Medical Image Computing and Computer Assisted Intervention - MICCAI 2023: 26th International Conference, Vancouver, BC, Canada, October 8-12, 2023, Proceedings, Part VIII.
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| 1. Verfasser: | |
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| Format: | Elektronisch E-Book |
| Sprache: | English |
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Cham
Springer
2023
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| Ausgabe: | 1st ed |
| Schriftenreihe: | Lecture Notes in Computer Science Series
v.14227 |
| Schlagworte: | |
| Online-Zugang: | DE-2070s |
| Beschreibung: | Description based on publisher supplied metadata and other sources |
| Beschreibung: | 1 Online-Ressource (726 Seiten) |
| ISBN: | 9783031439933 |
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| 490 | 0 | |a Lecture Notes in Computer Science Series |v v.14227 | |
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| 505 | 8 | |a Intro -- Preface -- Organization -- Contents - Part VIII -- Clinical Applications - Neuroimaging -- CoactSeg: Learning from Heterogeneous Data for New Multiple Sclerosis Lesion Segmentation -- 1 Introduction -- 2 Datasets -- 3 Method -- 3.1 Overview -- 3.2 Multi-head Architecture -- 3.3 Longitudinal Relation Regularization -- 4 Results -- 5 Conclusion -- References -- Generating Realistic Brain MRIs via a Conditional Diffusion Probabilistic Model -- 1 Introduction -- 2 Methodology -- 2.1 Diffusion Probabilistic Model -- 2.2 Conditional Generation with DPM (cDPM) -- 2.3 Network Architecture -- 3 Experiments -- 3.1 Data -- 3.2 Implementation Details -- 3.3 Quantitative Comparison -- 3.4 Results -- 4 Conclusion -- References -- Towards Accurate Microstructure Estimation via 3D Hybrid Graph Transformer -- 1 Introduction -- 2 3D Hybrid Graph Transformer -- 2.1 Network Overview -- 2.2 Efficient q-Space Learning Module -- 2.3 3D x-Space Learning Module -- 3 Experiments -- 3.1 Implementation Details -- 3.2 Dataset and Evaluation Metrics -- 3.3 Experimental Results -- 3.4 Ablation Study -- 4 Conclusion -- References -- Cortical Analysis of Heterogeneous Clinical Brain MRI Scans for Large-Scale Neuroimaging Studies -- 1 Introduction -- 2 Methods -- 2.1 Learning of SDFs -- 2.2 Geometry Processing for Surface Placement -- 2.3 Implementation Details -- 3 Experiments and Results -- 3.1 Datasets -- 3.2 Competing Methods -- 3.3 Results on the ADNI Dataset -- 3.4 Results on the Clinical Dataset -- 3.5 Discussion and Conclusion -- References -- Flow-Based Geometric Interpolation of Fiber Orientation Distribution Functions -- 1 Introduction -- 2 Method -- 2.1 FOD Decomposition -- 2.2 Modeling Single Peak FOD Components as Flow of Vector Fields -- 2.3 Rotation Calculation for SPHARM-Based FODs -- 2.4 Evaluation Methods -- 3 Experiment Results -- 4 Conclusion | |
| 505 | 8 | |a References -- Learnable Subdivision Graph Neural Network for Functional Brain Network Analysis and Interpretable Cognitive Disorder Diagnosis -- 1 Introduction -- 2 Method -- 2.1 Preliminary -- 2.2 Functional Subdivision Block -- 2.3 Functional Aggregation Block -- 2.4 Objective Function -- 3 Experiments -- 3.1 Dataset and Experimental Settings -- 3.2 Result Analysis -- 3.3 Ablation Study -- 3.4 Interpretability of Brain States -- 4 Conclusion -- References -- FE-STGNN: Spatio-Temporal Graph Neural Network with Functional and Effective Connectivity Fusion for MCI Diagnosis -- 1 Introduction -- 2 Method -- 2.1 Local Spatial Structural Features and Short-Term Temporal Characteristics Extraction -- 2.2 Spatio-Temporal Fusion with Dynamic FC and EC -- 3 Experiments -- 3.1 Dataset and Experimental Settings -- 3.2 Ablation Studies -- 3.3 Comparison with Other Methods -- 4 Conclusion -- References -- Learning Normal Asymmetry Representations for Homologous Brain Structures -- 1 Introduction -- 2 Methods -- 2.1 Pre-training the Shape Characterization Encoder as a CAE -- 2.2 Learning Normal Asymmetries with a Siamese Network -- 3 Experimental Setup -- 4 Results and Discussion -- 4.1 Characterization of Normal and Disease Related Asymmetries -- 4.2 Comparison with Other Approaches -- 5 Conclusions -- References -- Simulation of Arbitrary Level Contrast Dose in MRI Using an Iterative Global Transformer Model -- 1 Introduction -- 2 Methods -- 3 Experiments and Results -- 4 Discussions and Conclusion -- References -- Development and Fast Transferring of General Connectivity-Based Diagnosis Model to New Brain Disorders with Adaptive Graph Meta-Learner -- 1 Introduction -- 2 Methods -- 2.1 Notation and Problem Formulation -- 2.2 Meta-Learner Training Algorithm -- 2.3 Multi-view Graph Classifier c -- 2.4 Meta-Controller m -- 3 Experiments -- 3.1 Dataset | |
| 505 | 8 | |a 3.2 Settings -- 3.3 Results and Discussions -- 4 Conclusion -- References -- Brain Anatomy-Guided MRI Analysis for Assessing Clinical Progression of Cognitive Impairment with Structural MRI -- 1 Introduction -- 2 Materials and Proposed Method -- 3 Experiment -- 4 Conclusion and Future Work -- References -- Dynamic Structural Brain Network Construction by Hierarchical Prototype Embedding GCN Using T1-MRI -- 1 Introduction -- 2 Methods -- 2.1 Backbone -- 2.2 Dynamic Hierarchical Prototype Learning -- 2.3 Brain Network Graph Construction and Classification -- 3 Experiments -- 3.1 Dataset -- 3.2 Implementation Details -- 4 Results -- 4.1 Comparing with SOTA Methods -- 4.2 Ablation Study -- 5 Conclusion -- References -- Microstructure Fingerprinting for Heterogeneously Oriented Tissue Microenvironments -- 1 Introduction -- 2 Methods -- 2.1 Fingerprint Dictionary -- 2.2 Solving the Bloch-Torrey Partial Differential Equation (BT-PDE) -- 2.3 Solving for Volume Fractions -- 2.4 Radius Bias Correction -- 3 Experiments -- 3.1 Volume Fraction -- 3.2 Cell Size and Membrane Permeability -- 3.3 In-vivo Data -- 3.4 Histological Corroboration -- 4 Conclusion -- References -- AUA-dE: An Adaptive Uncertainty Guided Attention for Diffusion MRI Models Estimation -- 1 Introduction -- 2 Method -- 2.1 q-t Space Sparsity -- 2.2 Adaptive Uncertainty Attention Modelling -- 2.3 Dataset and Training -- 3 Experiments and Results -- 3.1 Ablation Study -- 3.2 Performance Test -- 4 Conclusion -- References -- Relaxation-Diffusion Spectrum Imaging for Probing Tissue Microarchitecture -- 1 Introduction -- 2 Methods -- 2.1 Multi-compartment Model -- 2.2 Model Simplification via Spherical Mean -- 2.3 Estimation of Relaxation and Diffusion Parameters -- 2.4 Microstructure Indices -- 2.5 Data Acquisition and Processing -- 3 Results -- 3.1 Ex Vivo Data: Compartment-Specific Parameters | |
| 505 | 8 | |a 3.2 In Vivo Data: Compartment-Specific Parameters -- 3.3 In Vivo Data: Neurite Morphology -- 3.4 Relation Between Relaxation and Diffusivity -- 3.5 fODFs -- 4 Conclusion -- References -- Joint Representation of Functional and Structural Profiles for Identifying Common and Consistent 3-Hinge Gyral Folding Landmark -- 1 Introduction -- 2 Method -- 2.1 Dataset and Preprocessing -- 2.2 Joint Representation of Functional and Structural Profiles -- 2.3 Consistency Analysis from Anatomical, Structural and Functional Perspective -- 2.4 Comparative Analysis of Consistent 3-hinges for Structural Data and Multimodal Data -- 3 Result -- 3.1 Visualization of the Identified Consistent 3-hinges -- 3.2 Effectiveness of the Proposed Consistent 3-hinges -- 3.3 Comparative Analysis on the Consistent 3-hinges Based on Structural Data and Multimodal Data -- 4 Conclusion -- References -- Single-subject Multi-contrast MRI Super-resolution via Implicit Neural Representations -- 1 Introduction -- 2 Methods -- 3 Experiments and Results -- 4 Discussion and Conclusion -- References -- DeepSOZ: A Robust Deep Model for Joint Temporal and Spatial Seizure Onset Localization from Multichannel EEG Data -- 1 Introduction -- 2 Methodology -- 2.1 The DeepSOZ Model Architecture -- 2.2 Loss Function and Model Training -- 2.3 Model Validation -- 3 Experimental Results -- 4 Conclusion -- References -- Multimodal Brain Age Estimation Using Interpretable Adaptive Population-Graph Learning -- 1 Introduction -- 2 Methods -- 3 Experiments -- 3.1 Results -- 3.2 Ablation Studies -- 3.3 Interpretability -- 4 Conclusion -- References -- BrainUSL: Unsupervised Graph Structure Learning for Functional Brain Network Analysis -- 1 Introduction -- 2 Method -- 2.1 Graph Generation Module -- 2.2 Topology-Aware Encoder -- 2.3 Objective Functions -- 3 Experiments and Results | |
| 505 | 8 | |a 3.1 Dataset and Experimental Details -- 3.2 Classification Results -- 3.3 Functional Connectivity Analysis -- 3.4 Association of Brain Diseases -- 4 Conclusion -- References -- Learning Asynchronous Common and Individual Functional Brain Network for AD Diagnosis -- 1 Introduction -- 2 Proposed Method -- 2.1 Attention-Based Sparse Common-and-Individual FBN Construction Module (ASCFCM) -- 2.2 Cross Spatiotemporal Asynchronous FCs -- 3 Experiments -- 3.1 Data and Preprocessing -- 3.2 Experimental Settings -- 3.3 Experimental Results -- 3.4 Ablation Study -- 4 Visualization and Conclusion -- References -- Self-pruning Graph Neural Network for Predicting Inflammatory Disease Activity in Multiple Sclerosis from Brain MR Images -- 1 Introduction -- 2 Methodology -- 3 Experiments -- 4 Results -- 5 Conclusion -- References -- .28em plus .1em minus .1ematTRACTive: Semi-automatic White Matter Tract Segmentation Using Active Learning -- 1 Introduction -- 2 Methods -- 2.1 Binary Classification for Tract Segmentation -- 2.2 Active Learning for Tract Selection -- 3 Experiments -- 3.1 Data -- 3.2 Experimental Setup -- 3.3 Results -- 4 Discussion -- References -- Domain-Agnostic Segmentation of Thalamic Nuclei from Joint Structural and Diffusion MRI -- 1 Introduction -- 2 Methods -- 2.1 Training Dataset, Preprocessing, and Data Representation -- 2.2 Domain Randomisation and Data Augmentation -- 2.3 Loss -- 2.4 Architecture and Implementation Details -- 3 Experiments and Results -- 3.1 MRI Data -- 3.2 Competing Methods and Ablations -- 3.3 Results -- 4 Discussion and Conclusion -- References -- Neural Pre-processing: A Learning Framework for End-to-End Brain MRI Pre-processing -- 1 Introduction -- 2 Methods -- 2.1 Model -- 2.2 Loss Function -- 3 Experiments -- 3.1 Runtime Analyses -- 3.2 Pre-processing Performance -- 3.3 Ablation -- 4 Conclusion -- References | |
| 505 | 8 | |a Dynamic Functional Connectome Harmonics | |
| 650 | 4 | |a Diagnostic imaging-Data processing-Congresses | |
| 655 | 7 | |0 (DE-588)1071861417 |a Konferenzschrift |y 2023 |z Vancouver |2 gnd-content | |
| 700 | 1 | |a Madabhushi, Anant |e Sonstige |4 oth | |
| 700 | 1 | |a Mousavi, Parvin |e Sonstige |4 oth | |
| 700 | 1 | |a Salcudean, Septimiu |e Sonstige |4 oth | |
| 700 | 1 | |a Duncan, James |e Sonstige |4 oth | |
| 700 | 1 | |a Syeda-Mahmood, Tanveer |e Sonstige |4 oth | |
| 700 | 1 | |a Taylor, Russell |e Sonstige |4 oth | |
| 776 | 0 | 8 | |i Erscheint auch als |n Druck-Ausgabe |a Greenspan, Hayit |t Medical Image Computing and Computer Assisted Intervention - MICCAI 2023 |d Cham : Springer,c2023 |z 9783031439926 |
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Datensatz im Suchindex
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| adam_text | |
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| author | Greenspan, Hayit |
| author_facet | Greenspan, Hayit |
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| author_sort | Greenspan, Hayit |
| author_variant | h g hg |
| building | Verbundindex |
| bvnumber | BV050100674 |
| collection | ZDB-30-PQE |
| contents | Intro -- Preface -- Organization -- Contents - Part VIII -- Clinical Applications - Neuroimaging -- CoactSeg: Learning from Heterogeneous Data for New Multiple Sclerosis Lesion Segmentation -- 1 Introduction -- 2 Datasets -- 3 Method -- 3.1 Overview -- 3.2 Multi-head Architecture -- 3.3 Longitudinal Relation Regularization -- 4 Results -- 5 Conclusion -- References -- Generating Realistic Brain MRIs via a Conditional Diffusion Probabilistic Model -- 1 Introduction -- 2 Methodology -- 2.1 Diffusion Probabilistic Model -- 2.2 Conditional Generation with DPM (cDPM) -- 2.3 Network Architecture -- 3 Experiments -- 3.1 Data -- 3.2 Implementation Details -- 3.3 Quantitative Comparison -- 3.4 Results -- 4 Conclusion -- References -- Towards Accurate Microstructure Estimation via 3D Hybrid Graph Transformer -- 1 Introduction -- 2 3D Hybrid Graph Transformer -- 2.1 Network Overview -- 2.2 Efficient q-Space Learning Module -- 2.3 3D x-Space Learning Module -- 3 Experiments -- 3.1 Implementation Details -- 3.2 Dataset and Evaluation Metrics -- 3.3 Experimental Results -- 3.4 Ablation Study -- 4 Conclusion -- References -- Cortical Analysis of Heterogeneous Clinical Brain MRI Scans for Large-Scale Neuroimaging Studies -- 1 Introduction -- 2 Methods -- 2.1 Learning of SDFs -- 2.2 Geometry Processing for Surface Placement -- 2.3 Implementation Details -- 3 Experiments and Results -- 3.1 Datasets -- 3.2 Competing Methods -- 3.3 Results on the ADNI Dataset -- 3.4 Results on the Clinical Dataset -- 3.5 Discussion and Conclusion -- References -- Flow-Based Geometric Interpolation of Fiber Orientation Distribution Functions -- 1 Introduction -- 2 Method -- 2.1 FOD Decomposition -- 2.2 Modeling Single Peak FOD Components as Flow of Vector Fields -- 2.3 Rotation Calculation for SPHARM-Based FODs -- 2.4 Evaluation Methods -- 3 Experiment Results -- 4 Conclusion References -- Learnable Subdivision Graph Neural Network for Functional Brain Network Analysis and Interpretable Cognitive Disorder Diagnosis -- 1 Introduction -- 2 Method -- 2.1 Preliminary -- 2.2 Functional Subdivision Block -- 2.3 Functional Aggregation Block -- 2.4 Objective Function -- 3 Experiments -- 3.1 Dataset and Experimental Settings -- 3.2 Result Analysis -- 3.3 Ablation Study -- 3.4 Interpretability of Brain States -- 4 Conclusion -- References -- FE-STGNN: Spatio-Temporal Graph Neural Network with Functional and Effective Connectivity Fusion for MCI Diagnosis -- 1 Introduction -- 2 Method -- 2.1 Local Spatial Structural Features and Short-Term Temporal Characteristics Extraction -- 2.2 Spatio-Temporal Fusion with Dynamic FC and EC -- 3 Experiments -- 3.1 Dataset and Experimental Settings -- 3.2 Ablation Studies -- 3.3 Comparison with Other Methods -- 4 Conclusion -- References -- Learning Normal Asymmetry Representations for Homologous Brain Structures -- 1 Introduction -- 2 Methods -- 2.1 Pre-training the Shape Characterization Encoder as a CAE -- 2.2 Learning Normal Asymmetries with a Siamese Network -- 3 Experimental Setup -- 4 Results and Discussion -- 4.1 Characterization of Normal and Disease Related Asymmetries -- 4.2 Comparison with Other Approaches -- 5 Conclusions -- References -- Simulation of Arbitrary Level Contrast Dose in MRI Using an Iterative Global Transformer Model -- 1 Introduction -- 2 Methods -- 3 Experiments and Results -- 4 Discussions and Conclusion -- References -- Development and Fast Transferring of General Connectivity-Based Diagnosis Model to New Brain Disorders with Adaptive Graph Meta-Learner -- 1 Introduction -- 2 Methods -- 2.1 Notation and Problem Formulation -- 2.2 Meta-Learner Training Algorithm -- 2.3 Multi-view Graph Classifier c -- 2.4 Meta-Controller m -- 3 Experiments -- 3.1 Dataset 3.2 Settings -- 3.3 Results and Discussions -- 4 Conclusion -- References -- Brain Anatomy-Guided MRI Analysis for Assessing Clinical Progression of Cognitive Impairment with Structural MRI -- 1 Introduction -- 2 Materials and Proposed Method -- 3 Experiment -- 4 Conclusion and Future Work -- References -- Dynamic Structural Brain Network Construction by Hierarchical Prototype Embedding GCN Using T1-MRI -- 1 Introduction -- 2 Methods -- 2.1 Backbone -- 2.2 Dynamic Hierarchical Prototype Learning -- 2.3 Brain Network Graph Construction and Classification -- 3 Experiments -- 3.1 Dataset -- 3.2 Implementation Details -- 4 Results -- 4.1 Comparing with SOTA Methods -- 4.2 Ablation Study -- 5 Conclusion -- References -- Microstructure Fingerprinting for Heterogeneously Oriented Tissue Microenvironments -- 1 Introduction -- 2 Methods -- 2.1 Fingerprint Dictionary -- 2.2 Solving the Bloch-Torrey Partial Differential Equation (BT-PDE) -- 2.3 Solving for Volume Fractions -- 2.4 Radius Bias Correction -- 3 Experiments -- 3.1 Volume Fraction -- 3.2 Cell Size and Membrane Permeability -- 3.3 In-vivo Data -- 3.4 Histological Corroboration -- 4 Conclusion -- References -- AUA-dE: An Adaptive Uncertainty Guided Attention for Diffusion MRI Models Estimation -- 1 Introduction -- 2 Method -- 2.1 q-t Space Sparsity -- 2.2 Adaptive Uncertainty Attention Modelling -- 2.3 Dataset and Training -- 3 Experiments and Results -- 3.1 Ablation Study -- 3.2 Performance Test -- 4 Conclusion -- References -- Relaxation-Diffusion Spectrum Imaging for Probing Tissue Microarchitecture -- 1 Introduction -- 2 Methods -- 2.1 Multi-compartment Model -- 2.2 Model Simplification via Spherical Mean -- 2.3 Estimation of Relaxation and Diffusion Parameters -- 2.4 Microstructure Indices -- 2.5 Data Acquisition and Processing -- 3 Results -- 3.1 Ex Vivo Data: Compartment-Specific Parameters 3.2 In Vivo Data: Compartment-Specific Parameters -- 3.3 In Vivo Data: Neurite Morphology -- 3.4 Relation Between Relaxation and Diffusivity -- 3.5 fODFs -- 4 Conclusion -- References -- Joint Representation of Functional and Structural Profiles for Identifying Common and Consistent 3-Hinge Gyral Folding Landmark -- 1 Introduction -- 2 Method -- 2.1 Dataset and Preprocessing -- 2.2 Joint Representation of Functional and Structural Profiles -- 2.3 Consistency Analysis from Anatomical, Structural and Functional Perspective -- 2.4 Comparative Analysis of Consistent 3-hinges for Structural Data and Multimodal Data -- 3 Result -- 3.1 Visualization of the Identified Consistent 3-hinges -- 3.2 Effectiveness of the Proposed Consistent 3-hinges -- 3.3 Comparative Analysis on the Consistent 3-hinges Based on Structural Data and Multimodal Data -- 4 Conclusion -- References -- Single-subject Multi-contrast MRI Super-resolution via Implicit Neural Representations -- 1 Introduction -- 2 Methods -- 3 Experiments and Results -- 4 Discussion and Conclusion -- References -- DeepSOZ: A Robust Deep Model for Joint Temporal and Spatial Seizure Onset Localization from Multichannel EEG Data -- 1 Introduction -- 2 Methodology -- 2.1 The DeepSOZ Model Architecture -- 2.2 Loss Function and Model Training -- 2.3 Model Validation -- 3 Experimental Results -- 4 Conclusion -- References -- Multimodal Brain Age Estimation Using Interpretable Adaptive Population-Graph Learning -- 1 Introduction -- 2 Methods -- 3 Experiments -- 3.1 Results -- 3.2 Ablation Studies -- 3.3 Interpretability -- 4 Conclusion -- References -- BrainUSL: Unsupervised Graph Structure Learning for Functional Brain Network Analysis -- 1 Introduction -- 2 Method -- 2.1 Graph Generation Module -- 2.2 Topology-Aware Encoder -- 2.3 Objective Functions -- 3 Experiments and Results 3.1 Dataset and Experimental Details -- 3.2 Classification Results -- 3.3 Functional Connectivity Analysis -- 3.4 Association of Brain Diseases -- 4 Conclusion -- References -- Learning Asynchronous Common and Individual Functional Brain Network for AD Diagnosis -- 1 Introduction -- 2 Proposed Method -- 2.1 Attention-Based Sparse Common-and-Individual FBN Construction Module (ASCFCM) -- 2.2 Cross Spatiotemporal Asynchronous FCs -- 3 Experiments -- 3.1 Data and Preprocessing -- 3.2 Experimental Settings -- 3.3 Experimental Results -- 3.4 Ablation Study -- 4 Visualization and Conclusion -- References -- Self-pruning Graph Neural Network for Predicting Inflammatory Disease Activity in Multiple Sclerosis from Brain MR Images -- 1 Introduction -- 2 Methodology -- 3 Experiments -- 4 Results -- 5 Conclusion -- References -- .28em plus .1em minus .1ematTRACTive: Semi-automatic White Matter Tract Segmentation Using Active Learning -- 1 Introduction -- 2 Methods -- 2.1 Binary Classification for Tract Segmentation -- 2.2 Active Learning for Tract Selection -- 3 Experiments -- 3.1 Data -- 3.2 Experimental Setup -- 3.3 Results -- 4 Discussion -- References -- Domain-Agnostic Segmentation of Thalamic Nuclei from Joint Structural and Diffusion MRI -- 1 Introduction -- 2 Methods -- 2.1 Training Dataset, Preprocessing, and Data Representation -- 2.2 Domain Randomisation and Data Augmentation -- 2.3 Loss -- 2.4 Architecture and Implementation Details -- 3 Experiments and Results -- 3.1 MRI Data -- 3.2 Competing Methods and Ablations -- 3.3 Results -- 4 Discussion and Conclusion -- References -- Neural Pre-processing: A Learning Framework for End-to-End Brain MRI Pre-processing -- 1 Introduction -- 2 Methods -- 2.1 Model -- 2.2 Loss Function -- 3 Experiments -- 3.1 Runtime Analyses -- 3.2 Pre-processing Performance -- 3.3 Ablation -- 4 Conclusion -- References Dynamic Functional Connectome Harmonics |
| ctrlnum | (ZDB-30-PQE)EBC30765547 (ZDB-30-PAD)EBC30765547 (ZDB-89-EBL)EBL30765547 (OCoLC)1401635254 (DE-599)BVBBV050100674 |
| dewey-full | 381 |
| dewey-hundreds | 300 - Social sciences |
| dewey-ones | 381 - Commerce (Trade) |
| dewey-raw | 381 |
| dewey-search | 381 |
| dewey-sort | 3381 |
| dewey-tens | 380 - Commerce, communications, transportation |
| discipline | Wirtschaftswissenschaften |
| edition | 1st ed |
| format | Electronic eBook |
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-- 2.2 Efficient q-Space Learning Module -- 2.3 3D x-Space Learning Module -- 3 Experiments -- 3.1 Implementation Details -- 3.2 Dataset and Evaluation Metrics -- 3.3 Experimental Results -- 3.4 Ablation Study -- 4 Conclusion -- References -- Cortical Analysis of Heterogeneous Clinical Brain MRI Scans for Large-Scale Neuroimaging Studies -- 1 Introduction -- 2 Methods -- 2.1 Learning of SDFs -- 2.2 Geometry Processing for Surface Placement -- 2.3 Implementation Details -- 3 Experiments and Results -- 3.1 Datasets -- 3.2 Competing Methods -- 3.3 Results on the ADNI Dataset -- 3.4 Results on the Clinical Dataset -- 3.5 Discussion and Conclusion -- References -- Flow-Based Geometric Interpolation of Fiber Orientation Distribution Functions -- 1 Introduction -- 2 Method -- 2.1 FOD Decomposition -- 2.2 Modeling Single Peak FOD Components as Flow of Vector Fields -- 2.3 Rotation Calculation for SPHARM-Based FODs -- 2.4 Evaluation Methods -- 3 Experiment Results -- 4 Conclusion</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">References -- Learnable Subdivision Graph Neural Network for Functional Brain Network Analysis and Interpretable Cognitive Disorder Diagnosis -- 1 Introduction -- 2 Method -- 2.1 Preliminary -- 2.2 Functional Subdivision Block -- 2.3 Functional Aggregation Block -- 2.4 Objective Function -- 3 Experiments -- 3.1 Dataset and Experimental Settings -- 3.2 Result Analysis -- 3.3 Ablation Study -- 3.4 Interpretability of Brain States -- 4 Conclusion -- References -- FE-STGNN: Spatio-Temporal Graph Neural Network with Functional and Effective Connectivity Fusion for MCI Diagnosis -- 1 Introduction -- 2 Method -- 2.1 Local Spatial Structural Features and Short-Term Temporal Characteristics Extraction -- 2.2 Spatio-Temporal Fusion with Dynamic FC and EC -- 3 Experiments -- 3.1 Dataset and Experimental Settings -- 3.2 Ablation Studies -- 3.3 Comparison with Other Methods -- 4 Conclusion -- References -- Learning Normal Asymmetry Representations for Homologous Brain Structures -- 1 Introduction -- 2 Methods -- 2.1 Pre-training the Shape Characterization Encoder as a CAE -- 2.2 Learning Normal Asymmetries with a Siamese Network -- 3 Experimental Setup -- 4 Results and Discussion -- 4.1 Characterization of Normal and Disease Related Asymmetries -- 4.2 Comparison with Other Approaches -- 5 Conclusions -- References -- Simulation of Arbitrary Level Contrast Dose in MRI Using an Iterative Global Transformer Model -- 1 Introduction -- 2 Methods -- 3 Experiments and Results -- 4 Discussions and Conclusion -- References -- Development and Fast Transferring of General Connectivity-Based Diagnosis Model to New Brain Disorders with Adaptive Graph Meta-Learner -- 1 Introduction -- 2 Methods -- 2.1 Notation and Problem Formulation -- 2.2 Meta-Learner Training Algorithm -- 2.3 Multi-view Graph Classifier c -- 2.4 Meta-Controller m -- 3 Experiments -- 3.1 Dataset</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">3.2 Settings -- 3.3 Results and Discussions -- 4 Conclusion -- References -- Brain Anatomy-Guided MRI Analysis for Assessing Clinical Progression of Cognitive Impairment with Structural MRI -- 1 Introduction -- 2 Materials and Proposed Method -- 3 Experiment -- 4 Conclusion and Future Work -- References -- Dynamic Structural Brain Network Construction by Hierarchical Prototype Embedding GCN Using T1-MRI -- 1 Introduction -- 2 Methods -- 2.1 Backbone -- 2.2 Dynamic Hierarchical Prototype Learning -- 2.3 Brain Network Graph Construction and Classification -- 3 Experiments -- 3.1 Dataset -- 3.2 Implementation Details -- 4 Results -- 4.1 Comparing with SOTA Methods -- 4.2 Ablation Study -- 5 Conclusion -- References -- Microstructure Fingerprinting for Heterogeneously Oriented Tissue Microenvironments -- 1 Introduction -- 2 Methods -- 2.1 Fingerprint Dictionary -- 2.2 Solving the Bloch-Torrey Partial Differential Equation (BT-PDE) -- 2.3 Solving for Volume Fractions -- 2.4 Radius Bias Correction -- 3 Experiments -- 3.1 Volume Fraction -- 3.2 Cell Size and Membrane Permeability -- 3.3 In-vivo Data -- 3.4 Histological Corroboration -- 4 Conclusion -- References -- AUA-dE: An Adaptive Uncertainty Guided Attention for Diffusion MRI Models Estimation -- 1 Introduction -- 2 Method -- 2.1 q-t Space Sparsity -- 2.2 Adaptive Uncertainty Attention Modelling -- 2.3 Dataset and Training -- 3 Experiments and Results -- 3.1 Ablation Study -- 3.2 Performance Test -- 4 Conclusion -- References -- Relaxation-Diffusion Spectrum Imaging for Probing Tissue Microarchitecture -- 1 Introduction -- 2 Methods -- 2.1 Multi-compartment Model -- 2.2 Model Simplification via Spherical Mean -- 2.3 Estimation of Relaxation and Diffusion Parameters -- 2.4 Microstructure Indices -- 2.5 Data Acquisition and Processing -- 3 Results -- 3.1 Ex Vivo Data: Compartment-Specific Parameters</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">3.2 In Vivo Data: Compartment-Specific Parameters -- 3.3 In Vivo Data: Neurite Morphology -- 3.4 Relation Between Relaxation and Diffusivity -- 3.5 fODFs -- 4 Conclusion -- References -- Joint Representation of Functional and Structural Profiles for Identifying Common and Consistent 3-Hinge Gyral Folding Landmark -- 1 Introduction -- 2 Method -- 2.1 Dataset and Preprocessing -- 2.2 Joint Representation of Functional and Structural Profiles -- 2.3 Consistency Analysis from Anatomical, Structural and Functional Perspective -- 2.4 Comparative Analysis of Consistent 3-hinges for Structural Data and Multimodal Data -- 3 Result -- 3.1 Visualization of the Identified Consistent 3-hinges -- 3.2 Effectiveness of the Proposed Consistent 3-hinges -- 3.3 Comparative Analysis on the Consistent 3-hinges Based on Structural Data and Multimodal Data -- 4 Conclusion -- References -- Single-subject Multi-contrast MRI Super-resolution via Implicit Neural Representations -- 1 Introduction -- 2 Methods -- 3 Experiments and Results -- 4 Discussion and Conclusion -- References -- DeepSOZ: A Robust Deep Model for Joint Temporal and Spatial Seizure Onset Localization from Multichannel EEG Data -- 1 Introduction -- 2 Methodology -- 2.1 The DeepSOZ Model Architecture -- 2.2 Loss Function and Model Training -- 2.3 Model Validation -- 3 Experimental Results -- 4 Conclusion -- References -- Multimodal Brain Age Estimation Using Interpretable Adaptive Population-Graph Learning -- 1 Introduction -- 2 Methods -- 3 Experiments -- 3.1 Results -- 3.2 Ablation Studies -- 3.3 Interpretability -- 4 Conclusion -- References -- BrainUSL: Unsupervised Graph Structure Learning for Functional Brain Network Analysis -- 1 Introduction -- 2 Method -- 2.1 Graph Generation Module -- 2.2 Topology-Aware Encoder -- 2.3 Objective Functions -- 3 Experiments and Results</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">3.1 Dataset and Experimental Details -- 3.2 Classification 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| genre | (DE-588)1071861417 Konferenzschrift 2023 Vancouver gnd-content |
| genre_facet | Konferenzschrift 2023 Vancouver |
| id | DE-604.BV050100674 |
| illustrated | Not Illustrated |
| indexdate | 2025-01-10T19:04:28Z |
| institution | BVB |
| isbn | 9783031439933 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-035437836 |
| oclc_num | 1401635254 |
| open_access_boolean | |
| owner | DE-2070s |
| owner_facet | DE-2070s |
| physical | 1 Online-Ressource (726 Seiten) |
| psigel | ZDB-30-PQE ZDB-30-PQE HWR_PDA_PQE |
| publishDate | 2023 |
| publishDateSearch | 2023 |
| publishDateSort | 2023 |
| publisher | Springer |
| record_format | marc |
| series2 | Lecture Notes in Computer Science Series |
| spelling | Greenspan, Hayit Verfasser aut Medical Image Computing and Computer Assisted Intervention - MICCAI 2023 26th International Conference, Vancouver, BC, Canada, October 8-12, 2023, Proceedings, Part VIII. 1st ed Cham Springer 2023 ©2023 1 Online-Ressource (726 Seiten) txt rdacontent c rdamedia cr rdacarrier Lecture Notes in Computer Science Series v.14227 Description based on publisher supplied metadata and other sources Intro -- Preface -- Organization -- Contents - Part VIII -- Clinical Applications - Neuroimaging -- CoactSeg: Learning from Heterogeneous Data for New Multiple Sclerosis Lesion Segmentation -- 1 Introduction -- 2 Datasets -- 3 Method -- 3.1 Overview -- 3.2 Multi-head Architecture -- 3.3 Longitudinal Relation Regularization -- 4 Results -- 5 Conclusion -- References -- Generating Realistic Brain MRIs via a Conditional Diffusion Probabilistic Model -- 1 Introduction -- 2 Methodology -- 2.1 Diffusion Probabilistic Model -- 2.2 Conditional Generation with DPM (cDPM) -- 2.3 Network Architecture -- 3 Experiments -- 3.1 Data -- 3.2 Implementation Details -- 3.3 Quantitative Comparison -- 3.4 Results -- 4 Conclusion -- References -- Towards Accurate Microstructure Estimation via 3D Hybrid Graph Transformer -- 1 Introduction -- 2 3D Hybrid Graph Transformer -- 2.1 Network Overview -- 2.2 Efficient q-Space Learning Module -- 2.3 3D x-Space Learning Module -- 3 Experiments -- 3.1 Implementation Details -- 3.2 Dataset and Evaluation Metrics -- 3.3 Experimental Results -- 3.4 Ablation Study -- 4 Conclusion -- References -- Cortical Analysis of Heterogeneous Clinical Brain MRI Scans for Large-Scale Neuroimaging Studies -- 1 Introduction -- 2 Methods -- 2.1 Learning of SDFs -- 2.2 Geometry Processing for Surface Placement -- 2.3 Implementation Details -- 3 Experiments and Results -- 3.1 Datasets -- 3.2 Competing Methods -- 3.3 Results on the ADNI Dataset -- 3.4 Results on the Clinical Dataset -- 3.5 Discussion and Conclusion -- References -- Flow-Based Geometric Interpolation of Fiber Orientation Distribution Functions -- 1 Introduction -- 2 Method -- 2.1 FOD Decomposition -- 2.2 Modeling Single Peak FOD Components as Flow of Vector Fields -- 2.3 Rotation Calculation for SPHARM-Based FODs -- 2.4 Evaluation Methods -- 3 Experiment Results -- 4 Conclusion References -- Learnable Subdivision Graph Neural Network for Functional Brain Network Analysis and Interpretable Cognitive Disorder Diagnosis -- 1 Introduction -- 2 Method -- 2.1 Preliminary -- 2.2 Functional Subdivision Block -- 2.3 Functional Aggregation Block -- 2.4 Objective Function -- 3 Experiments -- 3.1 Dataset and Experimental Settings -- 3.2 Result Analysis -- 3.3 Ablation Study -- 3.4 Interpretability of Brain States -- 4 Conclusion -- References -- FE-STGNN: Spatio-Temporal Graph Neural Network with Functional and Effective Connectivity Fusion for MCI Diagnosis -- 1 Introduction -- 2 Method -- 2.1 Local Spatial Structural Features and Short-Term Temporal Characteristics Extraction -- 2.2 Spatio-Temporal Fusion with Dynamic FC and EC -- 3 Experiments -- 3.1 Dataset and Experimental Settings -- 3.2 Ablation Studies -- 3.3 Comparison with Other Methods -- 4 Conclusion -- References -- Learning Normal Asymmetry Representations for Homologous Brain Structures -- 1 Introduction -- 2 Methods -- 2.1 Pre-training the Shape Characterization Encoder as a CAE -- 2.2 Learning Normal Asymmetries with a Siamese Network -- 3 Experimental Setup -- 4 Results and Discussion -- 4.1 Characterization of Normal and Disease Related Asymmetries -- 4.2 Comparison with Other Approaches -- 5 Conclusions -- References -- Simulation of Arbitrary Level Contrast Dose in MRI Using an Iterative Global Transformer Model -- 1 Introduction -- 2 Methods -- 3 Experiments and Results -- 4 Discussions and Conclusion -- References -- Development and Fast Transferring of General Connectivity-Based Diagnosis Model to New Brain Disorders with Adaptive Graph Meta-Learner -- 1 Introduction -- 2 Methods -- 2.1 Notation and Problem Formulation -- 2.2 Meta-Learner Training Algorithm -- 2.3 Multi-view Graph Classifier c -- 2.4 Meta-Controller m -- 3 Experiments -- 3.1 Dataset 3.2 Settings -- 3.3 Results and Discussions -- 4 Conclusion -- References -- Brain Anatomy-Guided MRI Analysis for Assessing Clinical Progression of Cognitive Impairment with Structural MRI -- 1 Introduction -- 2 Materials and Proposed Method -- 3 Experiment -- 4 Conclusion and Future Work -- References -- Dynamic Structural Brain Network Construction by Hierarchical Prototype Embedding GCN Using T1-MRI -- 1 Introduction -- 2 Methods -- 2.1 Backbone -- 2.2 Dynamic Hierarchical Prototype Learning -- 2.3 Brain Network Graph Construction and Classification -- 3 Experiments -- 3.1 Dataset -- 3.2 Implementation Details -- 4 Results -- 4.1 Comparing with SOTA Methods -- 4.2 Ablation Study -- 5 Conclusion -- References -- Microstructure Fingerprinting for Heterogeneously Oriented Tissue Microenvironments -- 1 Introduction -- 2 Methods -- 2.1 Fingerprint Dictionary -- 2.2 Solving the Bloch-Torrey Partial Differential Equation (BT-PDE) -- 2.3 Solving for Volume Fractions -- 2.4 Radius Bias Correction -- 3 Experiments -- 3.1 Volume Fraction -- 3.2 Cell Size and Membrane Permeability -- 3.3 In-vivo Data -- 3.4 Histological Corroboration -- 4 Conclusion -- References -- AUA-dE: An Adaptive Uncertainty Guided Attention for Diffusion MRI Models Estimation -- 1 Introduction -- 2 Method -- 2.1 q-t Space Sparsity -- 2.2 Adaptive Uncertainty Attention Modelling -- 2.3 Dataset and Training -- 3 Experiments and Results -- 3.1 Ablation Study -- 3.2 Performance Test -- 4 Conclusion -- References -- Relaxation-Diffusion Spectrum Imaging for Probing Tissue Microarchitecture -- 1 Introduction -- 2 Methods -- 2.1 Multi-compartment Model -- 2.2 Model Simplification via Spherical Mean -- 2.3 Estimation of Relaxation and Diffusion Parameters -- 2.4 Microstructure Indices -- 2.5 Data Acquisition and Processing -- 3 Results -- 3.1 Ex Vivo Data: Compartment-Specific Parameters 3.2 In Vivo Data: Compartment-Specific Parameters -- 3.3 In Vivo Data: Neurite Morphology -- 3.4 Relation Between Relaxation and Diffusivity -- 3.5 fODFs -- 4 Conclusion -- References -- Joint Representation of Functional and Structural Profiles for Identifying Common and Consistent 3-Hinge Gyral Folding Landmark -- 1 Introduction -- 2 Method -- 2.1 Dataset and Preprocessing -- 2.2 Joint Representation of Functional and Structural Profiles -- 2.3 Consistency Analysis from Anatomical, Structural and Functional Perspective -- 2.4 Comparative Analysis of Consistent 3-hinges for Structural Data and Multimodal Data -- 3 Result -- 3.1 Visualization of the Identified Consistent 3-hinges -- 3.2 Effectiveness of the Proposed Consistent 3-hinges -- 3.3 Comparative Analysis on the Consistent 3-hinges Based on Structural Data and Multimodal Data -- 4 Conclusion -- References -- Single-subject Multi-contrast MRI Super-resolution via Implicit Neural Representations -- 1 Introduction -- 2 Methods -- 3 Experiments and Results -- 4 Discussion and Conclusion -- References -- DeepSOZ: A Robust Deep Model for Joint Temporal and Spatial Seizure Onset Localization from Multichannel EEG Data -- 1 Introduction -- 2 Methodology -- 2.1 The DeepSOZ Model Architecture -- 2.2 Loss Function and Model Training -- 2.3 Model Validation -- 3 Experimental Results -- 4 Conclusion -- References -- Multimodal Brain Age Estimation Using Interpretable Adaptive Population-Graph Learning -- 1 Introduction -- 2 Methods -- 3 Experiments -- 3.1 Results -- 3.2 Ablation Studies -- 3.3 Interpretability -- 4 Conclusion -- References -- BrainUSL: Unsupervised Graph Structure Learning for Functional Brain Network Analysis -- 1 Introduction -- 2 Method -- 2.1 Graph Generation Module -- 2.2 Topology-Aware Encoder -- 2.3 Objective Functions -- 3 Experiments and Results 3.1 Dataset and Experimental Details -- 3.2 Classification Results -- 3.3 Functional Connectivity Analysis -- 3.4 Association of Brain Diseases -- 4 Conclusion -- References -- Learning Asynchronous Common and Individual Functional Brain Network for AD Diagnosis -- 1 Introduction -- 2 Proposed Method -- 2.1 Attention-Based Sparse Common-and-Individual FBN Construction Module (ASCFCM) -- 2.2 Cross Spatiotemporal Asynchronous FCs -- 3 Experiments -- 3.1 Data and Preprocessing -- 3.2 Experimental Settings -- 3.3 Experimental Results -- 3.4 Ablation Study -- 4 Visualization and Conclusion -- References -- Self-pruning Graph Neural Network for Predicting Inflammatory Disease Activity in Multiple Sclerosis from Brain MR Images -- 1 Introduction -- 2 Methodology -- 3 Experiments -- 4 Results -- 5 Conclusion -- References -- .28em plus .1em minus .1ematTRACTive: Semi-automatic White Matter Tract Segmentation Using Active Learning -- 1 Introduction -- 2 Methods -- 2.1 Binary Classification for Tract Segmentation -- 2.2 Active Learning for Tract Selection -- 3 Experiments -- 3.1 Data -- 3.2 Experimental Setup -- 3.3 Results -- 4 Discussion -- References -- Domain-Agnostic Segmentation of Thalamic Nuclei from Joint Structural and Diffusion MRI -- 1 Introduction -- 2 Methods -- 2.1 Training Dataset, Preprocessing, and Data Representation -- 2.2 Domain Randomisation and Data Augmentation -- 2.3 Loss -- 2.4 Architecture and Implementation Details -- 3 Experiments and Results -- 3.1 MRI Data -- 3.2 Competing Methods and Ablations -- 3.3 Results -- 4 Discussion and Conclusion -- References -- Neural Pre-processing: A Learning Framework for End-to-End Brain MRI Pre-processing -- 1 Introduction -- 2 Methods -- 2.1 Model -- 2.2 Loss Function -- 3 Experiments -- 3.1 Runtime Analyses -- 3.2 Pre-processing Performance -- 3.3 Ablation -- 4 Conclusion -- References Dynamic Functional Connectome Harmonics Diagnostic imaging-Data processing-Congresses (DE-588)1071861417 Konferenzschrift 2023 Vancouver gnd-content Madabhushi, Anant Sonstige oth Mousavi, Parvin Sonstige oth Salcudean, Septimiu Sonstige oth Duncan, James Sonstige oth Syeda-Mahmood, Tanveer Sonstige oth Taylor, Russell Sonstige oth Erscheint auch als Druck-Ausgabe Greenspan, Hayit Medical Image Computing and Computer Assisted Intervention - MICCAI 2023 Cham : Springer,c2023 9783031439926 |
| spellingShingle | Greenspan, Hayit Medical Image Computing and Computer Assisted Intervention - MICCAI 2023 26th International Conference, Vancouver, BC, Canada, October 8-12, 2023, Proceedings, Part VIII. Intro -- Preface -- Organization -- Contents - Part VIII -- Clinical Applications - Neuroimaging -- CoactSeg: Learning from Heterogeneous Data for New Multiple Sclerosis Lesion Segmentation -- 1 Introduction -- 2 Datasets -- 3 Method -- 3.1 Overview -- 3.2 Multi-head Architecture -- 3.3 Longitudinal Relation Regularization -- 4 Results -- 5 Conclusion -- References -- Generating Realistic Brain MRIs via a Conditional Diffusion Probabilistic Model -- 1 Introduction -- 2 Methodology -- 2.1 Diffusion Probabilistic Model -- 2.2 Conditional Generation with DPM (cDPM) -- 2.3 Network Architecture -- 3 Experiments -- 3.1 Data -- 3.2 Implementation Details -- 3.3 Quantitative Comparison -- 3.4 Results -- 4 Conclusion -- References -- Towards Accurate Microstructure Estimation via 3D Hybrid Graph Transformer -- 1 Introduction -- 2 3D Hybrid Graph Transformer -- 2.1 Network Overview -- 2.2 Efficient q-Space Learning Module -- 2.3 3D x-Space Learning Module -- 3 Experiments -- 3.1 Implementation Details -- 3.2 Dataset and Evaluation Metrics -- 3.3 Experimental Results -- 3.4 Ablation Study -- 4 Conclusion -- References -- Cortical Analysis of Heterogeneous Clinical Brain MRI Scans for Large-Scale Neuroimaging Studies -- 1 Introduction -- 2 Methods -- 2.1 Learning of SDFs -- 2.2 Geometry Processing for Surface Placement -- 2.3 Implementation Details -- 3 Experiments and Results -- 3.1 Datasets -- 3.2 Competing Methods -- 3.3 Results on the ADNI Dataset -- 3.4 Results on the Clinical Dataset -- 3.5 Discussion and Conclusion -- References -- Flow-Based Geometric Interpolation of Fiber Orientation Distribution Functions -- 1 Introduction -- 2 Method -- 2.1 FOD Decomposition -- 2.2 Modeling Single Peak FOD Components as Flow of Vector Fields -- 2.3 Rotation Calculation for SPHARM-Based FODs -- 2.4 Evaluation Methods -- 3 Experiment Results -- 4 Conclusion References -- Learnable Subdivision Graph Neural Network for Functional Brain Network Analysis and Interpretable Cognitive Disorder Diagnosis -- 1 Introduction -- 2 Method -- 2.1 Preliminary -- 2.2 Functional Subdivision Block -- 2.3 Functional Aggregation Block -- 2.4 Objective Function -- 3 Experiments -- 3.1 Dataset and Experimental Settings -- 3.2 Result Analysis -- 3.3 Ablation Study -- 3.4 Interpretability of Brain States -- 4 Conclusion -- References -- FE-STGNN: Spatio-Temporal Graph Neural Network with Functional and Effective Connectivity Fusion for MCI Diagnosis -- 1 Introduction -- 2 Method -- 2.1 Local Spatial Structural Features and Short-Term Temporal Characteristics Extraction -- 2.2 Spatio-Temporal Fusion with Dynamic FC and EC -- 3 Experiments -- 3.1 Dataset and Experimental Settings -- 3.2 Ablation Studies -- 3.3 Comparison with Other Methods -- 4 Conclusion -- References -- Learning Normal Asymmetry Representations for Homologous Brain Structures -- 1 Introduction -- 2 Methods -- 2.1 Pre-training the Shape Characterization Encoder as a CAE -- 2.2 Learning Normal Asymmetries with a Siamese Network -- 3 Experimental Setup -- 4 Results and Discussion -- 4.1 Characterization of Normal and Disease Related Asymmetries -- 4.2 Comparison with Other Approaches -- 5 Conclusions -- References -- Simulation of Arbitrary Level Contrast Dose in MRI Using an Iterative Global Transformer Model -- 1 Introduction -- 2 Methods -- 3 Experiments and Results -- 4 Discussions and Conclusion -- References -- Development and Fast Transferring of General Connectivity-Based Diagnosis Model to New Brain Disorders with Adaptive Graph Meta-Learner -- 1 Introduction -- 2 Methods -- 2.1 Notation and Problem Formulation -- 2.2 Meta-Learner Training Algorithm -- 2.3 Multi-view Graph Classifier c -- 2.4 Meta-Controller m -- 3 Experiments -- 3.1 Dataset 3.2 Settings -- 3.3 Results and Discussions -- 4 Conclusion -- References -- Brain Anatomy-Guided MRI Analysis for Assessing Clinical Progression of Cognitive Impairment with Structural MRI -- 1 Introduction -- 2 Materials and Proposed Method -- 3 Experiment -- 4 Conclusion and Future Work -- References -- Dynamic Structural Brain Network Construction by Hierarchical Prototype Embedding GCN Using T1-MRI -- 1 Introduction -- 2 Methods -- 2.1 Backbone -- 2.2 Dynamic Hierarchical Prototype Learning -- 2.3 Brain Network Graph Construction and Classification -- 3 Experiments -- 3.1 Dataset -- 3.2 Implementation Details -- 4 Results -- 4.1 Comparing with SOTA Methods -- 4.2 Ablation Study -- 5 Conclusion -- References -- Microstructure Fingerprinting for Heterogeneously Oriented Tissue Microenvironments -- 1 Introduction -- 2 Methods -- 2.1 Fingerprint Dictionary -- 2.2 Solving the Bloch-Torrey Partial Differential Equation (BT-PDE) -- 2.3 Solving for Volume Fractions -- 2.4 Radius Bias Correction -- 3 Experiments -- 3.1 Volume Fraction -- 3.2 Cell Size and Membrane Permeability -- 3.3 In-vivo Data -- 3.4 Histological Corroboration -- 4 Conclusion -- References -- AUA-dE: An Adaptive Uncertainty Guided Attention for Diffusion MRI Models Estimation -- 1 Introduction -- 2 Method -- 2.1 q-t Space Sparsity -- 2.2 Adaptive Uncertainty Attention Modelling -- 2.3 Dataset and Training -- 3 Experiments and Results -- 3.1 Ablation Study -- 3.2 Performance Test -- 4 Conclusion -- References -- Relaxation-Diffusion Spectrum Imaging for Probing Tissue Microarchitecture -- 1 Introduction -- 2 Methods -- 2.1 Multi-compartment Model -- 2.2 Model Simplification via Spherical Mean -- 2.3 Estimation of Relaxation and Diffusion Parameters -- 2.4 Microstructure Indices -- 2.5 Data Acquisition and Processing -- 3 Results -- 3.1 Ex Vivo Data: Compartment-Specific Parameters 3.2 In Vivo Data: Compartment-Specific Parameters -- 3.3 In Vivo Data: Neurite Morphology -- 3.4 Relation Between Relaxation and Diffusivity -- 3.5 fODFs -- 4 Conclusion -- References -- Joint Representation of Functional and Structural Profiles for Identifying Common and Consistent 3-Hinge Gyral Folding Landmark -- 1 Introduction -- 2 Method -- 2.1 Dataset and Preprocessing -- 2.2 Joint Representation of Functional and Structural Profiles -- 2.3 Consistency Analysis from Anatomical, Structural and Functional Perspective -- 2.4 Comparative Analysis of Consistent 3-hinges for Structural Data and Multimodal Data -- 3 Result -- 3.1 Visualization of the Identified Consistent 3-hinges -- 3.2 Effectiveness of the Proposed Consistent 3-hinges -- 3.3 Comparative Analysis on the Consistent 3-hinges Based on Structural Data and Multimodal Data -- 4 Conclusion -- References -- Single-subject Multi-contrast MRI Super-resolution via Implicit Neural Representations -- 1 Introduction -- 2 Methods -- 3 Experiments and Results -- 4 Discussion and Conclusion -- References -- DeepSOZ: A Robust Deep Model for Joint Temporal and Spatial Seizure Onset Localization from Multichannel EEG Data -- 1 Introduction -- 2 Methodology -- 2.1 The DeepSOZ Model Architecture -- 2.2 Loss Function and Model Training -- 2.3 Model Validation -- 3 Experimental Results -- 4 Conclusion -- References -- Multimodal Brain Age Estimation Using Interpretable Adaptive Population-Graph Learning -- 1 Introduction -- 2 Methods -- 3 Experiments -- 3.1 Results -- 3.2 Ablation Studies -- 3.3 Interpretability -- 4 Conclusion -- References -- BrainUSL: Unsupervised Graph Structure Learning for Functional Brain Network Analysis -- 1 Introduction -- 2 Method -- 2.1 Graph Generation Module -- 2.2 Topology-Aware Encoder -- 2.3 Objective Functions -- 3 Experiments and Results 3.1 Dataset and Experimental Details -- 3.2 Classification Results -- 3.3 Functional Connectivity Analysis -- 3.4 Association of Brain Diseases -- 4 Conclusion -- References -- Learning Asynchronous Common and Individual Functional Brain Network for AD Diagnosis -- 1 Introduction -- 2 Proposed Method -- 2.1 Attention-Based Sparse Common-and-Individual FBN Construction Module (ASCFCM) -- 2.2 Cross Spatiotemporal Asynchronous FCs -- 3 Experiments -- 3.1 Data and Preprocessing -- 3.2 Experimental Settings -- 3.3 Experimental Results -- 3.4 Ablation Study -- 4 Visualization and Conclusion -- References -- Self-pruning Graph Neural Network for Predicting Inflammatory Disease Activity in Multiple Sclerosis from Brain MR Images -- 1 Introduction -- 2 Methodology -- 3 Experiments -- 4 Results -- 5 Conclusion -- References -- .28em plus .1em minus .1ematTRACTive: Semi-automatic White Matter Tract Segmentation Using Active Learning -- 1 Introduction -- 2 Methods -- 2.1 Binary Classification for Tract Segmentation -- 2.2 Active Learning for Tract Selection -- 3 Experiments -- 3.1 Data -- 3.2 Experimental Setup -- 3.3 Results -- 4 Discussion -- References -- Domain-Agnostic Segmentation of Thalamic Nuclei from Joint Structural and Diffusion MRI -- 1 Introduction -- 2 Methods -- 2.1 Training Dataset, Preprocessing, and Data Representation -- 2.2 Domain Randomisation and Data Augmentation -- 2.3 Loss -- 2.4 Architecture and Implementation Details -- 3 Experiments and Results -- 3.1 MRI Data -- 3.2 Competing Methods and Ablations -- 3.3 Results -- 4 Discussion and Conclusion -- References -- Neural Pre-processing: A Learning Framework for End-to-End Brain MRI Pre-processing -- 1 Introduction -- 2 Methods -- 2.1 Model -- 2.2 Loss Function -- 3 Experiments -- 3.1 Runtime Analyses -- 3.2 Pre-processing Performance -- 3.3 Ablation -- 4 Conclusion -- References Dynamic Functional Connectome Harmonics Diagnostic imaging-Data processing-Congresses |
| subject_GND | (DE-588)1071861417 |
| title | Medical Image Computing and Computer Assisted Intervention - MICCAI 2023 26th International Conference, Vancouver, BC, Canada, October 8-12, 2023, Proceedings, Part VIII. |
| title_auth | Medical Image Computing and Computer Assisted Intervention - MICCAI 2023 26th International Conference, Vancouver, BC, Canada, October 8-12, 2023, Proceedings, Part VIII. |
| title_exact_search | Medical Image Computing and Computer Assisted Intervention - MICCAI 2023 26th International Conference, Vancouver, BC, Canada, October 8-12, 2023, Proceedings, Part VIII. |
| title_full | Medical Image Computing and Computer Assisted Intervention - MICCAI 2023 26th International Conference, Vancouver, BC, Canada, October 8-12, 2023, Proceedings, Part VIII. |
| title_fullStr | Medical Image Computing and Computer Assisted Intervention - MICCAI 2023 26th International Conference, Vancouver, BC, Canada, October 8-12, 2023, Proceedings, Part VIII. |
| title_full_unstemmed | Medical Image Computing and Computer Assisted Intervention - MICCAI 2023 26th International Conference, Vancouver, BC, Canada, October 8-12, 2023, Proceedings, Part VIII. |
| title_short | Medical Image Computing and Computer Assisted Intervention - MICCAI 2023 |
| title_sort | medical image computing and computer assisted intervention miccai 2023 26th international conference vancouver bc canada october 8 12 2023 proceedings part viii |
| title_sub | 26th International Conference, Vancouver, BC, Canada, October 8-12, 2023, Proceedings, Part VIII. |
| topic | Diagnostic imaging-Data processing-Congresses |
| topic_facet | Diagnostic imaging-Data processing-Congresses Konferenzschrift 2023 Vancouver |
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