,612 · see citing works

A survey on deep learning in medical image analysis

Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., van der Laak, J. A. W. M., van Ginneken, B., & Sánchez, C. I. (2017) · Medical Image Analysis · doi:10.1016/j.media.2017.07.005 · Cited by 15,332 · see citing works

Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling

Chung, J., Gulcehre, C., Cho, K., & Bengio, Y. (2014) · arXiv · doi:10.48550/arxiv.1412.3555 · Cited by 11,008 · see citing works

Conditional Generative Adversarial Nets

Mirza, M., & Osindero, S. (2014) · arXiv · doi:10.48550/arxiv.1411.1784 · Cited by 8,863 · see citing works

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 5,618 · see citing works

Spatial transformer networks

Jaderberg, M., Simonyan, K., Zisserman, A., & Kavukcuoglu, K. (2015) · arXiv · doi:10.48550/arxiv.1506.02025 · Cited by 5,137 · see citing works

Deep learning in medical image analysis

Shen, D., Wu, G., & Suk, H.-I. (2017) · Annual Review of Biomedical Engineering · doi:10.1146/annurev-bioeng-071516-044442 · Cited by 4,968 · see citing works

A reproducible evaluation of ANTs similarity metric performance in brain image registration

Avants, B. B., Tustison, N. J., Song, G., Cook, P. A., Klein, A., & Gee, J. C. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2010.09.025 · Cited by 4,796 · see citing works

Generative Adversarial Networks

Goodfellow, I. J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2014) · arXiv · doi:10.48550/arxiv.1406.2661 · Cited by 4,477 · 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

Elastix: a toolbox for intensity-based medical image registration

Klein, S., Staring, M., Murphy, K., Viergever, M. A., & Pluim, J. (2010) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2009.2035616 · Cited by 4,311 · see citing works

StarGAN: Unified Generative Adversarial Networks for Multi-domain Image-to-Image Translation

Choi, Y., Choi, M., Kim, M., Ha, J.-W., Kim, S., & Choo, J. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00916 · Cited by 4,163 · see citing works

InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial NetsPreprint

Chen, X., Duan, Y., Houthooft, R., Schulman, J., Sutskever, I., & Abbeel, P. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1606.03657 · Cited by 3,379 · see citing works

Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation

Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014) · arXiv · doi:10.48550/arxiv.1406.1078 · Cited by 3,317 · see citing works

VoxelMorph: 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 2,199 · see citing works

Attention gated networks: learning to leverage salient regions in medical images

Schlemper, J., Oktay, O., Schaap, M., Heinrich, M., Kainz, B., Glocker, B., & Rueckert, D. (2019) · Medical Image Analysis · doi:10.1016/j.media.2019.01.012 · Cited by 2,014 · see citing works

Diffeomorphic demons: Efficient non-parametric image registration

Vercauteren, T. K., Pennec, X., Aymeric Perchant, B., & Ayache, N. (2008) · NeuroImage · doi:10.1016/j.neuroimage.2008.10.040 · Cited by 1,589 · see citing works

Deep Learning Applications in Medical Image Analysis

Ker, J., Wang, L., Rao, J. P., & Lim, C. C. T. (2017) · IEEE Access · doi:10.1109/access.2017.2788044 · Cited by 1,521 · see citing works

Untitled work

(2011) · Cited by 1,258 · see citing works

The Concave-Convex Procedure

Yuille, A. L., & Rangarajan, A. (2003) · Neural Computation · doi:10.1162/08997660360581958 · Cited by 1,151 · see citing works

Fast free-form deformation using graphics processing units

Marc Modat, A., Ridgway, G. R., Taylor, Z. A., Lehmann, M., Barnes, J., Hawkes, D. J., Fox, N. C., & Sebastien Ourselin, A. (2009) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2009.09.002 · Cited by 1,087 · see citing works

U-Net: Convolutional Networks for Biomedical Image SegmentationPreprint

Ronneberger, O., Fischer, P., & Brox, T. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1505.04597 · Cited by 1,079 · see citing works

A Survey of Actor-Critic Reinforcement Learning: Standard and Natural Policy Gradients

Grondman, I., Buşoniu, L., Lopes, G. A. D., & Babuška, R. (2012) · IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews) · doi:10.1109/tsmcc.2012.2218595 · Cited by 1,042 · see citing works

Medical robotics in computer-integrated surgery

Taylor, R. H., & Stoianovici, D. S. (2003) · IEEE Transactions on Robotics and Automation · doi:10.1109/tra.2003.817058 · Cited by 999 · see citing works

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 · see citing works

Deep learning for pixel-level image fusion: Recent advances and future prospects

Liu, Y., Chen, X., Wang, Z., Wang, Z. J., Ward, R. K., & Wang, X. (2017) · Information Fusion · doi:10.1016/j.inffus.2017.10.007 · Cited by 739 · see citing works

Deep learning in medical imaging and radiation therapy

Sahiner, B., Pezeshk, A. X., Hadjiiski, L. M., Wang, X., Drukker, K., Cha, K. H., Summers, R. M., & Giger, M. L. (2018) · Medical Physics · doi:10.1002/mp.13264 · Cited by 739 · see citing works

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 · see citing works

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

MIND: Modality independent neighbourhood descriptor for multi-modal deformable registration

Heinrich‬, M. P., Jenkinson, M., Bhushan, M., Matin, T., Gleeson, F. V., Brady, M., & Schnabel, J. A. (2012) · Medical Image Analysis · doi:10.1016/j.media.2012.05.008 · Cited by 703 · see citing works

An Unsupervised Learning Model for Deformable Medical Image RegistrationPreprint

Balakrishnan, G., Zhao, A. T., Sabuncu, M. R., Guttag, J. V., & Dalca, A. V. (2018) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 673 · see citing works

A framework for evaluation of deformable image registration spatial accuracy using large landmark point sets

Castillo, R., Castillo, E., Guerra, R., Johnson, V., McPhail, T., Garg, A. K., & Guerrero, T. (2009) · Physics in Medicine and Biology · doi:10.1088/0031-9155/54/7/001 · Cited by 645 · see citing works

Quicksilver: Fast predictive image registration – A deep learning approach

Yang, X., Kwitt, R., Styner, M., & Niethammer, M. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.07.008 · Cited by 630 · cited 1× · see citing works

Advances in Neural Information Processing Systems 15 (NIPS 2002)

Williams, C. K. I., & Shawe-Taylor, J. S. (2002) · Cited by 612 · see citing works

An Unsupervised Learning Model for Deformable Medical Image Registration

Balakrishnan, G., Zhao, A., Sabuncu, M. R., Dalca, A. V., & Guttag, J. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00964 · Cited by 599 · see citing works

A gentle introduction to deep learning in medical image processing

Maier, A., Syben, C., Lasser, T., & Rieß, C. (2019) · Zeitschrift für Medizinische Physik · doi:10.1016/j.zemedi.2018.12.003 · Cited by 519 · see citing works

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 · see citing works

Weakly-supervised convolutional neural networks for multimodal image registration

Hu, Y., Marc Modat, A., Gibson, E., Li, W., Ghavami, N., Bonmati, E., Wang, G., Bandula, S., Moore, C. M., Emberton, M., et al. (2018) · Medical Image Analysis · doi:10.1016/j.media.2018.07.002 · Cited by 460 · 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

Deep Feature Learning for Medical Image Analysis with Convolutional Autoencoder Neural Network

Chen, M., Shi, X., Zhang⋆, Y., Wu, D., & Guizani, M. (2017) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2017.2717439 · Cited by 443 · see citing works

Unsupervised Learning for Fast Probabilistic Diffeomorphic Registration

Dalca, A. V., Balakrishnan, G., Guttag, J. V., & Sabuncu, M. R. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-00928-1_82 · Cited by 396 · see citing works

Accuracy of finite element model‐based multi‐organ deformable image registration

Brock, K. K., Sharpe, M. B., Dawson, L. A., Kim, S. M., & Jaffray, D. A. (2005) · Medical Physics · doi:10.1118/1.1915012 · Cited by 376 · see citing works

Recent Advances in Autoencoder-Based Representation LearningPreprint

Tschannen, M., Bachem, O., & Lučić, M. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1812.05069 · Cited by 359 · see citing works

Nonrigid Image Registration Using Multi-scale 3D Convolutional Neural Networks

Sokooti, H., de Vos, B., Berendsen, F., Lelieveldt, B. P. F., Išgum, I., & Staring, M. (2017) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-66182-7_27 · Cited by 340 · see citing works

Svf-net: learning deformable image registration using shape matching

Rohé, M.-M., Datar, M., Heimann, T., Sermesant, M., & Pennec, X. (2017) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-66182-7_31 · Cited by 330 · see citing works

Unsupervised 3D End-to-End Medical Image Registration With Volume Tweening Network

Zhao, S., Lau, T., Luo, J., Chang, E., & Xu, Y. (2019) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2019.2951024 · Cited by 327 · see citing works

Multi-Scale Deep Reinforcement Learning for Real-Time 3D-Landmark Detection in CT Scans

Ghesu, F.-C., Georgescu, B., Zheng, Y., Grbic, S., Maier, A., Hornegger, J., & Comaniciu, D. (2019) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2017.2782687 · Cited by 310 · see citing works

MRF-Based Deformable Registration and Ventilation Estimation of Lung CT

Heinrich‬, M. P., Jenkinson, M., Brady, M., & Schnabel, J. A. (2013) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2013.2246577 · Cited by 304 · see citing works

Survey on deep learning for radiotherapy

Meyer, P., Noblet, V., Mazzara, C., & Lallement, A. (2018) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2018.05.018 · Cited by 302 · see citing works

A Novel Coarse-to-Fine Scheme for Automatic Image Registration Based on SIFT and Mutual Information

Gong, M., Zhao, S., Jiao, L., Tian, D., & Wang, S. (2013) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2013.2281391 · Cited by 301 · see citing works

MRI‐only based synthetic CT generation using dense cycle consistent generative adversarial networks

Lei, Y., Harms, J. M., Wang, T., Liu, Y., Shu, H. G., Jani, A. B., Curran, W. J., Mao, H. Z., Liu, T., & Yang, X. (2019) · Medical Physics · doi:10.1002/mp.13617 · Cited by 298 · see citing works

Birnet: Brain image registration using dual-supervised fully convolutional networks

Fan, J., Cao, X., Yap, P.-T., & Shen, D. (2019) · Medical Image Analysis · doi:10.1016/j.media.2019.03.006 · Cited by 297 · see citing works

Scalable High-Performance Image Registration Framework by Unsupervised Deep Feature Representations Learning

Guorong Wu, A., Kim, M., Wang, Q., Munsell, B. C., & Shen, D. G. (2015) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2015.2496253 · Cited by 277 · see citing works

Automatic multiorgan segmentation in thorax <scp>CT</scp> images using U‐net‐ <scp>GAN</scp>

Dong, X., Lei, Y., Wang, T., Thomas, M., Tang, L., Curran, W. J., Liu, T., & Yang, X. (2019) · Medical Physics · doi:10.1002/mp.13458 · Cited by 275 · see citing works

Spline-Based Image Registration

Szeliski, R., & Coughlan, J. M. (1997) · International Journal of Computer Vision · doi:10.1023/a:1007996332012 · Cited by 257 · see citing works

Paired cycle‐GAN‐based image correction for quantitative cone‐beam computed tomography

Harms, J. M., Lei, Y., Wang, T., Zhang, R., Zhou, J., Tang, X., Curran, W. J., Liu, T., & Yang, X. (2019) · Medical Physics · doi:10.1002/mp.13656 · Cited by 257 · see citing works

A motion-incorporated reconstruction method for gated PET studies

Qiao, F., Pan, T., Clark, J. W., & Mawlawi, O. R. (2006) · Physics in Medicine and Biology · doi:10.1088/0031-9155/51/15/012 · Cited by 239 · see citing works

Radiological images and machine learning: Trends, perspectives, and prospects

Zhang, Z., & Sejdić, E. (2019) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2019.02.017 · Cited by 236 · see citing works

Robust non-rigid registration through agent-based action learning

Krebs, J., Mansi, T., Delingette, H., Zhang, L., Ghesu, F. C., Miao, S., Maier, A. K., Ayache, N., Liao, R., & Kamen, A. (2017) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-66182-7_40 · Cited by 225 · 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

Reinforcement Learning with Long Short-Term Memory

Bakker, B. (2001) · Cited by 209 · see citing works

Dynamic recurrent neural networks: Theory and applications

Giles, C. L., Kuhn, G. M., & Williams, R. J. (1994) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.1994.8753425 · Cited by 205 · see citing works

Atlas-Based Auto-segmentation of Head and Neck CT Images

Han, X., Hoogeman, M. S., Levendag, P. C., Hibbard, L. S., Teguh, D. N., Voet, P. W. J., Cowen, A. C., & Wolf, T. K. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-85990-1_52 · Cited by 200 · see citing works

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 · see citing works

A Survey of Medical Image Registration on Multicore and the GPU

Shams, R., Sadeghi, P., Kennedy, R., & Hartley, R. (2010) · IEEE Signal Processing Magazine · doi:10.1109/msp.2009.935387 · Cited by 198 · see citing works

Non-rigid image registration using self-supervised fully convolutional networks without training data

Li, H., & Yong Fan, A. (2018) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2018.8363757 · Cited by 194 · see citing works

Deeply supervised 3D fully convolutional networks with group dilated convolution for automatic MRI prostate segmentation

Wang, B., Lei, Y., Tian, S., Wang, T., Liu, Y., Patel, P. R., Jani, A. B., Mao, H. Z., Curran, W. J., Liu, T., et al. (2019) · Medical Physics · doi:10.1002/mp.13416 · Cited by 192 · see citing works

Pulmonary CT Registration Through Supervised Learning With Convolutional Neural Networks

Eppenhof, K. A. J., & Pluim, J. P. W. (2018) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2018.2878316 · Cited by 173 · 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

Deformable medical image registration using generative adversarial networks

Mahapatra, D., Antony, B. J., Sedai, S., & Garnavi, R. (2018) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2018.8363845 · Cited by 165 · see citing works

An Artificial Agent for Anatomical Landmark Detection in Medical Images

Ghesu, F. C., Georgescu, B., Mansi, T., Neumann, D., Joachim Hornegger, A., & Comaniciu, D. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46726-9_27 · Cited by 160 · see citing works

Generating Images with Perceptual Similarity Metrics based on Deep NetworksPreprint

Dosovitskiy, A., & Brox, T. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1602.02644 · Cited by 159 · see citing works

Deep similarity learning for multimodal medical images

Cheng, X., Zhang, L., & Zheng, Y. (2016) · Computer Methods in Biomechanics and Biomedical Engineering Imaging & Visualization · doi:10.1080/21681163.2015.1135299 · Cited by 158 · see citing works

Joint Learning of Motion Estimation and Segmentation for Cardiac MR Image Sequences

Qin, C., Bai, W., Schlemper, J., et al. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-00934-2_53 · Cited by 157 · see citing works

Image registration by local histogram matching

Shen, D. G. (2006) · Pattern Recognition · doi:10.1016/j.patcog.2006.08.012 · Cited by 151 · see citing works

Unsupervised Deformable Registration for Multi-modal Images via Disentangled Representations

Qin, C., Shi, B., Liao, R., Mansi, T., Rueckert, D., & Kamen, A. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-20351-1_19 · Cited by 151 · see citing works

On developing B-spline registration algorithms for multi-core processors

Shackleford, J., Kandasamy, N., & Sharp, G. C. (2010) · Physics in Medicine and Biology · doi:10.1088/0031-9155/55/21/001 · Cited by 148 · see citing works

Synthetic CT generation from non-attenuation corrected PET images for whole-body PET imaging

Dong, X., Wang, T., Lei, Y., Higgins, K. A., Liu, T., Curran, W. J., Mao, H. Z., Nye, J. A., & Yang, X. (2019) · Physics in Medicine and Biology · doi:10.1088/1361-6560/ab4eb7 · Cited by 144 · see citing works

Deep learning-based attenuation correction in the absence of structural information for whole-body positron emission tomography imaging

Dong, X., Lei, Y., Wang, T., Higgins, K. A., Liu, T., Curran, W. J., Mao, H. Z., Nye, J. A., & Yang, X. (2019) · Physics in Medicine and Biology · doi:10.1088/1361-6560/ab652c · Cited by 143 · see citing works

Learning deep similarity metric for 3D MR–TRUS image registration

Haskins, G., Kruecker, J., Kruger, U., Xu, S., Pinto, P. A., Wood, B. J., & Yan, P. (2019) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-018-1875-7 · Cited by 140 · see citing works

A novel improved deep convolutional neural network model for medical image fusion

Xia, K., Yin, H., & Jiang-qiang, W. (2018) · Cluster Computing · doi:10.1007/s10586-018-2026-1 · Cited by 138 · see citing works

Real-Time Deep Pose Estimation With Geodesic Loss for Image-to-Template Rigid Registration

Salehi, S. S. M., Khan, S., Deniz Erdogmus, B., & Gholipour, A. (2018) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2018.2866442 · Cited by 133 · see citing works

Synthetic MRI-aided multi-organ segmentation on male pelvic CT using cycle consistent deep attention network

Dong, X., Lei, Y., Tian, S., Wang, T., Patel, P. R., Curran, W. J., Jani, A. B., Liu, T., & Yang, X. (2019) · Radiotherapy and Oncology · doi:10.1016/j.radonc.2019.09.028 · Cited by 133 · see citing works

Adversarial Similarity Network for Evaluating Image Alignment in Deep Learning Based Registration

Fan, J., Cao, X., Xue, Z., Yap, P., & Shen, D. G. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-00928-1_83 · Cited by 132 · see citing works

Deformable Registration for Dose Accumulation.

Chetty, I., & Rosu-Bubulac, M. (2019) · Seminars in Radiation Oncology · doi:10.1016/j.semradonc.2019.02.002 · Cited by 131 · 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

A novel MRI segmentation method using CNN ‐based correction network for MRI ‐guided adaptive radiotherapy

Fu, Y., Mazur, T. R., Wu, X., Liu, S., Chang, X., Lu, Y., Li, H. H., Kim, H. J., Roach, M. C., Henke, L. E., et al. (2018) · Medical Physics · doi:10.1002/mp.13221 · Cited by 130 · see citing works

Ultrasound prostate segmentation based on multidirectional deeply supervised V‐Net

Lei, Y., Tian, S., He, X., Wang, T., Wang, B., Patel, P. R., Jani, A. B., Mao, H. Z., Curran, W. J., Liu, T., et al. (2019) · Medical Physics · doi:10.1002/mp.13577 · Cited by 129 · see citing works

Real‐time volumetric image reconstruction and 3D tumor localization based on a single x‐ray projection image for lung cancer radiotherapy

Li, R., Jia, X., Lewis, J. H., et al. (2010) · Medical Physics · doi:10.1118/1.3426002 · Cited by 126 · see citing works

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 · 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

Inverse-Consistent Deep Networks for Unsupervised Deformable Image RegistrationPreprint

Zhang, J. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1809.03443 · Cited by 122 · see citing works

Effect of Breathing Motion on Radiotherapy Dose Accumulation in the Abdomen Using Deformable Registration

Velec, M., Moseley, J. L., Eccles, C. L., et al. (2010) · International Journal of Radiation Oncology*Biology*Physics · doi:10.1016/j.ijrobp.2010.05.023 · Cited by 121 · see citing works

Efficient Model Learning Methods for Actor–Critic Control

Grondman, I., Vaandrager, M., Buşoniu, L., et al. (2011) · IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) · doi:10.1109/tsmcb.2011.2170565 · Cited by 120 · see citing works

Real-time Deep Pose Estimation with Geodesic Loss for Image-to-Template Rigid Registration

Salehi, S. S. M., Khan, S., Erdogmus, D., & Gholipour, A. (2018) · arXiv · doi:10.48550/arxiv.1803.05982 · Cited by 118 · see citing works

One-Shot Learning for Deformable Medical Image Registration and Periodic Motion Tracking

Fechter, T., & Baltas, D. (2020) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2020.2972616 · Cited by 116 · see citing works

Automated Segmentation of the Parotid Gland Based on Atlas Registration and Machine Learning: A Longitudinal MRI Study in Head-and-Neck Radiation Therapy

Yang, X., Wu, N., Cheng, G., et al. (2014) · International Journal of Radiation Oncology*Biology*Physics · doi:10.1016/j.ijrobp.2014.08.350 · Cited by 113 · see citing works

Dilated FCN for Multi-Agent 2D/3D Medical Image Registration

Miao, S., Piat, S., Fischer, P., Tuysuzoglu, A., Mewes, P., Mansi, T., & Liao, R. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11576 · Cited by 113 · see citing works

A fast inverse consistent deformable image registration method based on symmetric optical flow computation

Yang, D., Li, H., Low, D. A., Deasy, J. O., & Naqa, I. E. (2008) · Physics in Medicine and Biology · doi:10.1088/0031-9155/53/21/017 · Cited by 111 · see citing works

Training cnns for image registration from few samples with model-based data augmentation

Uzunova, H., Wilms, M., Handels, H., & Ehrhardt, J. (2017) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-66182-7_26 · Cited by 110 · see citing works

FAIM – A ConvNet Method for Unsupervised 3D Medical Image Registration

Kuang, D., & Schmah, T. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-32692-0_74 · Cited by 109 · see citing works

3D–2D image registration for target localization in spine surgery: investigation of similarity metrics providing robustness to content mismatch

De Silva, T., Uneri, A., Ketcha, M. D., Reaungamornrat, S., Kleinszig, G., Vogt, S., Aygun, N., Lo, S.-F., Wolinsky, J.-P., & Siewerdsen, J. H. (2016) · Physics in Medicine &amp; Biology · doi:10.1088/0031-9155/61/8/3009 · Cited by 107 · see citing works

Whole-body PET estimation from low count statistics using cycle-consistent generative adversarial networks

Lei, Y., Dong, X., Wang, T., Higgins, K. A., Liu, T., Curran, W. J., Mao, H. Z., Nye, J. A., & Yang, X. (2019) · Physics in Medicine and Biology · doi:10.1088/1361-6560/ab4891 · Cited by 105 · see citing works

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 · see citing works

4D-CT deformable image registration using multiscale unsupervised deep learning

Lei, Y., Fu, Y., Wang, T., Liu, Y., Patel, P. R., Curran, W. J., Liu, T., & Yang, X. (2020) · Physics in Medicine and Biology · doi:10.1088/1361-6560/ab79c4 · Cited by 100 · see citing works

MRI-based treatment planning for proton radiotherapy: dosimetric validation of a deep learning-based liver synthetic CT generation method

Liu, Y., Lei, Y., Wang, Y., Wang, T., Ren, L., Lin, L., McDonald, M. W., Curran, W. J., Liu, T., Zhou, J., et al. (2019) · Physics in Medicine and Biology · doi:10.1088/1361-6560/ab25bc · Cited by 99 · see citing works

Technical Note: DIRART – A software suite for deformable image registration and adaptive radiotherapy research

Yang, D., Brame, S., Naqa, I. E., et al. (2010) · Medical Physics · doi:10.1118/1.3521468 · Cited by 97 · see citing works

A learning-based automatic segmentation and quantification method on left ventricle in gated myocardial perfusion SPECT imaging: A feasibility study

Wang, T., Lei, Y., Tang, H., He, Z., Castillo, R., Wang, C., Li, D., Higgins, K. A., Liu, T., Curran, W. J., et al. (2019) · Journal of Nuclear Cardiology · doi:10.1007/s12350-019-01594-2 · Cited by 96 · see citing works

Deformable registration for image-guided radiation therapy

Sarrut, D. (2006) · Zeitschrift für Medizinische Physik · doi:10.1078/0939-3889-00327 · Cited by 94 · see citing works

A multi-scale framework with unsupervised joint training of convolutional neural networks for pulmonary deformable image registration

Jiang, Z., Yin, F.-F., Ge, Y., & Ren, L. (2019) · Physics in Medicine and Biology · doi:10.1088/1361-6560/ab5da0 · Cited by 94 · see citing works

Multimodal image registration with deep context reinforcement learning

Ma, K., Wang, J., Singh, V., Tamersoy, B., Chang, Y.-J., Wimmer, A., & Chen, T. (2017) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-66182-7_28 · Cited by 87 · see citing works

Context-encoding Variational Autoencoder for Unsupervised Anomaly DetectionPreprint

Zimmerer, D., Köhl, S., Petersen, J., et al. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1812.05941 · Cited by 87 · see citing works

Adversarial Deformation Regularization for Training Image Registration Neural Networks

Hu, Y., Gibson, E., Ghavami, N., Bonmati, E., Moore, C. M., Emberton, M., Vercauteren, T. K., Noble, J. A., & Barratt, D. C. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-00928-1_87 · Cited by 85 · see citing works

Unsupervised Deformable Image Registration Using Cycle-Consistent CNN

Kim, B., Kim, J., Lee, J., Kim, D. H., Park, S. H., & Ye, J. C. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-32226-7_19 · Cited by 82 · see citing works

CT prostate segmentation based on synthetic MRI‐aided deep attention fully convolution network

Lei, Y., Dong, X. Y., Tian, Z., et al. (2019) · Medical Physics · doi:10.1002/mp.13933 · Cited by 82 · see citing works

Registration of sliding objects using direction dependent B-splines decomposition

Delmon, V., Rit, S., Pinho, R., & Sarrut, D. (2013) · Physics in Medicine and Biology · doi:10.1088/0031-9155/58/5/1303 · Cited by 80 · see citing works

Adversarial deformation regularization for training image registration neural networks

Hu, Y., Gibson, E., Ghavami, N., Bonmati, E., Moore, C. M., Emberton, M., Vercauteren, T. K., Noble, J., & Barratt, D. (2018) · UCL Discovery (University College London) · Cited by 80 · see citing works

Unsupervised Probabilistic Deformation Modeling for Robust Diffeomorphic Registration

Krebs, J., Mansi, T., Mailhé, B., Ayache, N., & Delingette, H. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-00889-5_12 · Cited by 78 · see citing works

AIRNet: Self-Supervised Affine Registration for 3D Medical Images using Neural NetworksPreprint

Chee, E., & Wu, Z. (2018) · arXiv.org · doi:10.48550/arxiv.1810.02583 · Cited by 77 · see citing works

Respiratory motion correction for free-breathing 3D abdominal MRI using CNN-based image registration: a feasibility study

Lv, J., Yang, M. Y., Zhang, J., & Wang, X. (2017) · British Journal of Radiology · doi:10.1259/bjr.20170788 · Cited by 76 · see citing works

Simple DVH parameter addition as compared to deformable registration for bladder dose accumulation in cervix cancer brachytherapy

Andersen, E., Noe, K. Ø., Sørensen, T. S., et al. (2013) · Radiotherapy and Oncology · doi:10.1016/j.radonc.2013.01.013 · Cited by 74 · see citing works

Construction of a Statistical Model for Cardiac Motion Analysis Using Nonrigid Image Registration

Chandrashekara, R., Rao, A. W., Sanchez-Ortiz, G. I., Mohiaddin, R. H., & Rueckert, D. (2003) · Lecture notes in computer science · doi:10.1007/978-3-540-45087-0_50 · Cited by 73 · see citing works

Deformable image registration using convolutional neural networks

Lafarge, M. W., Moeskops, P., Veta, M., Pluim, J. P. W., & Eppenhof, K. A. J. (2018) · SPIE Medical Imaging: Image Processing · doi:10.1117/12.2292443 · Cited by 73 · see citing works

Joint Registration And Segmentation Of Xray Images Using Generative Adversarial Networks

Mahapatra, D., Ge, Z., Sedai, S., & Chakravorty, R. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-00919-9_9 · Cited by 73 · see citing works

MRI-based treatment planning for brain stereotactic radiosurgery: Dosimetric validation of a learning-based pseudo-CT generation method

Wang, T., Manohar, N., Lei, Y., et al. (2018) · Medical dosimetry · doi:10.1016/j.meddos.2018.06.008 · Cited by 72 · see citing works

MRI-based treatment planning for liver stereotactic body radiotherapy: validation of a deep learning-based synthetic CT generation method

Liu, Y., Lei, Y., Wang, T., et al. (2019) · British Journal of Radiology · doi:10.1259/bjr.20190067 · Cited by 72 · see citing works

ssEMnet: Serial-Section Electron Microscopy Image Registration Using a Spatial Transformer Network with Learned Features

Yoo, I., Hildebrand, D. G. C., Tobin, W. F., Lee, W.-C. A., & Jeong, W. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-67558-9_29 · Cited by 69 · see citing works

Robust contour propagation using deep learning and image registration for online adaptive proton therapy of prostate cancer

Elmahdy, M. S., Jagt, T. Z., Zinkstok, R. T., Qiao, Y., Shahzad, R., Sokooti, H., Yousefi, S., Incrocci, L., Marijnen, C. A. M., Hoogeman, M. S., et al. (2019) · Medical Physics · doi:10.1002/mp.13620 · Cited by 68 · see citing works

Estimation of lung motion fields in 4D CT data by variational non-linear intensity-based registration: A comparison and evaluation study

Werner, R., Schmidt-Richberg, A., Handels, H., et al. (2014) · Physics in Medicine and Biology · doi:10.1088/0031-9155/59/15/4247 · Cited by 65 · see citing works

Effect of deformable registration uncertainty on lung SBRT dose accumulation

Samavati, N., Velec, M., & Brock, K. K. (2015) · Medical Physics · doi:10.1118/1.4938412 · Cited by 64 · see citing works

On the dosimetric effect and reduction of inverse consistency and transitivity errors in deformable image registration for dose accumulation

Bender, E. T., Hardcastle, N., & Tomé, W. A. (2011) · Medical Physics · doi:10.1118/1.3666948 · Cited by 63 · see citing works

An unsupervised convolutional neural network-based algorithm for deformable image registration

Kearney, V. P., Haaf, S., Sudhyadhom, A., Valdés, G., & Solberg, T. D. (2018) · Physics in Medicine and Biology · doi:10.1088/1361-6560/aada66 · Cited by 62 · see citing works

Learning Rigid Image Registration - Utilizing Convolutional Neural Networks for Medical Image Registration

Sloan, J. M., Goatman, K. A., & Siebert, J. P. (2018) · International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC) · doi:10.5220/0006543700890099 · Cited by 60 · see citing works

Nonrigid Registration of Ultrasound and MRI Using Contextual Conditioned Mutual Information

Rivaz, H. J., Karimaghaloo, Z., Fonov, V., & Collins, D. L. (2014) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2013.2294630 · Cited by 59 · see citing works

Error estimation of deformable image registration of pulmonary CT scans using convolutional neural networks

Eppenhof, K. A. J., & Pluim, J. P. W. (2018) · Journal of medical imaging · doi:10.1117/1.jmi.5.2.024003 · Cited by 58 · see citing works

Enhancing Label-Driven Deep Deformable Image Registration with Local Distance Metrics for State-of-the-Art Cardiac Motion Tracking

Hering, A., Kuckertz, S., Heldmann, S., & Heinrich‬, M. P. (2019) · Informatik aktuell · doi:10.1007/978-3-658-25326-4_69 · Cited by 58 · see citing works

A generalized framework unifying image registration and respiratory motion models and incorporating image reconstruction, for partial image data or full images

McClelland, J. R., Marc Modat, A., Arridge, S., et al. (2017) · Physics in Medicine and Biology · doi:10.1088/1361-6560/aa6070 · Cited by 56 · see citing works

Pairwise domain adaptation module for CNN-based 2-D/3-D registration

Zheng, J., Miao, S., Wang, Z., & Liao, R. (2018) · Journal of medical imaging · doi:10.1117/1.jmi.5.2.021204 · Cited by 54 · see citing works

End-to-end Active Object Tracking via Reinforcement LearningPreprint

Luo, W., Sun, P., Zhong, F., et al. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1705.10561 · Cited by 54 · see citing works

Automatic and hierarchical segmentation of the human skeleton in CT images

Fu, Y., Liu, S., Li, H., et al. (2017) · Physics in Medicine and Biology · doi:10.1088/1361-6560/aa6055 · Cited by 53 · see citing works

On the Adaptability of Unsupervised CNN-Based Deformable Image Registration to Unseen Image Domains

Ferrante, E., Oktay, O., Glocker, B., & Milone, D. H. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-00919-9_34 · Cited by 51 · see citing works

Efficient Exploration in Reinforcement Learning

Langford, J. C. (2017) · Encyclopedia of Machine Learning and Data Mining · doi:10.1007/978-1-4899-7687-1_244 · Cited by 50 · see citing works

3D prostate segmentation of ultrasound images combining longitudinal image registration and machine learning

Yang, X., & Fei, B. (2012) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.912188 · Cited by 49 · see citing works

Learning‐based CBCT correction using alternating random forest based on auto‐context model

Lei, Y., Tang, X., Higgins, K. A., et al. (2018) · Medical Physics · doi:10.1002/mp.13295 · Cited by 47 · see citing works

3D Convolutional Neural Networks Image Registration Based on Efficient Supervised Learning from Artificial DeformationsPreprint

Sokooti, H., de Vos, B. D., Berendsen, F. F., Ghafoorian, M., Yousefi, S., Lelieveldt, B. P. F., Išgum, I., & Staring, M. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1908.10235 · Cited by 47 · see citing works

Multi-Modal Medical Image Registration with Full or Partial Data: A Manifold Learning Approach

Bashiri, F. S., Baghaie, A., Rostami, R., Yu, Z., & D’Souza, R. M. (2018) · Journal of Imaging · doi:10.3390/jimaging5010005 · Cited by 46 · see citing works

GDL-FIRE$$^\text {4D}$$: Deep Learning-Based Fast 4D CT Image Registration

Sentker, T., Madesta, F., & Werner, R. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-00928-1_86 · Cited by 43 · see citing works

Deep learning-based image quality improvement for low-dose computed tomography simulation in radiation therapy

Wang, T., Lei, Y., Tian, Z., Dong, X., Liu, Y., Jiang, X., Curran, W. J., Liu, T., Shu, H. G., & Yang, X. (2019) · Journal of medical imaging · doi:10.1117/1.jmi.6.4.043504 · Cited by 43 · see citing works

dPIRPLE: a joint estimation framework for deformable registration and penalized-likelihood CT image reconstruction using prior images

Dang, H., Wang, A., Sussman, M. S., Siewerdsen, J. H., & Stayman, J. W. (2014) · Physics in Medicine and Biology · doi:10.1088/0031-9155/59/17/4799 · Cited by 42 · see citing works

MRI-based synthetic CT generation using semantic random forest with iterative refinement

Lei, Y., Harms, J. M., Wang, T., et al. (2019) · Physics in Medicine and Biology · doi:10.1088/1361-6560/ab0b66 · Cited by 42 · 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

Quantitative error prediction of medical image registration using regression forests

Sokooti, H., Saygılı, G., Glocker, B., Lelieveldt, B. P. F., & Staring, M. (2019) · Medical Image Analysis · doi:10.1016/j.media.2019.05.005 · Cited by 39 · see citing works

Deep deformable registration: Enhancing accuracy by fully convolutional neural net

Ghosal, S., & Ray, N. (2017) · Pattern Recognition Letters · doi:10.1016/j.patrec.2017.05.022 · Cited by 38 · see citing works

Unsupervised End-to-end Learning for Deformable Medical Image RegistrationPreprint

Shan, S., Wen, Y., Guo, X., et al. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1711.08608 · Cited by 38 · see citing works

Quicksilver: Fast predictive image registration – A deep learning approach

Styner, M. A., Kwitt, R., Yang, X., & Niethammer, M. (2020) · Carolina Digital Repository (University of North Carolina at Chapel Hill) · doi:10.17615/h90s-7k12 · Cited by 37 · see citing works

Pulmonary Image Registration with elastix using a Standard Intensity-Based Algorithm

Staring, M., Klein, S., Niessen, W. J., et al. (2010) · Cited by 36 · see citing works

A novel learning-based dissimilarity metric for rigid and non-rigid medical image registration by using bhattacharyya distances

So, R. W. K., & Chung, A. C. S. (2017) · Pattern Recognition · doi:10.1016/j.patcog.2016.09.004 · Cited by 36 · see citing works

Robust Multimodal Image Registration Using Deep Recurrent Reinforcement Learning

Sun, S., Hu, J., Yao, M., Hu, J., Yang, X., Song, Q., & Wu, X. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-20890-5_33 · Cited by 36 · see citing works

Unsupervised deformable image registration with fully connected generative neural network

Sheikhjafari, A., Noga, M., Punithakumar, K., & Ray, N. (2018) · Cited by 35 · see citing works

Automatic 3D segmentation of ultrasound images using atlas registration and statistical texture prior

Yang, X., Schuster, D. M., Master, V. V., et al. (2011) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.877888 · Cited by 34 · see citing works

Deformable MRI-Ultrasound Registration Using 3D Convolutional Neural Network

Sun, L., & Zhang, S. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01045-4_18 · Cited by 34 · see citing works

Image synthesis-based multi-modal image registration framework by using deep fully convolutional networks

Liu, X., Jiang, D., Wang, M., & Song, Z. (2018) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-018-1924-y · Cited by 34 · see citing works

Adversarial Optimization for Joint Registration and Segmentation in Prostate CT Radiotherapy

Elmahdy, M. S., Wolterink, J. M., Sokooti, H., Išgum, I., & Staring, M. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-32226-7_41 · Cited by 31 · see citing works

3D non-rigid registration using surface and local salient features for transrectal ultrasound image-guided prostate biopsy

Yang, X., Akbari, H., Halig, L. V., & Fei, B. (2011) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.878153 · Cited by 30 · see citing works

Optimal virtual monoenergetic image in “TwinBeam” dual‐energy CT for organs‐at‐risk delineation based on contrast‐noise‐ratio in head‐and‐neck radiotherapy

Wang, T., Ghavidel, B. B., Beitler, J. J., et al. (2019) · Journal of Applied Clinical Medical Physics · doi:10.1002/acm2.12539 · Cited by 30 · see citing works

Dosimetric study on learning-based cone-beam CT correction in adaptive radiation therapy

Wang, T., Lei, Y., Manohar, N., et al. (2019) · Medical dosimetry · doi:10.1016/j.meddos.2019.03.001 · Cited by 29 · see citing works

3D transrectal ultrasound (TRUS) prostate segmentation based on optimal feature learning framework

Yang, X., Rossi, P. J., Jani, A. B., et al. (2016) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2216396 · Cited by 28 · see citing works

Weakly Supervised Learning of Metric Aggregations for Deformable Image Registration

Ferrante, E., Dokania, P. K., Silva, R. M., & Nikos Paragios, A. (2018) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2018.2869700 · Cited by 28 · see citing works

Real-Time 2D-3D Deformable Registration with Deep Learning and Application to Lung Radiotherapy Targeting

Foote, M. D., Zimmerman, B. E., Sawant, A. R., & Joshi, S. C. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-20351-1_20 · Cited by 28 · see citing works

Nonrigid registration and classification of the kidneys in 3D dynamic contrast enhanced (DCE) MR images

Yang, X., Ghafourian, P., Sharma, P., et al. (2012) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.912190 · Cited by 27 · see citing works

LSTM Spatial Co-transformer Networks for Registration of 3D Fetal US and MR Brain Images

Wright, R., Khanal, B., Gómez, A., Skelton, E., Matthew, J., Hajnal, J. V., Rueckert, D., & Schnabel, J. A. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-00807-9_15 · Cited by 27 · see citing works

Registration of organs with sliding interfaces and changing topologies

Berendsen, F. F., Kotte, A. N. T. J., Viergever, M. A., & Pluim, J. P. W. (2014) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2043447 · Cited by 26 · see citing works

A neural network approach for fast, automated quantification of DIR performance

Neylon, J., Min, Y., Low, D. A., & Santhanam, A. P. (2017) · Medical Physics · doi:10.1002/mp.12321 · Cited by 26 · see citing works

Non-rigid Craniofacial 2D-3D Registration Using CNN-Based Regression

Pei, Y., Zhang, Y., Qin, H., Ma, G., Guo, Y., Xu, T., & Zha, H. (2017) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-67558-9_14 · Cited by 25 · see citing works

A Fast and Scalable Method for Quality Assurance of Deformable Image Registration on Lung CT Scans using Convolutional Neural Networks.

Galib, S., Lee, H., Guy, C., Riblett, M., & Hugo, G. D. (2019) · Medical Physics (Lancaster) · doi:10.1002/mp.13890 · Cited by 23 · see citing works

Learning 3D non-rigid deformation based on an unsupervised deep learning for PET/CT image registration

Jiang, H., Yu, H., Zhou, X., Kang, H., Wang, Z., Hara, T., & Fujita, H. (2019) · SPIE Medical Imaging: Biomedical Applications in Molecular, Structural, and Functional Imaging · doi:10.1117/12.2512698 · Cited by 22 · see citing works

Semi-Supervised Deep Metrics for Image RegistrationPreprint

Sedghi, A., Luo, J., Mehrtash, A., Pieper, S., Tempany, C. M., Kapur, T., Mousavi, P., & Wells, W. M. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1804.01565 · Cited by 21 · see citing works

Towards Robust CT-Ultrasound Registration Using Deep Learning Methods

Sun, Y., Moelker, A. D., Niessen, W. J., & van Walsum, T. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-02628-8_5 · Cited by 21 · see citing works

An unsupervised network for fast microscopic image registration

Shu, C., Chen, X., Xie, Q., & Han, H. (2018) · SPIE Medical Imaging: Digital and Computational Pathology · doi:10.1117/12.2293264 · Cited by 20 · see citing works

Technical Note: Deformable image registration on partially matched images for radiotherapy applications

Yang, D., Goddu, S. M., Lü, W., Pechenaya, O. L., Wu, Y., Deasy, J. O., Naqa, I. E., & Low, D. A. (2009) · Medical Physics · doi:10.1118/1.3267547 · Cited by 19 · see citing works

Automatic large quantity landmark pairs detection in 4DCT lung images

Fu, Y., Wu, X., Thomas, A. M., Li, H. H., & Yang, D. (2019) · Medical Physics · doi:10.1002/mp.13726 · Cited by 19 · see citing works

A dosimetric comparison of the use of equally spaced beam (ESB), beam angle optimization (BAO), and volumetric modulated arc therapy (VMAT) in head and neck cancers treated by intensity modulated radiotherapy

Leung, W., Wu, V., Liu, C. Y. W., et al. (2019) · Journal of Applied Clinical Medical Physics · doi:10.1002/acm2.12748 · Cited by 18 · see citing works

Real-time Deep Registration With Geodesic Loss.Preprint

Salehi, S. S. M., Khan, S., Deniz Erdogmus, B., & Gholipour, A. (2018) · arXiv (Cornell University) · Cited by 17 · see citing works

Elastic Registration of Medical Images With GANs.Preprint

Mahapatra, D., Sedai, S., & Garnavi, R. (2018) · arXiv (Cornell University) · Cited by 17 · see citing works

Weakly Supervised Learning in Deformable EM Image Registration Using Slice Interpolation

Nguyen-Duc, T., Yoo, I., Thomas, L. A., et al. (2019) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2019.8759290 · Cited by 17 · see citing works

Non-rigid MR-TRUS image registration for image-guided prostate biopsy using correlation ratio-based mutual information

Gong, L., Wang, H., Peng, C., Dai, Y., Ding, M., Sun, Y., Yang, X., & Zheng, J. (2017) · BioMedical Engineering OnLine · doi:10.1186/s12938-016-0308-5 · Cited by 16 · see citing works

CNN Driven Sparse Multi-level B-Spline Image Registration

Jiang, P., & Shackleford, J. (2018) · 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.2018.00967 · Cited by 16 · see citing works

4D-CT Deformable Image Registration Using an Unsupervised Deep Convolutional Neural Network

Lei, Y., Fu, Y., Harms, J. M., Wang, T., Curran, W. J., Liu, T., Higgins, K. A., & Yang, X. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-32486-5_4 · Cited by 16 · see citing works

Motion Tracking and Strain Map Computation for Quasi-Static Magnetic Resonance Elastography

Fu, Y., Chui, C., Teo, C. L., et al. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-23623-5_54 · Cited by 15 · see citing works

Review of deep learning: concepts, CNN architectures, challenges, applications, future directions

Alzubaidi, L., Zhang, J., Humaidi, A. J., Al-Dujaili, A., Duan, Y., Al-Shamma, O., Santamaría, J., Fadhel, M. A., Al-Amidie, M., & Farhan, L. (2021) · Journal of Big Data · doi:10.1186/s40537-021-00444-8 · Cited by 7,906 · see citing works

The Impact of Machine Learning on 2D/3D Registration for Image-Guided Interventions: A Systematic Review and Perspective

Unberath, M., Gao, C., Hu, Y., Judish, M., Taylor, R. H., Armand, M., & Grupp, R. (2021) · Frontiers in Robotics and AI · doi:10.3389/frobt.2021.716007 · Cited by 102 · 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

Biomechanically constrained non-rigid mr-trus prostate registration using deep learning based 3d point cloud matching

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

Automatic ovarian tumors recognition system based on ensemble convolutional neural network with ultrasound imaging

Hsu, S.-T., Su, Y.-J., Hung, C.-H., Chen, M.-J., Lu, C.-H., & Kuo, C.-E. (2022) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-022-02047-6 · Cited by 58 · see citing works

Deep reinforcement learning and its applications in medical imaging and radiation therapy: a survey

Xu, L., Zhu, S., & Wen, N. (2022) · Physics in Medicine &amp; Biology · doi:10.1088/1361-6560/ac9cb3 · Cited by 35 · see citing works

Review on Research of Marxism Popularity in 2008

Xia, T. (2009) · Journal of Bohai University

Analysis of a Sporting Event on a Social Network: True Popularity & Popularity Bond

Gupta, A. K., Mittal, N., & Kohli, N. (2016) · Advances in intelligent systems and computing · doi:10.1007/978-981-10-1678-3_26 · Cited by 2 · see citing works

Higher education popularity:an inevitable alternative of higher education in new century

Xin, L. (2002) · Journal of Changchun University

Popularity: Does it magnify associations between popularity prioritization and the bullying and defending behavior of early adolescent boys and girls?

Duffy, A. L., Penn, S., Nesdale, D., & Zimmer‐Gembeck, M. J. (2016) · Social Development · doi:10.1111/sode.12206 · Cited by 63 · see citing works

On Phenomena of the Popularity and the Marketing of Literature

Zhu, H. (2006) · Journal of Zhejiang Sci-Tech University