Svf-net: learning deformable image registration using shape matching
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Rohe et al. adopted a U-Net-like network to predict the deformation field for 3D cardiac MRI volume registration, with supervisions generated from mesh segmentations and SSD as loss; their results are comparable with traditional registration.
“Rohe et al. (76) adopted a U-Net-like network to predict the deformation field for 3D cardiac MRI volume registration. The supervisions are transformations generated from mesh segmentations, and SSD between the supervision and prediction is set as the loss function. Their results are comparable with those from traditional registration.”
From A review of deep learning-based deformable medical image registration (2022) · cites this paper as [76]
Cites
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Self-citationFast Predictive Image Registration
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Automatic Segmentation of the Myocardium in Cine MR Images Using Deformable Registration
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Learning-Based Detection and Tracking in Medical Imaging: A Probabilistic Approach
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Bernard, O., Lalande, A., Zotti, C., Cervenansky, F., Yang, X., Heng, P.-A., Cetin, I., Lekadir, K., Camara, O., Gonzalez Ballester, M. A., et al. (2018) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2018.2837502 · Cited by 2,431 · see citing works
“The registration module implements an encoder-decoder network called SVFNet [56].”
MethodSelf-citationVoxelMorph: 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
TransMorph: Transformer for unsupervised medical image registration
Chen, J., Frey, E. C., He, Y., Segars, W. P., Li, Y., & Du, Y. (2022) · Medical Image Analysis · doi:10.1016/j.media.2022.102615 · Cited by 826 · 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
“The following year, Rohe et al. [102] also used a U-net [103] inspired network to estimate the deformation field used to register 3D cardiac MR volumes.”
MethodAn 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
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
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
“Most of these rely on ground truth warp fields or segmentations [26, 35, 39, 45], a significant drawback compared to our method, which does not require either.”
MethodA 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
“o using existing labeled datasets of real scenarios [95,96], many data”
MethodApplications, promises, and pitfalls of deep learning for fluorescence image reconstruction
Belthangady, C., & Royer, L. A. (2019) · Nature Methods · doi:10.1038/s41592-019-0458-z · Cited by 502 · see citing works
Data Augmentation Using Learned Transformations for One-Shot Medical Image Segmentation
Zhao, A. T., Balakrishnan, G., Durand, F., Guttag, J. V., & Dalca, A. V. (2019) · 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.2019.00874 · Cited by 476 · see citing works
“This mature field spans optimization-based methods [4, 7, 67, 70], and recent learning-based methods [8, 9, 20, 42, 62, 72, 80].”
MethodWeakly-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
Unsupervised Learning of Probabilistic Diffeomorphic Registration for Images and Surfaces
Dalca, A. V., Balakrishnan, G., Guttag, J., & Sabuncu, M. R. (2019) · Medical Image Analysis · doi:10.1016/j.media.2019.07.006 · Cited by 467 · see citing works
“Most earlier approaches demonstrated the feasibility of deep learning based registration, and required ground truth registration fields [13, 42, 59, 64, 68].”
BackgroundUnsupervised 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
“which can introduce a bias and necessitate significant preprocessing [23,26,28].”
BackgroundCycleMorph: Cycle consistent unsupervised deformable image registration
Kim, B., Kim, D. H., Park, S. H., Kim, J., Lee, J., & Ye, J. C. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.102036 · Cited by 346 · see citing works
Large Deformation Diffeomorphic Image Registration with Laplacian Pyramid Networks
Mok, T. C. W., & Chung, A. C. S. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-59716-0_21 · Cited by 338 · see citing works
“ndent on the degree of misalignment between the input images and can be time-consuming with high-resolution 3D image volumes. Recent unsupervised deep learning-based image registration (DLIR) methods [4,5,20,26] have demonstrated promising registration speed and quality in a variety of deformable image registration ta…”
BackgroundGoing 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
“attention even before the deep learning era [180], [181], [182], [183], [184], [185], [186]. Advent of deep learning has also caused neural networks to penetrate in medical image registration [187], [188], [189], [190]. 3.3.1 Brain Van et al. [191] proposed a stacked bidirectional convolutional LSTM (C-LSTM) network f…”
BackgroundInfluentialFast Symmetric Diffeomorphic Image Registration with Convolutional Neural Networks
Mok, T. C. W., & Chung, A. C. S. (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/cvpr42600.2020.00470 · Cited by 278 · see citing works
“Existing CNN-based methods often ignore desirable dif-feomorphic properties, including topology preservation, in-vertibility and inverse consistency of the transformation [7, 24, 8, 32, 11, 5].”
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
“Here, llllllllφφ is a the Euclidean distance as defined in (Rohé et al., 2017; Sokooti et al., 2017), which assumes that the ground-truth deformation fields are already achieved.”
BackgroundMethodLearning a Probabilistic Model for Diffeomorphic Registration
Krebs, J., Delingette, H., Mailhé, B., Ayache, N., & Mansi, T. (2019) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2019.2897112 · Cited by 230 · see citing works
“Due to the difficulty of finding ground truth voxel mappings, supervised methods need to rely on predictions from existing algorithms [15], [16], simulations [17]–[19] or a combination of both [20], [21].”
BackgroundMethodSynthMorph: Learning Contrast-Invariant Registration Without Acquired Images
Hoffmann, M., Billot, B., Greve, D. N., Iglesias, J. E., Fischl, B. R., & Dalca, A. V. (2021) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2021.3116879 · Cited by 220 · see citing works
“ulated warps or deformation fields estimated by classical methods [21], [22], [24], [42]–[44].”
MethodConvolutional Neural Network With Shape Prior Applied to Cardiac MRI Segmentation
Zotti, C., Luo, Z., Lalande, A., & Jodoin, P. (2018) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2018.2865450 · Cited by 208 · see citing works
“That shift as well as the large inter-slice gap (often 10 mm) make the use of 3D convolution operations ill-suited [48].”
BackgroundLi, H., & Yong Fan, A. (2018) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2018.8363757 · Cited by 194 · see citing works
“A variety of strategies have been proposed to build training data, specifically the spatial transformations [1-3].”
MethodMedical 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
“…Recall and Dice Directly regressing the deformable multimodal (CT- US) registration parameters using a weakly-supervised trained CNN Rohe et al. (2017) M.-M. Rohé, M. Datar, T. Heimann, M. Sermesant, and X. Pennec, “SVF-Net: Learning Deformable Image Registration Using Shape…”
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
“[11] used a fully convolutional neural network to perform direct estimation of a Stationary Velocity Field transformation model, trained on previously registered cardiac images.”
MethodChen, 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
“…end-to-end learning, the registration network can be trained with ground-truth deformations, which cannot be manually delineated and are usually obtained by a traditional registration algorithm under the help of tissue segmentation maps (Cao et al., 2017; Yang et al., 2017; Rohé et al., 2017).”
BackgroundMethodHyperMorph: Amortized Hyperparameter Learning for Image Registration
Hoopes, A., Hoffmann, M., Fischl, B. R., Guttag, J. V., & Dalca, A. V. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-78191-0_1 · Cited by 150 · see citing works
“Learningbased registration methods employ image datasets to learn a function that rapidly computes the deformation field between image pairs [7,20,48,52,56,58,59].”
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
“However, most still rely on the choice of image similarity measure or on non-trivial, and potentially nonrealistic, application-specific assumptions in procuring large number of ground-truth transformations for training, such as those from simulations [11, 12], existing registration methods [13] or manual rigid alignm…”
BackgroundAdversarial 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 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
“Specifi-cally, fully convolutional network (FCN) has been employed in [10] and [51] to learn the deformation momenta and stationary velocity field (SVF), respectively.”
MethodTeTrIS: Template Transformer Networks for Image Segmentation With Shape Priors
Lee, M. C. H., Petersen, K., Pawlowski, N., Glocker, B., & Schaap, M. (2019) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2019.2905990 · Cited by 118 · see citing works
“Recent works on neural network based image registration fall into two major categories, the first treats network based registration as a regression problem on a given ground truth deformation field such as in [23]–[27].”
BackgroundFAIM – 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
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
“Roh´e [56] proposed an FCN model to learn the stationary velocity field, which consists of a contracting path to extract task-relevant features and a symmetric expanding path to output the transformation parameters.”
MethodDeng, X., Liu, E., Li, S., Duan, Y., & Xu, M. (2023) · IEEE Transactions on Image Processing · doi:10.1109/tip.2023.3240024 · Cited by 109 · see citing works
“To overcome this drawback, some methods are proposed using neural networks to achieve image registration with high speed [36], [37], [38], [39], [40], [41], [42].”
MethodAdaptive Augmentation of Medical Data Using Independently Conditional Variational Auto-Encoders
Pesteie, M., Abolmaesumi, P., & Rohling, R. N. (2019) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2019.2914656 · Cited by 102 · see citing works
“model have been proposed for various tasks [5], [17], [18].”
MethodDiffuseMorph: Unsupervised Deformable Image Registration Using Diffusion Model
Kim, B., Han, I., & Ye, J. C. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19821-2_20 · Cited by 102 · 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
“(76) adopted a U-Net-like network to predict the deformation field for 3D cardiac MRI volume registration.”
MethodLearning deformable registration of medical images with anatomical constraints
Mansilla, L., Milone, D. H., & Ferrante, E. (2020) · Neural Networks · doi:10.1016/j.neunet.2020.01.023 · Cited by 90 · see citing works
“flow estimation [10], supervised CNN-based image registration methods [49, 35, 39] posed image registration as a regression problem.”
BackgroundMethodNenoff, L., Amstutz, F., Murr, M., Archibald‐Heeren, B. R., Fusella, M., Hussein, M., Lechner, W., Zhang, Y., Sharp, G., & Osorio, E. M. V. (2023) · Physics in Medicine and Biology · doi:10.1088/1361-6560/ad0d8a · Cited by 99 · see citing works
CNN-based lung CT registration with multiple anatomical constraints
Hering, A., Häger, S., Moltz, J. H., Leßmann, N., Heldmann, S., & van Ginneken, B. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.102139 · Cited by 89 · see citing works
“As a lot of deep-learning-based registration methods are build on top of U-Net (e.g. Bal-akrishnan et al. (2019); Hering and Heldmann (2019); Hering et al. (2019b); Roh´e et al. (2017)), they are also multi-leveled in their nature.”
DeepFLASH: An Efficient Network for Learning-Based Medical Image Registration
Wang, J., & Zhang, 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/cvpr42600.2020.00450 · Cited by 89 · see citing works
“Such algorithms typically adopt convolutional neural networks (CNNs) to learn a mapping between pairwise images and associated spatial transformations in training dataset [28, 21, 7, 10, 9].”
BackgroundImage-and-Spatial Transformer Networks for Structure-Guided Image Registration
Lee, M. C. H., Oktay, O., Schuh, A., Schaap, M., & Glocker, B. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-32245-8_38 · Cited by 87 · see citing works
Sanchez‐Martinez, S., Cámara, Ó., Piella, G., Čikeš, M., González‐Ballester, M. Á., Miron, M., Vellido, A., Gómez, E., Fraser, A. G., & Bijnens, B. H. (2022) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2021.765693 · Cited by 100 · see citing works
“…as a crucial step for status interpretation toward diagnosis, and deep learning has emerged as a suitable tool to register 3D cardiac volumes (13), 3D pre-operative cardiac models to 2D intraoperative x-ray fluoroscopy to facilitate image-guided interventions (14), or cardiac MRI sequences…”
MethodMetric Learning for Image Registration
Niethammer, M., Kwitt, R., & Vialard, F. (2019) · 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.2019.00866 · Cited by 81 · see citing works
“Alternative approaches have been proposed which can register 3D images [35, 38, 12, 23, 2, 15] and assure diffeomorphisms [47, 48].”
BackgroundMethodInfluentialAdversarial 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
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
Learning Conditional Deformable Templates with Convolutional NetworksPreprint
Dalca, A. V., Rakic, M., Guttag, J. V., et al. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1908.02738 · Cited by 79 · see citing works
“Recently proposed learning based registration methods offer a significant speed-up at test time [8, 9, 12, 17, 18, 23, 47, 46, 61, 65, 71].”
BackgroundProgressively Trained Convolutional Neural Networks for Deformable Image Registration
Eppenhof, K. A. J., Lafarge, M. W., Veta, M., & Pluim, J. P. W. (2019) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2019.2953788 · Cited by 74 · see citing works
“MRIs [4], [6], [13], [17], [19], cardiac MRIs [5], [14], prostate MRIs and ultrasound images [10], and chest CTs [3], [5], [7],”
BackgroundMahapatra, D., & Ge, Z. (2019) · Pattern Recognition · doi:10.1016/j.patcog.2019.107109 · Cited by 72 · 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
mlVIRNET: Multilevel Variational Image Registration Network
Hering, A., van Ginneken, B., & Heldmann, S. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-32226-7_29 · Cited by 71 · see citing works
“al step for many medical image analysis tasks and has been an active eld of research for decades. Since recently, deep learning based approaches have been successfully employed for image registration [2,4,6,7,13,16]. They have shown promising results in a wide range of application. However, capturing large motion and…”
BackgroundMethodNon-rigid image registration using fully convolutional networks with deep self-supervisionPreprint
Li, H., & Yong Fan, A. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1709.00799 · Cited by 70 · see citing works
“Similar to most deep learning tasks, the quality of training data plays an important role in the prediction based image registration, and a variety of strategies have been proposed to build training data, specifically the spatial transformations that register images in a training dataset [7-10].”
BackgroundMethodInfluentialArtificial Intelligence in Radiation Therapy
Fu, Y., Zhang, H., Morris, E. D., Glide‐Hurst, C. K., Pai, S., Traverso, A., Wee, L., Hadžić, I., Lønne, P.-I., Shen, C., et al. (2021) · IEEE Transactions on Radiation and Plasma Medical Sciences · doi:10.1109/trpms.2021.3107454 · Cited by 72 · see citing works
A 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
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
“used a similar network architecture for cardiac MR image registration, and obtained comparable results with conventional methods in terms of contour overlap (90).”
BackgroundArtificial intelligence with deep learning in nuclear medicine and radiology
Decuyper, M., Maebe, J., Holen, R. V., & Vandenberghe, S. (2021) · EJNMMI Physics · doi:10.1186/s40658-021-00426-y · Cited by 60 · see citing works
DNetUnet: a semi-supervised CNN of medical image segmentation for super-computing AI service
Tseng, K.-K., Zhang, R., Chen, C., & Hassan, M. M. (2020) · The Journal of Supercomputing · doi:10.1007/s11227-020-03407-7 · Cited by 60 · see citing works
“Other methods such as SVF-Net [17], or Grid Net [18], are also applied to address this cardiac segmentation problem (Table 1).”
BackgroundMethodHering, 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
“We categorize these approaches into supervised [1], unsupervised [2,4] and weakly-supervised [3] techniques based on how they train the network.”
Symmetric transformer-based network for unsupervised image registration
Ma, M., Xu, Y., Song, L., et al. (2022) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.109959 · Cited by 55 · see citing works
Recursive Decomposition Network for Deformable Image Registration
Hu, B., Zhou, S. K., Xiong, Z., & Wu, F. (2022) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2022.3189696 · Cited by 54 · see citing works
Data augmentation using learned transformations for one-shot medical image segmentationPreprint
Zhao, A. T., Balakrishnan, G., Durand, F., Guttag, J. V., & Dalca, A. V. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1902.09383 · Cited by 54 · see citing works
“This mature field spans optimization-based methods [4, 7, 67, 70], and recent learning-based methods [8, 9, 20, 42, 62, 72, 80].”
MethodDeep 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
“Rohe et al [30] proposed to derive a reference Stationary Velocity Field (SVF) deformation using segmented shapes.”
Blendowski, M., Hansen, L., & Heinrich, M. P. (2020) · Medical Image Analysis · doi:10.1016/j.media.2020.101822 · Cited by 54 · see citing works
Automatic Multi-Atlas Segmentation of Myocardium with SVF-Net
Rohé, M.-M., Maxime Sermesant, B., & Pennec, X. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-75541-0_18 · Cited by 52 · see citing works
“With respect to the state-of-the-art MAS methods, our contribution rely on the use of a very fast and robust registration algorithm [6] specifically trained to perform inter-patient heart registration.”
MethodInfluentialSelf-citationCross-Resolution Distillation for Efficient 3D Medical Image Registration
Hu, B., Zhou, S. K., Xiong, Z., et al. (2022) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2022.3178178 · Cited by 52 · see citing works
“SVF-Net [25] 155 builds reference deformations by utilizing segmented regions 156 of interests and estimates deformation fields with a U-Net 157 structure.”
MethodAdversarial 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
Ye, M., Kanski, M., Yang, D., Chang, Q., Yan, Z., Huang, Q., Axel, L., & Metaxas, D. (2021) · 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/cvpr46437.2021.00718 · Cited by 52 · see citing works
“Among those approaches, supervised methods [42] require ground truth deformation fields, which are usually synthetic.”
MethodImplementation and Validation of a Three-dimensional Cardiac Motion Estimation Network
Morales, M. A., Izquierdo‐Garcia, D., Aganj, I., et al. (2019) · Radiology Artificial Intelligence · doi:10.1148/ryai.2019180080 · Cited by 51 · see citing works
Zampieri, A., Charpiat, G., Girard, N., & Tarabalka, Y. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01270-0_40 · Cited by 51 · see citing works
Wei, W., Poirion, É., Bodini, B., Durrleman, S., Ayache, N., Stankoff, B., & Colliot, O. (2019) · Medical Image Analysis · doi:10.1016/j.media.2019.101546 · Cited by 50 · see citing works
“For the generators, we use the 3D U-Net architecture which is widely used and has achieved competitive performance in both computer vision (Ma et al., 2018; Zhang et al., 2018) and medical imaging fields (Roh´e et al., 2017; Zheng et al., 2018).”
MethodRistea, N.-C., Ionescu, A.-I., Savencu, O., Georgescu, M.-I., Verga, N. I., Khan, F. S., & Ionescu, R. T. (2023) · Neurocomputing · doi:10.1016/j.neucom.2023.03.072 · Cited by 49 · see citing works
“The state-of-the-art methods for medical image alignment are based on deep neural networks [35, 36, 37, 38, 39, 40, 41].”
MethodFast contour propagation for MR‐guided prostate radiotherapy using convolutional neural networks
Eppenhof, K. A. J., Maspero, M., Savenije, M. H. F., de Boer, J. C. J., van Zyp, J. R. N. V. D. V., Raaymakers, B. W., Raaijmakers, A. J. E., Veta, M., van den Berg, C. A. T., & Pluim, J. P. W. (2019) · Medical Physics · doi:10.1002/mp.13994 · Cited by 46 · see citing works
Duong, S. T. M., Phung, S. L., Bouzerdoum, A., et al. (2020) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2020.04.004 · Cited by 46 · see citing works
Learning the Effect of Registration Hyperparameters with HyperMorph
Hoopes, A., Hoffmann, M., Greve, D. N., Fischl, B. R., Guttag, J. V., & Dalca, A. V. (2022) · The Journal of Machine Learning for Biomedical Imaging · doi:10.59275/j.melba.2022-74f1 · Cited by 43 · see citing works
“Supervised models are trained to predict deformation fields that have been been simulated or computed by other techniques (Krebs et al., 2017; Rohé et al., 2017; Sokooti et al., 2017; Yang et al., 2017), whereas unsupervised, or self-supervised, strategies are trained end-to-end and optimize an…”
Learning image-based spatial transformations via convolutional neural networks: A review
Tustison, N. J., Avants, B., & Gee, J. C. (2019) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2019.05.037 · Cited by 42 · 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
“, 3D brain DIR [5], with the side effect that global information about the transformation to learn might be missing [3].”
BackgroundUnderstanding the brain with attention: A survey of transformers in brain sciences
Chen, C., Wang, H., Chen, Y., Yin, Z., Yang, X., Ning, H., Zhang, Q., Li, W., Xiao, R., & Zhao, J. (2023) · Brain‐X · doi:10.1002/brx2.29 · Cited by 43 · see citing works
Wang, C., Tyagi, N., Rimner, A., Hu, Y., Veeraraghavan, H., Li, G., Hunt, M. A., Mageras, G. S., & Zhang, P. (2018) · Radiotherapy and Oncology · doi:10.1016/j.radonc.2018.10.037 · Cited by 40 · see citing works
“As on-going research, a patient-specific registration method is designed using a deep learning model [24,25] to make the system work on a consistent platform with minimal human intervention.”
MethodH-ViT: A Hierarchical Vision Transformer for Deformable Image Registration
Ghahremani, M., Khateri, M., Jian, B., et al. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.01094 · Cited by 39 · see citing works
“Deformable image registration techniques are typically classified into two categories: supervised [19, 31, 47, 51, 70] and unsupervised methods [5, 8, 10, 11, 20, 32, 35, 73, 74, 77].”
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
“Roh´e et al. (2017) proposed to build reference DVFs which were obtained by performing registration over segmented regions of interest.”
MethodBlendowski, M., & Heinrich, M. P. (2018) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-018-1888-2 · Cited by 38 · see citing works
Multimodal 3D medical image registration guided by shape encoder–decoder networks
Blendowski, M., Bouteldja, N., & Heinrich, M. (2019) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-019-02089-8 · Cited by 33 · see citing works
S3Reg: Superfast Spherical Surface Registration Based on Deep Learning
Zhao, F., Wu, Z., Wang, F., Lin, W., Xia, S., Shen, D. G., Wang, L., & Li, G. (2021) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2021.3069645 · Cited by 38 · see citing works
Deformer: Towards Displacement Field Learning for Unsupervised Medical Image Registration
Chen, J., Lu, D., Zhang, Y., Wei, D., Ning, M., Shi, X., Xu, Z., & Zheng, Y. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-16446-0_14 · Cited by 38 · see citing works
Huang, Y., Ahmad, S., Fan, J., Shen, D. G., & Yap, P. (2020) · Medical Image Analysis · doi:10.1016/j.media.2020.101817 · Cited by 37 · see citing works
“…methods can be broadly divided into three categories according to the training strategy: i. Supervised training (Dosovitskiy et al., 2015; Rohé et al., 2017; Sokooti et al., 2017) - Supervised training requires that the training dataset includes the corresponding ground-truth deformation…”
MethodJoshi, A., & Hong, Y. (2023) · Medical Image Analysis · doi:10.1016/j.media.2023.102917 · Cited by 36 · see citing works
Unified model for interpreting multi-view echocardiographic sequences without temporal information
Li, M., Dong, S., Gao, Z., Feng, C., Xiong, H., Zheng, W., Ghista, D. N., Zhang, H., & de Albuquerque, V. H. C. (2020) · Applied Soft Computing · doi:10.1016/j.asoc.2019.106049 · Cited by 37 · see citing works
Deep adaptive registration of multi-modal prostate images
Guo, H., Kruger, M., Xu, S., Wood, B. J., & Yan, P. (2020) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2020.101769 · Cited by 36 · see citing works
“It has been found that typical auxiliary information, such as the atlas-based segmentation, can be used to improve the registration performance [17].”
BackgroundRegion-specific Diffeomorphic Metric Mapping
Shen, Z., Vialard, F., & Niethammer, M. (2019) · PubMed · doi:10.48550/arxiv.1906.00139 · Cited by 35 · see citing works
“Consequentially, various deep-learning (DL) approaches have been proposed to instead predict displacements [4, 6], stationary velocity [29] or momentum fields [43, 23].”
BackgroundU‐net‐based deformation vector field estimation for motion‐compensated 4D‐CBCT reconstruction
Huang, X., Zhang, Y., Chen, L., & Wang, J. (2020) · Medical Physics · doi:10.1002/mp.14150 · Cited by 35 · see citing works
Automatic brain labeling via multi-atlas guided fully convolutional networks
Fang, L., Zhang, L., Nie, D., Cao, X., Rekik, I., Lee, S., He, H., & Shen, D. G. (2018) · Medical Image Analysis · doi:10.1016/j.media.2018.10.012 · Cited by 35 · see citing works
TD-Net:unsupervised medical image registration network based on Transformer and CNN
Song, L., Liu, G., & Ma, M. (2022) · Applied Intelligence · doi:10.1007/s10489-022-03472-w · Cited by 33 · see citing works
Anatomy-aware and acquisition-agnostic joint registration with SynthMorph
Hoffmann, M., Hoopes, A., Greve, D. N., Fischl, B. R., & Dalca, A. V. (2024) · Imaging Neuroscience · doi:10.1162/imag_a_00197 · Cited by 32 · see citing works
“Recent advances in DL have enabled registration with unprecedented efficiency and accuracy (Balakrish-nan et al., 2019; Dalca et al., 2018; Eppenhof and Pluim, 2019; Krebs et al., 2017; Li and Fan, 2017; Roh´e et al., 2017; Sokooti et al., 2017; Yang et al., 2016, 2017).”
BackgroundMethodMR 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
Li, H., Yong Fan, A., & Initiative, A. D. N. (2022) · Human Brain Mapping · doi:10.1002/hbm.25782 · Cited by 32 · see citing works
“The prediction-based image registration algorithms typically adopt convolutional neural networks (CNNs) to learn informative image features and a mapping between the learned image features and spatial transformations that register images in a training dataset, consisting of deformation fields and images that can be re…”
BackgroundMethodInfluentialNeurReg: Neural Registration and Its Application to Image Segmentation
Zhu, W., Myronenko, A., Xu, Z., Li, W., Roth, H. R., Huang, Y., Milletarì, F., & Xu, D. (2020) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv45572.2020.9093506 · Cited by 31 · see citing works
“Deep learning-based registration methods have recently been emerging as a viable alternative to the above conventional methods [31, 39, 43].”
MethodSynthetic data in generalizable, learning-based neuroimaging
Gopinath, K. K., Hoopes, A., Alexander, D. C., Arnold, S. E., Balbastre, Y., Billot, B., Casamitjana, A., Cheng, Y., Chua, R. Y. Z., Edlow, B. L., et al. (2024) · Imaging Neuroscience · doi:10.1162/imag_a_00337 · Cited by 29 · see citing works
“A number of early learning registration approaches optimize a loss comparing the network output to a ground truth warp (Krebs et al., 2017; Roh´e et al., 2017; Yang et al., 2017; Young et al., 2022).”
Image Registration in Medical Robotics and Intelligent Systems: Fundamentals and Applications
Liu, J., Singh, G., Alaref, S. J., Lee, B., Oleru, O. O., Min, J. K., Dunham, S. N., Sabuncu, M. R., & Mosadegh, B. (2019) · Advanced Intelligent Systems · doi:10.1002/aisy.201900048 · Cited by 29 · see citing works
Probabilistic Multilayer Regularization Network for Unsupervised 3D Brain Image Registration
Liu, L., Hu, X., Zhu, L., & Heng, P. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-32245-8_39 · Cited by 29 · see citing works
- Authors
- Rohé, M.-M., Datar, M., Heimann, T., Sermesant, M., & Pennec, X.
- Affiliations
- Université Côte d'Azur (FR)
- Siemens Healthineers (Germany) (DE)
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2017
- Pages
- 266-274
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2752785527
- Semantic Scholar
- f9a53dcd92bd7c1f4e1689a08ad33dae77209eef
- Cited by
- 330
- References
- 11
- Citations by year
- 2026: 7 · 2025: 25 · 2024: 26 · 2023: 35 · 2022: 54 · 2021: 50 · 2020: 44 · 2019: 60 · 2018: 28 · 2017: 1
- FWCI
- 10.63
- Type
- conference paper
- Language
- en
- Open access
- green
- inria.hal.science
- Licence
- Locations
- Lecture notes in computer science (publishedVersion)
- HAL (submittedVersion, other-oa)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Physical Sciences › Engineering › Computational Mechanics › 3D Shape Modeling and Analysis
- Physical Sciences › Physics and Astronomy › Radiation › Advanced Radiotherapy Techniques
- Keywords
- deformable image registration, shape matching, fully convolutional network, training strategies, 3D medical image registration, outlier robustness
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