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[null,null,"Titolo",null,"Medical Image Computing and Computer Assisted Intervention – MICCAI 2022 : 25th International Conference, Singapore, September 18–22, 2022, Proceedings, Part VI / / edited by Linwei Wang, Qi Dou, P. Thomas Fletcher, Stefanie Speidel, Shuo Li"]
[null,null,"Pubbl/distr/stampa",null,"Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2022"]
[null,null,"ISBN",null,"9783031164460"]
[{"$nulls":4},"3031164466"]
[null,null,"Edizione",null,"[1st ed. 2022.]"]
[null,null,"Descrizione fisica",null,"1 online resource (841 pages)"]
[null,null,"Collana",null,"Lecture Notes in Computer Science, , 1611-3349 ; ; 13436"]
[null,null,"Disciplina",null,"616.07540285"]
[null,null,"Soggetti",null,"Image processing"]
[{"$nulls":4},"Image Processing"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and index."]
[null,null,"Nota di contenuto",null,"Intro -- Preface -- Organization -- Contents - Part VI -- Image Registration -- SVoRT: Iterative Transformer for Slice-to-Volume Registration in Fetal Brain MRI -- 1 Introduction -- 2 Methods -- 2.1 Transformation Update -- 2.2 Volume Estimation -- 2.3 Training -- 3 Experiments and Results -- 3.1 Experiment Setup -- 3.2 Simulated Data -- 3.3 Real Fetal MR Data -- 4 Conclusion -- References -- Double-Uncertainty Guided Spatial and Temporal Consistency Regularization Weighting for Learning-Based Abdominal Registration -- 1 Introduction -- 2 Methods -- 2.1 Mean-Teacher Based Temporal Consistency Regularization -- 2.2 Double-Uncertainty Guided Adaptive Weighting -- 3 Experiments and Results -- 4 Conclusion -- References -- Unsupervised Deformable Image Registration with Absent Correspondences in Pre-operative and Post-recurrence Brain Tumor MRI Scans -- 1 Introduction -- 2 Methods -- 2.1 Bidirectional Deformable Image Registration -- 2.2 Forward-Backward Consistency Constraint -- 2.3 Inverse Consistency -- 2.4 Objective Function -- 3 Experiments -- 4 Conclusion -- References -- On the Dataset Quality Control for Image Registration Evaluation -- 1 Introduction -- 2 Method -- 2.1 Constructing the Variogram -- 2.2 Potential FLEs -- 2.3 Atypical Variogram Patterns -- 3 Experiments -- 4 Discussion -- References -- Dual-Branch Squeeze-Fusion-Excitation Module for Cross-Modality Registration of Cardiac SPECT and CT -- 1 Introduction "]
[{"$nulls":4},"-- 2 Methods -- 2.1 Dataset and Preprocessing -- 2.2 Dual-Branch Squeeze-Fusion-Excitation Module -- 2.3 Deep Registration and Fully Connected Layers -- 2.4 Implementation Details -- 2.5 Quantitative Evaluations -- 3 Results -- 4 Conclusion -- References -- Embedding Gradient-Based Optimization in Image Registration Networks -- 1 Introduction -- 2 Method -- 3 Experiments -- 4 Conclusion -- References."]
[{"$nulls":4},"ContraReg: Contrastive Learning of Multi-modality Unsupervised Deformable Image Registration -- 1 Introduction -- 2 Related Work -- 3 Methods -- 4 Experiments -- 5 Discussion -- References -- Swin-VoxelMorph: A Symmetric Unsupervised Learning Model for Deformable Medical Image Registration Using Swin Transformer -- 1 Introduction -- 2 Method -- 2.1 Network Structures -- 2.2 Loss Function -- 3 Experiments -- 3.1 Datasets, Preprocessing and Evaluation Criteria -- 3.2 Results -- 4 Conclusions -- References -- Non-iterative Coarse-to-Fine Registration Based on Single-Pass Deep Cumulative Learning -- 1 Introduction -- 2 Method -- 2.1 Selectively-Propagated Feature Learning (SFL) -- 2.2 Single-Pass Deep Cumulative Learning (SDCL) -- 2.3 Unsupervised Training -- 3 Experimental Setup -- 3.1 Datasets -- 3.2 Implementation Details -- 3.3 Comparison Methods -- 3.4 Experimental Settings -- 4 Results and Discussion -- 5 Conclusion -- References -- DSR: Direct Simultaneous Registration for Multiple 3D Images -- 1 Introduction -- 2 Methodology -- 2.1 Direct Bundle Adjustment -- 2.2 Simultaneous Registration Without Intensity Optimization -- 3 Experiments and Results -- 3.1 Simulated Experiments -- 3.2 In-Vivo Experiments -- 4 Conclusion -- References -- Multi-modal Retinal Image Registration Using a Keypoint-Based Vessel Structure Aligning Network -- 1 Introduction -- 2 Methods -- 2.1 Synthetic Augmentations for Multi-modal Retinal Images -- 2.2 Multi-modal Retinal Keypoint Detection and Description Network -- 2.3 Keypoint Matching Using a Graph Convolutional Neural Network -- 3 Experiments -- 3.1 Multi-modal Retinal Datasets -- 3.2 Implementation and Experimental Details -- 3.3 Results -- 4 Conclusion -- References -- A Deep-Discrete Learning Framework for Spherical Surface Registration -- 1 Introduction -- 2 Method -- 2.1 Rotation Architecture."]
[{"$nulls":4},"2.2 Deep-Discrete Networks -- 3 Experiments -- 4 Conclusions -- References -- Privacy Preserving Image Registration -- 1 Introduction -- 2 Problem Statement -- 3 Methods -- 3.1 Secure Computation -- 3.2 PPIR: Privacy Preserving Image Registration -- 4 Experimental Results -- 5 Conclusion -- References -- Deformer: Towards Displacement Field Learning for Unsupervised Medical Image Registration -- 1 Introduction -- 2 Method -- 3 Experiments and Discussion -- 4 Conclusion -- References -- End-to-End Multi-Slice-to-Volume Concurrent Registration and Multimodal Generation -- 1 Introduction -- 2 Methods -- 2.1 Synthetic CT Generation from MR -- 2.2 Multi-Slice-to-Volume Registration -- 3 Experiments and Results -- 3.1 Dataset and Preprocessing -- 3.2 Implementation Details -- 3.3 Baseline Methods -- 3.4 Results for MR-to-CT Translation -- 3.5 Results for Multi-Slice-to-Volume Registration -- 4 Conclusion -- References -- Fast Spherical Mapping of Cortical Surface Meshes Using Deep Unsupervised Learning -- 1 Introduction -- 2 Method -- 2.1 Overall Design and Conception -- 2.2 Coarse-to-Fine Multi-resolution Framework -- 2.3 Loss Functions -- 3 Experiments and Results -- 3.1 Experimental Setting -- 3.2 Results -- 4 Conclusion -- References -- Learning-Based US-MR Liver Image Registration with Spatial Priors -- 1 Introduction -- 2 Methods -- 3 Results and Discussion -- 4 Conclusion -- References -- Unsupervised Deep Non-rigid Alignment by Low-Rank "]
[{"$nulls":4},"Loss and Multi-input Attention -- 1 Introduction -- 2 Deep Non-rigid Alignment Using Low-Rank Loss -- 3 Experiments -- 4 Conclusion -- References -- Transformer Lesion Tracker -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Feature Extractor and Sparse Selection Strategy -- 3.2 Cross Attention-Based Transformer -- 3.3 Center Predictor and Training Loss -- 4 Experiments and Experimental Results."]
[{"$nulls":4},"4.1 Dataset and Experiment Setup -- 4.2 Experimental Results and Discussion -- 5 Conclusion -- References -- LiftReg: Limited Angle 2D/3D Deformable Registration -- 1 Introduction -- 2 Problem Formulation -- 3 Method -- 3.1 PCA-Based Deformation Vector Field Subspace -- 3.2 Network Structure -- 3.3 Network Training -- 4 Experiments -- 4.1 Data Preparation -- 4.2 Evaluation Metrics -- 4.3 Validation of the DVF Subspace -- 4.4 Pairwise 2D/3D Deformable Image Registration -- 5 Conclusion -- References -- XMorpher: Full Transformer for Deformable Medical Image Registration via Cross Attention -- 1 Introduction -- 2 Methodology -- 2.1 XMorpher for Efficient and Multi-level Semantic Feature Representation in Registration -- 2.2 Cross Attention Transformer Block for Corresponding Atention -- 2.3 Multi-size Window Partitions for Local-Wise Correspondence -- 3 Experiment -- 3.1 Experiment Protocol -- 3.2 Results and Analysis -- 4 Conclusion -- References -- Weakly-Supervised Biomechanically-Constrained CT/MRI Registration of the Spine -- 1 Introduction -- 2 Method -- 3 Experiments -- 4 Conclusion and Discussion -- References -- Collaborative Quantization Embeddings for Intra-subject Prostate MR Image Registration -- 1 Introduction -- 2 Method -- 2.1 Preliminary: Deep Vector Quantization -- 2.2 Model Overview -- 2.3 Vanilla Quantization -- 2.4 Hierarchical Quantization -- 2.5 Collaborative Quantization -- 2.6 Training -- 3 Experiment -- 3.1 Experimental Settings -- 3.2 Ablation Study -- 3.3 Comparison with Existing Methods -- 4 Conclusion -- References -- Mesh-Based 3D Motion Tracking in Cardiac MRI Using Deep Learning -- 1 Introduction -- 2 Method -- 2.1 Mesh Displacement Estimation -- 2.2 Mesh Prediction -- 2.3 Differentiable Mesh-to-Image Rasterizer -- 2.4 Optimization -- 3 Experiments and Results -- 4 Conclusion -- References."]
[{"$nulls":4},"Data-Driven Multi-modal Partial Medical Image Preregistration by Template Space Patch Mapping -- 1 Introduction -- 2 Method -- 2.1 Template-Space Patch Mapping (TSPM) -- 2.2 Pipeline Execution -- 3 Experiments -- 4 Conclusion -- References -- Global Multi-modal 2D/3D Registration via Local Descriptors Learning -- 1 Introduction -- 2 Approach -- 2.1 Challenges of Local Feature Extraction for Medical Images -- 2.2 Detector-Free Local Feature Networks -- 2.3 Multiple Frames -- 3 Experiments -- 3.1 Datasets -- 3.2 Baselines and Main Results -- 3.3 Ablation Studies -- 3.4 Similarity Visualization -- 4 Conclusions -- References -- Adapting the Mean Teacher for Keypoint-Based Lung Registration Under Geometric Domain Shifts -- 1 Introduction -- 2 Methods -- 2.1 Problem Statement -- 2.2 Baseline Model -- 2.3 Domain-Adaptive Registration with the Mean Teacher -- 3 Experiments -- 3.1 Experimental Setup -- 3.2 Results -- 4 Conclusion -- References -- DisQ: Disentangling Quantitative MRI Mapping of the Heart -- 1 Introduction -- 2 Methodology -- 2.1 Overall Framework: Disentangling Latent Spaces -- 2.2 Bootstrapping Disentangled Representations -- 3 Experiments -- 3.1 Dataset -- 3.2 Implementation -- 3.3 Results -- 4 Conclusion -- References -- Learning Iterative Optimisation for Deformable Image Registration of Lung CT with Recurrent Convolutional Networks -- 1 Introduction -- 1.1 Related Work -- 1.2 Adam Optimisation -- 1.3 Our Contribution -- "]
[{"$nulls":4},"2 Methods -- 2.1 Pre-registration -- 2.2 Extraction of Optimisation Inputs -- 2.3 Optimiser Network -- 2.4 Comparison to Feed-Forward Nets and Adam Optimisation -- 3 Experiments and Results -- 4 Discussion -- References -- Electron Microscope Image Registration Using Laplacian Sharpening Transformer U-Net -- 1 Introduction -- 2 Methods -- 2.1 Displacement Field Generation -- 2.2 Feature Enhancement."]
[{"$nulls":4},"2.3 Cascaded Registration."]
[null,null,null,"Sommario/riassunto","The eight-volume set LNCS 13431, 13432, 13433, 13434, 13435, 13436, 13437, and 13438 constitutes the refereed proceedings of the 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022, which was held in Singapore in September 2022. The 574 revised full papers presented were carefully reviewed and selected from 1831 submissions in a double-blind review process. The papers are organized in the following topical sections: Part I: Brain development and atlases; DWI and tractography; functional brain networks; neuroimaging; heart and lung imaging; dermatology; Part II: Computational (integrative) pathology; computational anatomy and physiology; ophthalmology; fetal imaging; Part III: Breast imaging; colonoscopy; computer aided diagnosis; Part IV: Microscopic image analysis; positron emission tomography; ultrasound imaging; video data analysis; image segmentation I; Part V: Image segmentation II; integration of imaging with non-imaging biomarkers; Part VI: Image registration; image reconstruction; Part VII: Image-Guided interventions and surgery; outcome and disease prediction; surgical data science; surgical planning and simulation; machine learning – domain adaptation and generalization; Part VIII: Machine learning – weakly-supervised learning; machine learning – model interpretation; machine learning – uncertainty; machine learning theory and methodologies. ."]