Deep Learning in Medical Image Registration: A Review

This paper presents a review of deep learning (DL) based medical image registration methods. We summarized the latest developments and applications of DL-based registration methods in the medical field. These methods were classified into seven categories according to their methods, functions and popularity. A detailed review of each category was presented, highlighting important contributions and identifying specific challenges. A short assessment was presented following the detailed review of each category to summarize its achievements and future potentials. We provided a comprehensive comparison among DL-based methods for lung and brain registration using benchmark datasets. Lastly, we analyzed the statistics of all the cited works from various aspects, revealing the popularity and future trend of DL-based medical image registration.

[1]  Thomas Brox,et al.  FlowNet: Learning Optical Flow with Convolutional Networks , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[2]  Yang Lei,et al.  Whole-body PET estimation from low count statistics using cycle-consistent generative adversarial networks , 2019, Physics in medicine and biology.

[3]  Yang Lei,et al.  Optimal virtual monoenergetic image in “TwinBeam” dual‐energy CT for organs‐at‐risk delineation based on contrast‐noise‐ratio in head‐and‐neck radiotherapy , 2019, Journal of applied clinical medical physics.

[4]  Lipo Wang,et al.  Deep Learning Applications in Medical Image Analysis , 2018, IEEE Access.

[5]  Zhenwei Zhang,et al.  Radiological images and machine learning: trends, perspectives, and prospects , 2019, Comput. Biol. Medicine.

[6]  Mitko Veta,et al.  Deformable image registration using convolutional neural networks , 2018, Medical Imaging.

[7]  Maxime Sermesant,et al.  SVF-Net: Learning Deformable Image Registration Using Shape Matching , 2017, MICCAI.

[8]  Dorin Comaniciu,et al.  Multi-Scale Deep Reinforcement Learning for Real-Time 3D-Landmark Detection in CT Scans , 2019, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  Yonggang Lu,et al.  A novel MRI segmentation method using CNN‐based correction network for MRI‐guided adaptive radiotherapy , 2018, Medical physics.

[10]  Albert C. S. Chung,et al.  A novel learning-based dissimilarity metric for rigid and non-rigid medical image registration by using Bhattacharyya Distances , 2017, Pattern Recognit..

[11]  Vince D. Calhoun,et al.  Variational Autoencoders for Feature Detection of Magnetic Resonance Imaging Data , 2016, ArXiv.

[12]  Marc Niethammer,et al.  Quicksilver: Fast predictive image registration – A deep learning approach , 2017, NeuroImage.

[13]  J H Siewerdsen,et al.  dPIRPLE: a joint estimation framework for deformable registration and penalized-likelihood CT image reconstruction using prior images , 2014, Physics in medicine and biology.

[14]  Xiao Han,et al.  Atlas-Based Auto-segmentation of Head and Neck CT Images , 2008, MICCAI.

[15]  Andrew Zisserman,et al.  Spatial Transformer Networks , 2015, NIPS.

[16]  Michael Brady,et al.  MIND: Modality independent neighbourhood descriptor for multi-modal deformable registration , 2012, Medical Image Anal..

[17]  Puneet Sharma,et al.  Nonrigid registration and classification of the kidneys in 3D dynamic contrast enhanced (DCE) MR images , 2012, Medical Imaging.

[18]  Hervé Delingette,et al.  Unsupervised Probabilistic Deformation Modeling for Robust Diffeomorphic Registration , 2018, DLMIA/ML-CDS@MICCAI.

[19]  Mert R. Sabuncu,et al.  VoxelMorph: A Learning Framework for Deformable Medical Image Registration , 2018, IEEE Transactions on Medical Imaging.

[20]  Sébastien Ourselin,et al.  Weakly-supervised convolutional neural networks for multimodal image registration , 2018, Medical Image Anal..

[21]  Won-Ki Jeong,et al.  ssEMnet: Serial-Section Electron Microscopy Image Registration Using a Spatial Transformer Network with Learned Features , 2017, DLMIA/ML-CDS@MICCAI.

[22]  Tian Liu,et al.  Deeply supervised 3D fully convolutional networks with group dilated convolution for automatic MRI prostate segmentation , 2019, Medical physics.

[23]  Thomas Brox,et al.  Generating Images with Perceptual Similarity Metrics based on Deep Networks , 2016, NIPS.

[24]  N Kandasamy,et al.  On developing B-spline registration algorithms for multi-core processors , 2010, Physics in medicine and biology.

[25]  Marius Staring,et al.  Adversarial optimization for joint registration and segmentation in prostate CT radiotherapy , 2019, MICCAI.

[26]  Sébastien Ourselin,et al.  Fast free-form deformation using graphics processing units , 2010, Comput. Methods Programs Biomed..

[27]  Hongbin Zha,et al.  Non-rigid Craniofacial 2D-3D Registration Using CNN-Based Regression , 2017, DLMIA/ML-CDS@MICCAI.

[28]  Tian Liu,et al.  MRI-based treatment planning for brain stereotactic radiosurgery: Dosimetric validation of a learning-based pseudo-CT generation method. , 2019, Medical dosimetry : official journal of the American Association of Medical Dosimetrists.

[29]  Yang Lei,et al.  Deep learning-based attenuation correction in the absence of structural information for whole-body positron emission tomography imaging , 2019, Physics in medicine and biology.

[30]  Kaiming He,et al.  Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[31]  Ben Glocker,et al.  On the Adaptability of Unsupervised CNN-Based Deformable Image Registration to Unseen Image Domains , 2018, MLMI@MICCAI.

[32]  Alan L. Yuille,et al.  The Concave-Convex Procedure , 2003, Neural Computation.

[33]  Thomas Brox,et al.  U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.

[34]  Yoshua Bengio,et al.  Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling , 2014, ArXiv.

[35]  Demis Hassabis,et al.  Mastering the game of Go with deep neural networks and tree search , 2016, Nature.

[36]  Aaron Fenster,et al.  Automatic 3D segmentation of ultrasound images using atlas registration and statistical texture prior , 2011, Medical Imaging.

[37]  Dinggang Shen,et al.  Image registration by local histogram matching , 2007, Pattern Recognit..

[38]  Zhengyang Zhou,et al.  Automated segmentation of the parotid gland based on atlas registration and machine learning: a longitudinal MRI study in head-and-neck radiation therapy. , 2014, International journal of radiation oncology, biology, physics.

[39]  Ben Glocker,et al.  Quantitative Error Prediction of Medical Image Registration using Regression Forests , 2019, Medical Image Anal..

[40]  Yang Lei,et al.  4D-CT Deformable Image Registration Using an Unsupervised Deep Convolutional Neural Network , 2019, AIRT@MICCAI.

[41]  Tanya Schmah,et al.  FAIM - A ConvNet Method for Unsupervised 3D Medical Image Registration , 2018, MLMI@MICCAI.

[42]  Shane Legg,et al.  Human-level control through deep reinforcement learning , 2015, Nature.

[43]  Jong Chul Ye,et al.  Unsupervised Deformable Image Registration Using Cycle-Consistent CNN , 2019, MICCAI.

[44]  Nikos Komodakis,et al.  A Deep Metric for Multimodal Registration , 2016, MICCAI.

[45]  Josien P. W. Pluim,et al.  Pulmonary CT Registration Through Supervised Learning With Convolutional Neural Networks , 2019, IEEE Transactions on Medical Imaging.

[46]  Boudewijn P. F. Lelieveldt,et al.  Nonrigid Image Registration Using Multi-scale 3D Convolutional Neural Networks , 2017, MICCAI.

[47]  Yang Lei,et al.  CT Prostate Segmentation Based on Synthetic MRI-aided Deep Attention Fully Convolution Network. , 2019, Medical physics.

[48]  Xiangrong Zhou,et al.  Learning 3D non-rigid deformation based on an unsupervised deep learning for PET/CT image registration , 2019, Medical Imaging.

[49]  Z. Jane Wang,et al.  A CNN Regression Approach for Real-Time 2D/3D Registration , 2016, IEEE Transactions on Medical Imaging.

[50]  Nikos Paragios,et al.  Linear and Deformable Image Registration with 3D Convolutional Neural Networks , 2018, RAMBO+BIA+TIA@MICCAI.

[51]  Max A. Viergever,et al.  elastix: A Toolbox for Intensity-Based Medical Image Registration , 2010, IEEE Transactions on Medical Imaging.

[52]  Russell H. Taylor,et al.  Medical robotics in computer-integrated surgery , 2003, IEEE Trans. Robotics Autom..

[53]  Wei Lu,et al.  Technical note: deformable image registration on partially matched images for radiotherapy applications. , 2009, Medical physics.

[54]  Max A. Viergever,et al.  End-to-End Unsupervised Deformable Image Registration with a Convolutional Neural Network , 2017, DLMIA/ML-CDS@MICCAI.

[55]  D Sarrut,et al.  Registration of sliding objects using direction dependent B-splines decomposition , 2013, Physics in medicine and biology.

[56]  Yang Lei,et al.  4D-CT deformable image registration using multiscale unsupervised deep learning , 2020, Physics in medicine and biology.

[57]  M. Modat,et al.  A generalized framework unifying image registration and respiratory motion models and incorporating image reconstruction, for partial image data or full images , 2017, Physics in medicine and biology.

[58]  Simon Osindero,et al.  Conditional Generative Adversarial Nets , 2014, ArXiv.

[59]  Dongyang Kuang,et al.  On Reducing Negative Jacobian Determinant of the Deformation Predicted by Deep Registration Networks , 2019, ArXiv.

[60]  Theo van Walsum,et al.  Towards Robust CT-Ultrasound Registration Using Deep Learning Methods , 2018, MLCN/DLF/iMIMIC@MICCAI.

[61]  Hervé Delingette,et al.  Robust Non-rigid Registration Through Agent-Based Action Learning , 2017, MICCAI.

[62]  Zhe-Ming Lu,et al.  Multimodal medical image registration via common representations learning and differentiable geometric constraints , 2019, Electronics Letters.

[63]  Heinz Handels,et al.  Training CNNs for Image Registration from Few Samples with Model-based Data Augmentation , 2017, MICCAI.

[64]  Brian B. Avants,et al.  Symmetric diffeomorphic image registration with cross-correlation: Evaluating automated labeling of elderly and neurodegenerative brain , 2008, Medical Image Anal..

[65]  Bishesh Khanal,et al.  LSTM Spatial Co-transformer Networks for Registration of 3D Fetal US and MR Brain Images , 2018, DATRA/PIPPI@MICCAI.

[66]  Sheng Xu,et al.  Learning deep similarity metric for 3D MR–TRUS image registration , 2018, International Journal of Computer Assisted Radiology and Surgery.

[67]  Tian Liu,et al.  3D transrectal ultrasound (TRUS) prostate segmentation based on optimal feature learning framework , 2016, SPIE Medical Imaging.

[68]  Kilian Q. Weinberger,et al.  Densely Connected Convolutional Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[69]  D. Louis Collins,et al.  Nonrigid Registration of Ultrasound and MRI Using Contextual Conditioned Mutual Information , 2014, IEEE Transactions on Medical Imaging.

[70]  Pingge Jiang,et al.  CNN Driven Sparse Multi-level B-Spline Image Registration , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[71]  Jun Zhang,et al.  Inverse-Consistent Deep Networks for Unsupervised Deformable Image Registration , 2018, ArXiv.

[72]  Dinggang Shen,et al.  Adversarial Similarity Network for Evaluating Image Alignment in Deep Learning Based Registration , 2018, MICCAI.

[73]  A. Schmidt-Richberg,et al.  Estimation of lung motion fields in 4D CT data by variational non-linear intensity-based registration: A comparison and evaluation study , 2014, Physics in medicine and biology.

[74]  A Uneri,et al.  3D–2D image registration for target localization in spine surgery: investigation of similarity metrics providing robustness to content mismatch , 2016, Physics in medicine and biology.

[75]  Nikos Paragios,et al.  Weakly Supervised Learning of Metric Aggregations for Deformable Image Registration , 2018, IEEE Journal of Biomedical and Health Informatics.

[76]  Steve B. Jiang,et al.  Real-time volumetric image reconstruction and 3D tumor localization based on a single x-ray projection image for lung cancer radiotherapy. , 2010, Medical physics.

[77]  Michael Velec,et al.  Effect of deformable registration uncertainty on lung SBRT dose accumulation. , 2015, Medical physics.

[78]  Sarang C. Joshi,et al.  Real-Time 2D-3D Deformable Registration with Deep Learning and Application to Lung Radiotherapy Targeting , 2019, IPMI.

[79]  Dwarikanath Mahapatra,et al.  Combining Transfer Learning And Segmentation Information with GANs for Training Data Independent Image Registration , 2019, ArXiv.

[80]  Ji Luo,et al.  Unsupervised 3D End-to-End Medical Image Registration With Volume Tweening Network , 2019, IEEE Journal of Biomedical and Health Informatics.

[81]  Max A. Viergever,et al.  Registration of organs with sliding interfaces and changing topologies , 2014, Medical Imaging.

[82]  Jing Hu,et al.  Robust Multimodal Image Registration Using Deep Recurrent Reinforcement Learning , 2018, ACCV.

[83]  Li Sun,et al.  Deformable MRI-Ultrasound Registration Using 3D Convolutional Neural Network , 2018, POCUS/BIVPCS/CuRIOUS/CPM@MICCAI.

[84]  Yang Lei,et al.  Deep learning-based image quality improvement for low-dose computed tomography simulation in radiation therapy , 2019, Journal of medical imaging.

[85]  Fang-Fang Yin,et al.  A multi-scale framework with unsupervised joint training of convolutional neural networks for pulmonary deformable image registration , 2019, Physics in medicine and biology.

[86]  Thenkurussi Kesavadas,et al.  A Novel Framework for 3D-2D Vertebra Matching , 2019, 2019 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR).

[87]  Richard Szeliski,et al.  Spline-Based Image Registration , 1997, International Journal of Computer Vision.

[88]  Mert R. Sabuncu,et al.  An Unsupervised Learning Model for Deformable Medical Image Registration , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[89]  Zeyun Yu,et al.  Multi-Modal Medical Image Registration with Full or Partial Data: A Manifold Learning Approach , 2018, J. Imaging.

[90]  Michael Velec,et al.  Effect of breathing motion on radiotherapy dose accumulation in the abdomen using deformable registration. , 2011, International journal of radiation oncology, biology, physics.

[91]  Yang Lei,et al.  Dosimetric study on learning-based cone-beam CT correction in adaptive radiation therapy. , 2019, Medical dosimetry : official journal of the American Association of Medical Dosimetrists.

[92]  Jian Zheng,et al.  Non-rigid MR-TRUS image registration for image-guided prostate biopsy using correlation ratio-based mutual information , 2017, Biomedical engineering online.

[93]  Mert R. Sabuncu,et al.  Unsupervised Learning for Fast Probabilistic Diffeomorphic Registration , 2018, MICCAI.

[94]  Rabab Kreidieh Ward,et al.  Deep learning for pixel-level image fusion: Recent advances and future prospects , 2018, Inf. Fusion.

[95]  Xi Chen,et al.  An unsupervised network for fast microscopic image registration , 2018, Medical Imaging.

[96]  Chee-Kong Chui,et al.  Motion Tracking and Strain Map Computation for Quasi-Static Magnetic Resonance Elastography , 2011, MICCAI.

[97]  Stefan Heldmann,et al.  Enhancing Label-Driven Deep Deformable Image Registration with Local Distance Metrics for State-of-the-Art Cardiac Motion Tracking , 2018, Bildverarbeitung für die Medizin.

[98]  Marius Staring,et al.  3D Convolutional Neural Networks Image Registration Based on Efficient Supervised Learning from Artificial Deformations , 2019, ArXiv.

[99]  Dean C. Barratt,et al.  Adversarial Deformation Regularization for Training Image Registration Neural Networks , 2018, MICCAI.

[100]  Sasa Mutic,et al.  Technical note: DIRART--A software suite for deformable image registration and adaptive radiotherapy research. , 2011, Medical physics.

[101]  Yang Lei,et al.  MRI-based treatment planning for liver stereotactic body radiotherapy: validation of a deep learning-based synthetic CT generation method. , 2019, The British journal of radiology.

[102]  Zhenzhou Wu,et al.  AIRNet: Self-Supervised Affine Registration for 3D Medical Images using Neural Networks , 2018, ArXiv.

[103]  Klaus H. Maier-Hein,et al.  Context-encoding Variational Autoencoder for Unsupervised Anomaly Detection , 2018, ArXiv.

[104]  Tian Liu,et al.  MRI-based synthetic CT generation using semantic random forest with iterative refinement , 2019, Physics in medicine and biology.

[105]  Tian Liu,et al.  Paired cycle-GAN based image correction for quantitative cone-beam CT. , 2019, Medical physics.

[106]  Nilanjan Ray,et al.  Deep deformable registration: Enhancing accuracy by fully convolutional neural net , 2016, Pattern Recognit. Lett..

[107]  David Sarrut,et al.  Deformable registration for image-guided radiation therapy. , 2006, Zeitschrift fur medizinische Physik.

[108]  John W. Clark,et al.  A motion-incorporated reconstruction method for gated PET studies , 2006, Physics in medicine and biology.

[109]  Xue Wu,et al.  Automatic large quantity landmark pairs detection in 4DCT lung images. , 2019, Medical physics.

[110]  Arno Klein,et al.  A reproducible evaluation of ANTs similarity metric performance in brain image registration , 2011, NeuroImage.

[111]  Yang Lei,et al.  MRI-only based synthetic CT generation using dense cycle consistent generative adversarial networks. , 2019, Medical physics.

[112]  Deshan Yang,et al.  A fast inverse consistent deformable image registration method based on symmetric optical flow computation , 2008, 2008 42nd Asilomar Conference on Signals, Systems and Computers.

[113]  Rui Liao,et al.  Dilated FCN for Multi-Agent 2D/3D Medical Image Registration , 2017, AAAI.

[114]  Gilmer Valdes,et al.  An unsupervised convolutional neural network-based algorithm for deformable image registration , 2018, Physics in medicine and biology.

[115]  Syed Muhammad Anwar,et al.  Deep Learning in Medical Image Analysis , 2017 .

[116]  Raúl San José Estépar,et al.  Diffeomorphic Lung Registration Using Deep CNNs and Reinforced Learning , 2018, RAMBO+BIA+TIA@MICCAI.

[117]  Dwarikanath Mahapatra,et al.  Deformable medical image registration using generative adversarial networks , 2018, 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018).

[118]  Michael Brady,et al.  MRF-Based Deformable Registration and Ventilation Estimation of Lung CT , 2013, IEEE Transactions on Medical Imaging.

[119]  Daniel Rueckert,et al.  Joint Learning of Motion Estimation and Segmentation for Cardiac MR Image Sequences , 2018, MICCAI.

[120]  Dinggang Shen,et al.  Scalable High-Performance Image Registration Framework by Unsupervised Deep Feature Representations Learning , 2016, IEEE Transactions on Biomedical Engineering.

[121]  Daniel A Low,et al.  A neural network approach for fast, automated quantification of DIR performance , 2017, Medical physics.

[122]  Yang Lei,et al.  A learning-based automatic segmentation and quantification method on left ventricle in gated myocardial perfusion SPECT imaging: A feasibility study , 2019, Journal of Nuclear Cardiology.

[123]  Baowei Fei,et al.  3D prostate segmentation of ultrasound images combining longitudinal image registration and machine learning , 2012, Medical Imaging.

[124]  Pingkun Yan,et al.  Deep learning in medical image registration: a survey , 2020, Machine Vision and Applications.

[125]  Bram van Ginneken,et al.  A survey on deep learning in medical image analysis , 2017, Medical Image Anal..

[126]  Bram Bakker,et al.  Reinforcement Learning with Long Short-Term Memory , 2001, NIPS.

[127]  Kari Tanderup,et al.  Simple DVH parameter addition as compared to deformable registration for bladder dose accumulation in cervix cancer brachytherapy. , 2013, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[128]  Jung-Woo Ha,et al.  StarGAN: Unified Generative Adversarial Networks for Multi-domain Image-to-Image Translation , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[129]  Dwarikanath Mahapatra,et al.  Elastic Registration of Medical Images With GANs , 2018, ArXiv.

[130]  Indrin J Chetty,et al.  Deformable Registration for Dose Accumulation. , 2019, Seminars in radiation oncology.

[131]  Avinash Kori,et al.  Zero Shot Learning for Multi-Modal Real Time Image Registration , 2019, ArXiv.

[132]  Alexei A. Efros,et al.  Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[133]  Marleen de Bruijne,et al.  Automated Image Registration Quality Assessment Utilizing Deep-learning based Ventricle Extraction in Clinical Data , 2019, ArXiv.

[134]  Li Zhang,et al.  Deep similarity learning for multimodal medical images , 2018, Comput. methods Biomech. Biomed. Eng. Imaging Vis..

[135]  Baowei Fei,et al.  3D non-rigid registration using surface and local salient features for transrectal ultrasound image-guided prostate biopsy , 2011, Medical Imaging.

[136]  Wen Yan,et al.  Unsupervised End-to-end Learning for Deformable Medical Image Registration , 2017, ArXiv.

[137]  Mohsen Guizani,et al.  Deep Features Learning for Medical Image Analysis with Convolutional Autoencoder Neural Network , 2017 .

[138]  Yang Lei,et al.  Synthetic CT generation from non-attenuation corrected PET images for whole-body PET imaging , 2019, Physics in medicine and biology.

[139]  Ben Glocker,et al.  Attention Gated Networks: Learning to Leverage Salient Regions in Medical Images , 2018, Medical Image Anal..

[140]  René Werner,et al.  GDL-FIRE ^\text 4D : Deep Learning-Based Fast 4D CT Image Registration , 2018, MICCAI.

[141]  Dumitru Erhan,et al.  Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[142]  Sheng Xu,et al.  Adversarial Image Registration with Application for MR and TRUS Image Fusion , 2018, MLMI@MICCAI.

[143]  Pierre Baldi,et al.  Autoencoders, Unsupervised Learning, and Deep Architectures , 2011, ICML Unsupervised and Transfer Learning.

[144]  Alex Lallement,et al.  Survey on deep learning for radiotherapy , 2018, Comput. Biol. Medicine.

[145]  K. Brock,et al.  Accuracy of finite element model-based multi-organ deformable image registration. , 2005, Medical physics.

[146]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[147]  Josien P W Pluim,et al.  Error estimation of deformable image registration of pulmonary CT scans using convolutional neural networks , 2018, Journal of medical imaging.

[148]  Dinggang Shen,et al.  Deep Learning based Inter-Modality Image Registration Supervised by Intra-Modality Similarity , 2018, MLMI@MICCAI.

[149]  Xiaohuan Cao,et al.  Adversarial learning for mono- or multi-modal registration , 2019, Medical Image Anal..

[150]  Max A. Viergever,et al.  A deep learning framework for unsupervised affine and deformable image registration , 2018, Medical Image Anal..

[151]  Berkman Sahiner,et al.  Deep learning in medical imaging and radiation therapy. , 2018, Medical physics.

[152]  Maoguo Gong,et al.  A Novel Coarse-to-Fine Scheme for Automatic Image Registration Based on SIFT and Mutual Information , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[153]  Dorin Comaniciu,et al.  An Artificial Agent for Anatomical Landmark Detection in Medical Images , 2016, MICCAI.

[154]  Marius Staring,et al.  Robust contour propagation using deep learning and image registration for online adaptive proton therapy of prostate cancer , 2019, Medical physics.

[155]  Dinggang Shen,et al.  BIRNet: Brain image registration using dual‐supervised fully convolutional networks , 2018, Medical Image Anal..

[156]  Tian Liu,et al.  Automatic multiorgan segmentation in thorax CT images using U-net-GAN. , 2019, Medical physics.

[157]  Hong-sheng Yin,et al.  A novel improved deep convolutional neural network model for medical image fusion , 2018, Cluster Computing.

[158]  R. Castillo,et al.  A framework for evaluation of deformable image registration spatial accuracy using large landmark point sets , 2009, Physics in medicine and biology.

[159]  Tian Liu,et al.  MRI-based Treatment Planning for Proton Radiotherapy: Dosimetric Validation of a Deep Learning-based Liver Synthetic CT Generation Method , 2019, Physics in medicine and biology.

[160]  C. L. Giles,et al.  Dynamic recurrent neural networks: Theory and applications , 1994, IEEE Trans. Neural Networks Learn. Syst..

[161]  Robert Babuska,et al.  A Survey of Actor-Critic Reinforcement Learning: Standard and Natural Policy Gradients , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[162]  Rodney A. Kennedy,et al.  A Survey of Medical Image Registration on Multicore and the GPU , 2010, IEEE Signal Processing Magazine.

[163]  Dimos Baltas,et al.  One-Shot Learning for Deformable Medical Image Registration and Periodic Motion Tracking , 2019, IEEE Transactions on Medical Imaging.

[164]  Eduardo G Moros,et al.  Voxel-based dose reconstruction for total body irradiation with helical tomotherapy. , 2012, International journal of radiation oncology, biology, physics.

[165]  William M. Wells,et al.  Semi-Supervised Deep Metrics for Image Registration , 2018, ArXiv.

[166]  Dinggang Shen,et al.  Deformable Image Registration Using a Cue-Aware Deep Regression Network , 2018, IEEE Transactions on Biomedical Engineering.

[167]  Stefan Klein,et al.  Pulmonary Image Registration with elastix using a Standard Intensity-Based Algorithm , 2010 .

[168]  Yang Lei,et al.  Synthetic MRI-aided multi-organ segmentation on male pelvic CT using cycle consistent deep attention network. , 2019, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[169]  Yang Lei,et al.  Learning‐based CBCT correction using alternating random forest based on auto‐context model , 2018, Medical physics.

[170]  Dwarikanath Mahapatra,et al.  Joint Registration And Segmentation Of Xray Images Using Generative Adversarial Networks , 2018, MLMI@MICCAI.

[171]  Yang Lei,et al.  Ultrasound prostate segmentation based on multidirectional deeply supervised V-Net. , 2019, Medical physics.

[172]  Nilanjan Ray,et al.  Unsupervised deformable image registration with fully connected generative neural network , 2018 .

[173]  Tom Vercauteren,et al.  Diffeomorphic demons: Efficient non-parametric image registration , 2009, NeuroImage.

[174]  Daniel Rueckert,et al.  Unsupervised Deformable Registration for Multi-Modal Images via Disentangled Representations , 2019, IPMI.

[175]  Xiaoying Wang,et al.  Respiratory motion correction for free-breathing 3D abdominal MRI using CNN-based image registration: a feasibility study. , 2018, The British journal of radiology.

[176]  Rui Liao,et al.  Pairwise domain adaptation module for CNN-based 2-D/3-D registration , 2018, Journal of medical imaging.

[177]  Dorin Comaniciu,et al.  An Artificial Agent for Robust Image Registration , 2016, AAAI.

[178]  Christian Riess,et al.  A Gentle Introduction to Deep Learning in Medical Image Processing , 2018, Zeitschrift fur medizinische Physik.

[179]  Yoshua Bengio,et al.  Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation , 2014, EMNLP.

[180]  W. Tomé,et al.  On the dosimetric effect and reduction of inverse consistency and transitivity errors in deformable image registration for dose accumulation. , 2011, Medical physics.

[181]  J. Paul Siebert,et al.  Learning Rigid Image Registration - Utilizing Convolutional Neural Networks for Medical Image Registration , 2018, BIOIMAGING.

[182]  Deshan Yang,et al.  Automatic and hierarchical segmentation of the human skeleton in CT images , 2017, Physics in medicine and biology.

[183]  Shaikat M Galib,et al.  A Fast and Scalable Method for Quality Assurance of Deformable Image Registration on Lung CT Scans using Convolutional Neural Networks. , 2019, Medical physics.

[184]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[185]  Won-Ki Jeong,et al.  Weakly Supervised Learning in Deformable EM Image Registration Using Slice Interpolation , 2019, 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019).

[186]  Deniz Erdogmus,et al.  Real-time Deep Registration With Geodesic Loss , 2018, ArXiv.

[187]  Jiangping Wang,et al.  Multimodal Image Registration with Deep Context Reinforcement Learning , 2017, MICCAI.

[188]  Yong Fan,et al.  Non-rigid image registration using self-supervised fully convolutional networks without training data , 2018, 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018).

[189]  Zhijian Song,et al.  Image synthesis-based multi-modal image registration framework by using deep fully convolutional networks , 2018, Medical & Biological Engineering & Computing.

[190]  Yang Lei,et al.  LungRegNet: an unsupervised deformable image registration method for 4D-CT lung. , 2020, Medical physics.