SVF-Net: Learning Deformable Image Registration Using Shape Matching

U-Net: Convolutional Networks for Biomedical Image Segmentation

Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404

Fully convolutional networks for semantic segmentation

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Computing Large Deformation Metric Mappings via Geodesic Flows of Diffeomorphisms

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A Log-Euclidean Framework for Statistics on Diffeomorphisms

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Morphometry of anatomical shape complexes with dense deformations and sparse parameters

Durrleman, S., Prastawa, M. W., Charon, N., Korenberg, J. R., Joshi, S. C., Gerig, G., & Trouvé, A. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.06.043 · Cited by 288

A physics-based coordinate transformation for 3-D image matching

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LCC-Demons: A robust and accurate symmetric diffeomorphic registration algorithm

Lorenzi, M., Ayache, N., Frisoni, G. B., & Pennec, X. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.04.114 · Cited by 160

Fast Predictive Image Registration

Yang, X., Kwitt, R., & Niethammer, M. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46976-8_6 · Cited by 127

Computational analysis of LDDMM for brain mapping

Ceritoglu, C., Tang, X., Chow, M., Hadjiabadi, D. H., Shah, D., Brown, T., Burhanullah, M. H., Trinh, H., Hsu, J. T., Ament, K. A., et al. (2013) · Frontiers in Neuroscience · doi:10.3389/fnins.2013.00151 · Cited by 54

Automatic Segmentation of the Myocardium in Cine MR Images Using Deformable Registration

Jolly, M., Guetter, C., Lu, X., et al. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-28326-0_10 · Cited by 49

ResNet-LDDMM: Advancing the LDDMM Framework using Deep Residual Networks

Amor, B. B., Arguillère, S., & Shao, L. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3174908 · Cited by 28

SequenceMorph: A Unified Unsupervised Learning Framework for Motion Tracking on Cardiac Image Sequences

Ye, M., Yang, D. L., Huang, Q., et al. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3243040 · 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

Deep Learning Methods for Cardiovascular Image

Cao, Y., Liu, Z., Zhang, P., Zheng, Y., Song, Y., & Cui, L. (2019) · Journal of Artificial Intelligence and Systems · doi:10.33969/ais.2019.11006 · Cited by 27

Co-attention spatial transformer network for unsupervised motion tracking and cardiac strain analysis in 3D echocardiography

Ahn, S. S., Ta, K., Thorn, S., et al. (2022) · Medical Image Analysis · doi:10.1016/j.media.2022.102711 · Cited by 27

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

A Review of Keypoints’ Detection and Feature Description in Image Registration

Liu, C., Xu, J., & Wang, F. (2021) · Scientific Programming · doi:10.1155/2021/8509164 · Cited by 47

Towards Accurate and Robust Multi-Modal Medical Image Registration Using Contrastive Metric Learning

Hu, J., Sun, S., Yang, X., Zhou, S., Wang, X., Fu, Y., Zhou, J., Yin, Y., Cao, K., Song, Q., et al. (2019) · IEEE Access · doi:10.1109/access.2019.2938858 · Cited by 27

Edge-Aware Pyramidal Deformable Network for Unsupervised Registration of Brain MR Images

Cao, Y., Zhu, Z., Rao, Y., Qin, C., Lin, D., Dou, Q., Ni, D., & Wang, Y. (2021) · Frontiers in Neuroscience · doi:10.3389/fnins.2020.620235 · Cited by 26

Tutorial: methods for three-dimensional visualization of archival tissue material

Haddad, T. S., Friedl, P., Farahani, N., Treanor, D. E., Zlobec, I., & Nagtegaal, I. D. (2021) · Nature Protocols · doi:10.1038/s41596-021-00611-4 · Cited by 24

Adversarial learning for deformable registration of brain MR image using a multi-scale fully convolutional network

Duan, L., Yuan, G., Gong, L., Fu, T., Yang, X., Chen, X., & Zheng, J. (2019) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2019.101562 · Cited by 27

Image registration using machine and deep learning

Cao, X., Fan, J., Dong, P., Ahmad, S., Yap, P., & Shen, D. G. (2019) · Elsevier eBooks · doi:10.1016/b978-0-12-816176-0.00019-3 · Cited by 25

Learning a Generative Motion Model From Image Sequences Based on a Latent Motion Matrix

Krebs, J., Delingette, H., Ayache, N., & Mansi, T. (2021) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2021.3056531 · Cited by 24

Uncertainty Learning towards Unsupervised Deformable Medical Image Registration

Gong, X., Khaidem, L., Zhu, W., Zhang, B., & Doermann, D. (2022) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv51458.2022.00162 · Cited by 24

Deformable image registration with attention-guided fusion of multi-scale deformation fields

He, Z., He, Y., & Cao, W. (2022) · Applied Intelligence · doi:10.1007/s10489-022-03659-1 · Cited by 24

Deformable Image Registration Using Functions of Bounded Deformation

Nie, Z., & Yang, X. (2019) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2019.2896170 · Cited by 24

Longitudinal diffusion MRI analysis using Segis-Net: A single-step deep-learning framework for simultaneous segmentation and registration

Li, B., Niessen, W. J., Klein, S., de Groot, M., Ikram, M. A., Vernooij, M. W., & Bron, E. E. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118004 · Cited by 24

Probabilistic Image Registration via Deep Multi-class Classification: Characterizing Uncertainty

Sedghi, A., Kapur, T., Luo, J., Mousavi, P., & Wells, W. M. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-32689-0_2 · Cited by 22

Test-Time Training for Deformable Multi-Scale Image Registration

Zhu, W., Huang, Y., Xu, D., Qian, Z. Y., Fan, W., & Xie, X. S. (2021) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48506.2021.9561808 · Cited by 21

Memory-efficient 2.5D convolutional transformer networks for multi-modal deformable registration with weak label supervision applied to whole-heart CT and MRI scans

Hering, A., Kuckertz, S., Heldmann, S., & Heinrich‬, M. P. (2019) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-019-02068-z · Cited by 21

FDRN: A fast deformable registration network for medical images

Sun, K., & Simon, S. (2021) · Medical Physics · doi:10.1002/mp.15011 · Cited by 21

Joint affine and deformable three‐dimensional networks for brain MRI registration

Zhu, Z., Cao, Y., Qin, C., Rao, Y., Lin, D., Dou, Q., Ni, D., & Wang, Y. (2020) · Medical Physics · doi:10.1002/mp.14674 · Cited by 21

Multi-scale Neural ODEs for 3D Medical Image Registration

Xu, J., Chen, E. Z., Chen, X., Chen, T., & Sun, S. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-87202-1_21 · Cited by 21

Multi-strategy mutual learning network for deformable medical image registration

Zheng, Z., Cao, W., Duan, Y., Cao, G., & Lian, D. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.06.020 · Cited by 20

Unsupervised deep learning registration model for multimodal brain images

Abbasi, S., Mehdizadeh, A., Boveiri, H. R., Mosleh‐Shirazi, M. A., Javidan, R., Khayami, R., & Tavakoli, M. (2023) · Journal of Applied Clinical Medical Physics · doi:10.1002/acm2.14177 · Cited by 20

Advanced 4-dimensional cone-beam computed tomography reconstruction by combining motion estimation, motion-compensated reconstruction, biomechanical modeling and deep learning

Zhang, Y., Huang, X., & Wang, J. (2019) · Visual Computing for Industry Biomedicine and Art · doi:10.1186/s42492-019-0033-6 · Cited by 20

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

Cycle-Consistent Training for Reducing Negative Jacobian Determinant in Deep Registration Networks

Kuang, D. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-32778-1_13 · Cited by 20

OTMorph: Unsupervised Multi-Domain Abdominal Medical Image Registration Using Neural Optimal Transport

Kim, B., Zhuang, Y., Mathai, T. S., et al. (2024) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2024.3437295 · Cited by 19

Gum-Net: Unsupervised Geometric Matching for Fast and Accurate 3D Subtomogram Image Alignment and Averaging

Zeng, X., & Xu, M. (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/cvpr42600.2020.00413 · Cited by 19

DeepMapi: a Fully Automatic Registration Method for Mesoscopic Optical Brain Images Using Convolutional Neural Networks

Ni, H., Feng, Z., Guan, Y., Jia, X., Wu, C., Jiang, T., Zhong, Q., Yuan, J., Ren, M., Li, X., et al. (2020) · Neuroinformatics · doi:10.1007/s12021-020-09483-7 · 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

Attention-Aware Non-Rigid Image Registration for Accelerated MR Imaging

Ghoul, A., Pan, J., Lingg, A., Kübler, J., Krumm, P., Hammernik, K., Rueckert, D., Gatidis, S., & Küstner, T. (2024) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2024.3385024 · Cited by 18

Semantically Guided Large Deformation Estimation with Deep Networks

Ha, I. Y., Wilms, M., & Heinrich‬, M. P. (2020) · Sensors · doi:10.3390/s20051392 · Cited by 18

Neural Multi-Scale Self-Supervised Registration for Echocardiogram Dense TrackingPreprint

Zhu, W., Huang, Y., Vannan, M. A., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/674630 · Cited by 18

Symmetric pyramid network for medical image inverse consistent diffeomorphic registration

Zhang, L., Ning, G., Zhou, L., & Liao, H. (2023) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2023.102184 · Cited by 18

U-Net cascaded with dilated convolution for medical image registration

Cheng, Z., Guo, K., Wu, C., Shen, J.-K., & Qu, L. (2019) · Chinese Automation Congress · doi:10.1109/cac48633.2019.8996569 · Cited by 17

Conditional Segmentation in Lieu of Image RegistrationPreprint

Hu, Y., Gibson, E., Barratt, D., Emberton, M., Noble, J., & Vercauteren, T. K. (2019) · Research Portal (King's College London) · Cited by 17

Unsupervised learning for large motion thoracic CT follow-up registration

Hering, A., & Heldmann, S. (2019) · SPIE Medical Imaging: Image Processing · doi:10.1117/12.2506962 · Cited by 16

Image registration in PCB Fault Detection Based on Infrared Thermal Imaging

Dong, Z., & Chen, L. (2019) · Chinese Control Conference (CCC) · doi:10.23919/chicc.2019.8866191 · Cited by 15

All-Pass Parametric Image Registration

Zhang, X., Gilliam, C., & Blu, T. (2020) · IEEE Transactions on Image Processing · doi:10.1109/tip.2020.2984897 · Cited by 15

A diffeomorphic unsupervised method for deformable soft tissue image registration

Zhang, S., Liu, P. X., Zheng, M., & Shi, W. (2020) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2020.103708 · Cited by 15

An Unsupervised Learning Approach to Discontinuity-Preserving Image Registration

Ng, E., & Ebrahimi, M. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-50120-4_15 · Cited by 15

Learning Mri Contrast-Agnostic Registration

Hoffmann, M., Billot, B., Iglesias, J. E., et al. (2021) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi48211.2021.9434113 · Cited by 14

Probabilistic Modeling for Image Registration Using Radial Basis Functions: Application to Cardiac Motion Estimation

Gan, Z., Sun, W., Liao, K., & Yang, X. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3141119 · Cited by 14

Complete set of translation invariant measurements with Lipschitz bounds

Cahill, J., Contreras, A. A., & Hip, A. A. C. (2020) · Applied and Computational Harmonic Analysis · doi:10.1016/j.acha.2020.05.007 · Cited by 14

Generation of Annotated Brain Tumor MRIs with Tumor-induced Tissue Deformations for Training and Assessment of Neural Networks

Uzunova, H., Ehrhardt, J., & Heinz Handels, A. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-59719-1_49 · Cited by 14

Large Deformation Image Registration with Anatomy-Aware Laplacian Pyramid Networks

Mok, T. C. W., & Chung, A. C. S. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-71827-5_7 · Cited by 14

Continuous 3D Myocardial Motion Tracking via Echocardiography

Shen, C., Zhu, H., Zhou, Y., Liu, Y., Si, Y., Dong, L., Zhao, W., Brady, D. J., Cao, X., Ma, Z., et al. (2024) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2024.3419780 · Cited by 13

Vector field attention for deformable image registration

Liu, Y., Chen, J., Zuo, L., Carass, A., & Prince, J. L. (2024) · Journal of medical imaging · doi:10.1117/1.jmi.11.6.064001 · Cited by 16

A FCN-based Unsupervised Learning Model for Deformable Chest CT Image Registration

Fang, Q., Gu, X., Yan, J., Zhao, J., & Li, Q. (2019) · IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC) · doi:10.1109/nss/mic42101.2019.9059976 · Cited by 13

FIRE: Unsupervised bi-directional inter- and intra-modality registration using deep networks

Wang, C., Yang, G., & Papanastasiou, G. (2021) · Proceedings - IEEE Symposium on Computer-Based Medical Systems · doi:10.1109/cbms52027.2021.00101 · Cited by 13

Unsupervised Medical Image Alignment With Curriculum Learning

Burduja, M., & Ionescu, R. T. (2021) · Proceedings - International Conference on Image Processing · doi:10.1109/icip42928.2021.9506067 · 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

Conditional Segmentation in Lieu of Image Registration

Hu, Y., Gibson, E., Barratt, D. C., Emberton, M., Noble, J. A., & Vercauteren, T. K. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-32245-8_45 · Cited by 12

End‐to‐end unsupervised cycle‐consistent fully convolutional network for 3D pelvic CT‐MR deformable registration

Guo, Y., Wu, X., Wang, Z., Pei, X., & Xu, X. (2020) · Journal of Applied Clinical Medical Physics · doi:10.1002/acm2.12968 · Cited by 12

MAE-TransRNet: An improved transformer-ConvNet architecture with masked autoencoder for cardiac MRI registration

Xiao, X., Dong, S., Yu, Y., Li, Y., Yang, G., & Qiu, Z. (2023) · Frontiers in Medicine · doi:10.3389/fmed.2023.1114571 · Cited by 12

NeurEPDiff: Neural Operators to Predict Geodesics in Deformation Spaces

Wu, N., & Zhang, M. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-34048-2_45 · 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

Unsupervised Deep Learning Network with Self-Attention Mechanism for Non-Rigid Registration of 3D Brain MR Images

Oh, D., Kim, B., Lee, J., & Shin, Y.-G. (2020) · Journal of Medical Imaging and Health Informatics · doi:10.1166/jmihi.2021.3345 · Cited by 11

Progressive anatomically constrained deep neural network for 3D deformable medical image registration

Zheng, Z., Cao, W., He, Z., & Luo, Y. P. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.08.097 · Cited by 11

A Fully Unsupervised Deep Learning Framework for Non-Rigid Fundus Image Registration

Benvenuto, G. A., Colnago, M., Dias, M. A., Negri, R. G., da Silva, E. A., & Casaca, W. C. D. O. (2022) · Bioengineering · doi:10.3390/bioengineering9080369 · Cited by 11

FIRE: Unsupervised bi-directional inter-modality registration using deep networksPreprint

Wang, C., Papanastasiou, G., Chartsias, A., Jacenków, G., Tsaftaris, S. A., & Zhang, H. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1907.05062 · Cited by 11

CyTran: Cycle-Consistent Transformers for Non-Contrast to Contrast CT Translation.Preprint

Ristea, N.-C., Ionescu, A.-I., Savencu, O., Georgescu, M.-I., Verga, N. I., Khan, F. S., & Ionescu, R. T. (2021) · arXiv (Cornell University) · Cited by 11

TS-Net: Two-stage deformable medical image registration network based on new smooth constraints

Song, L., Ma, M., & Liu, G. (2023) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2023.01.013 · 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

Registration of Histopathology Images Using Self Supervised Fine Grained Feature Maps

Tong, J., Mahapatra, D., Bonnington, C. P., et al. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-60548-3_5 · 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

Efficient momentum conservation constrained PDE-LDDMM with Gauss–Newton–Krylov optimization, Semi-Lagrangian Runge–Kutta solvers, and the band-limited parameterization

Hernández, M. (2021) · Journal of Computational Science · doi:10.1016/j.jocs.2021.101470 · Cited by 10

Automated Registration for Dual-View X-Ray Mammography Using Convolutional Neural Networks

Walton, W. C., Kim, S.-J., & Mullen, L. A. (2022) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2022.3173182 · Cited by 10

Context-driven pyramid registration network for estimating large topology-preserved deformation

Wang, P., Yan, Y., Qian, L., Suo, S., Xu, J., Guo, Y., & Wang, Y. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.11.088 · Cited by 10

Evaluation of a Learning-based Deformable Registration Method on Abdominal CT Images

Bhattacharjee, R., Heitz, F., Noblet, V., Sharma, S., & Sharma, N. (2020) · IRBM · doi:10.1016/j.irbm.2020.04.002 · Cited by 10

Dual U-Net based feature map algorithm for automatic projection alignment of synchrotron nano-CT

Su, B., Gao, R., Tao, F., et al. (2022) · Nuclear Instruments and Methods in Physics Research Section A Accelerators Spectrometers Detectors and Associated Equipment · doi:10.1016/j.nima.2022.167242 · Cited by 10

Wavelet-Guided Multi-Scale ConvNeXt for Unsupervised Medical Image Registration

Zhang, X., Xu, A., Ouyang, G., Xu, Z., Shen, S., Chen, W., Liang, M., Zhang, G., Wei, J., Zhou, X., et al. (2025) · Bioengineering · doi:10.3390/bioengineering12040406 · Cited by 9

A Coarse-to-Fine Registration Network Based on Affine Transformation and Multi-Scale Pyramid

Li, D., Li, Y., Li, J., & Lu, G. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.121587 · Cited by 10

iRegNet: Non-Rigid Registration of MRI to Interventional US for Brain-Shift Compensation Using Convolutional Neural Networks

Zeineldin, R. A., Karar, M. E., Elshaer, Z., Schmidhammer, M., Coburger, J., Wirtz, C. R., Burgert, O., & Mathis-Ullrich, F. (2021) · IEEE Access · doi:10.1109/access.2021.3120306 · Cited by 11

Weighted Metamorphosis for Registration of Images with Different Topologies

François, A., Maillard, M., Oppenheim, C., et al. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-11203-4_2 · Cited by 9

A Comparative Analysis of Registration Tools: Traditional vs Deep Learning Approach on High Resolution Tissue Cleared DataPreprint

Nazib, A., Fookes, C., & Perrin, D. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1810.08315 · Cited by 9

Unsupervised Landmark Detection-Based Spatiotemporal Motion Estimation for 4-D Dynamic Medical Images

Guo, Y., Bi, L., Wei, D., Chen, L., Zhu, Z., Feng, D. D., Zhang, R., Wang, Q., & Kim, J. (2021) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2021.3126817 · Cited by 8

One Registration is Worth Two Segmentations

Huang, S., Xu, T., Shen, Z., Saeed, S. U., Wen, Y., Barratt, D. C., & Hu, Y. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72390-2_62 · Cited by 8

Scale‐adaptive deep network for deformable image registration

Sang, Y., & Ruan, D. (2021) · Medical Physics · doi:10.1002/mp.14935 · Cited by 8

Supervised Learning With Perceptual Similarity for Multimodal Gene Expression Registration of a Mouse Brain Atlas

Krepl, J., Casalegno, F., Delattre, É., Erö, C., Lu, H., Keller, D. X., Rodarie, D., Markram, H., & Schürmann, F. (2021) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2021.691918 · Cited by 8

Tri-view two-photon microscopic image registration and deblurring with convolutional neural networks

Lee, S., Kume, H., Urakubo, H., Kasai, H., & Ishii, S. (2022) · Neural Networks · doi:10.1016/j.neunet.2022.04.011 · Cited by 7

A multi-scale unsupervised learning for deformable image registration

Shao, S., Pei, Z., Chen, W., Zhu, W., Wu, X., & Zhang, B. (2021) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-021-02511-0 · Cited by 8

Registration of Histopathogy Images Using Structural Information From Fine Grained Feature MapsPreprint

Mahapatra, D. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.02078 · Cited by 8

EyeLiner

Veturi, Y. A., McNamara, S., Kinder, S., et al. (2024) · Ophthalmology Science · doi:10.1016/j.xops.2024.100664 · Cited by 7

Unsupervised Deep Learning Registration of Uterine Cervix Sequence Images

Guo, P. W., Xue, Z., Angara, S., et al. (2022) · Cancers · doi:10.3390/cancers14102401 · Cited by 7

An Adaptive Region-Based Transformer for Nonrigid Medical Image Registration With a Self-Constructing Latent Graph

Lan, S., Li, X., & Guo, Z. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2023.3294290 · 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

Unsupervised Three-Dimensional Image Registration Using a Cycle Convolutional Neural Network

Lu, Z., Coatrieux, J.-L., Yang, G., Hua, T., Hu, L., Kong, Y., Tang, L., Zhu, X., Dillenseger, J., & Shu, H. (2019) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2019.8803163 · Cited by 8

Optimizing early cancer diagnosis and detection using a temporal subtraction technique

Miyake, N., Lu, H., Kamiya, T., Aoki, T., & Kido, S. (2021) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2021.120745 · Cited by 7

Deformable Registration of Brain MR Images via a Hybrid Loss

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Partial Differential Equation-Constrained Diffeomorphic Registration from Sum of Squared Differences to Normalized Cross-Correlation, Normalized Gradient Fields, and Mutual Information: A Unifying Framework

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