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Atlas and anatomy of PET/MRI, PET/CT and SPECT/CT / / edited by E. Edmund Kim [and three others]
| Atlas and anatomy of PET/MRI, PET/CT and SPECT/CT / / edited by E. Edmund Kim [and three others] |
| Edizione | [2nd ed.] |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (288 pages) |
| Disciplina | 616.07575 |
| Soggetto topico |
Nuclear medicine
Tomography, Emission Imatges per ressonància magnètica Tomografia Diagnòstic per la imatge Anatomia humana |
| Soggetto genere / forma |
Atles anatòmics
Llibres electrònics |
| ISBN | 3-030-92349-5 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910741142303321 |
| Cham, Switzerland : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Atlas of differential diagnosis : MRI / / Guoguang Fan
| Atlas of differential diagnosis : MRI / / Guoguang Fan |
| Autore | Fan Guoguang |
| Pubbl/distr/stampa | Singapore : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (379 pages) |
| Disciplina | 616.07548 |
| Soggetto topico |
Magnetic resonance imaging
Magnetic resonance imaging - Digital techniques Imatges per ressonància magnètica Diagnòstic diferencial |
| Soggetto genere / forma | Llibres electrònics |
| ISBN |
981-16-9763-9
981-16-9762-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Contents -- List of Author and Contributors -- Author -- Contributors -- Part I: Brain -- 1: MRI Signal Characteristics of Brain -- 1.1 Signal Characteristics of Normal Tissue -- 1.2 Signal Characteristics of Pathological Tissues -- 1.3 Tissue with High and Low Signal on MRI -- 2: Differential Diagnosis of Brain Diseases -- 2.1 Abnormal Signals of Brain Parenchyma with Common Diseases -- 2.2 Differential Diagnosis of Cerebral Infarction, Inflammation, and Tumor -- 2.3 Differential Diagnosis of Intracerebral and Extracerebral Tumors -- 2.4 Differential Diagnosis of Various Types of Brain Edema -- 2.5 Differential Diagnosis of Hydrocephalus and Cerebral Atrophy -- 2.6 Differential Diagnosis of Astrocytomas at Various Levels -- 2.7 Differential Diagnosis of Common Tumors of Brain Parenchyma -- 2.8 Differential Diagnosis of Common Cystic Lesions in Sellar Region -- 2.9 Differential Diagnosis of Common Solid Lesions in Sellar Region -- 2.10 Differential Diagnosis of Tumors in Cerebellopontine Angle Region -- 2.11 Differential Diagnosis of Common Tumors in Posterior Fossa -- 2.12 Differential Diagnosis of Pineal Region Tumors -- 2.13 Differential Diagnosis of Brainstem Diseases -- 2.14 Differential Diagnosis of Congenital Myelin Sheath Disease -- 3: Differential Diagnosis of Cerebrovascular and Infectious Diseases -- 3.1 Differential Diagnosis of Various Stages of Intracranial Hemorrhage -- 3.2 Differential Diagnosis of Vascular Leukoencephalopathy and Multiple Sclerosis -- 3.3 Differential Diagnosis of Brain Abscess Stages -- 3.4 Differential Diagnosis of Brain Infectious Diseases -- 4: Differential Diagnosis of Ventricular and Cisternal Diseases -- 4.1 Differential Diagnosis of Common Tumors in the Fourth Ventricle -- 4.2 Differential Diagnosis of Common Tumors in Lateral Ventricle.
4.3 Differential Diagnosis of Tumors in the Third Ventricle -- 5: Differential Diagnosis of Meningeal and Cranial Diseases -- 5.1 Differential Diagnosis of Meningeal Diseases -- 5.2 Differential Diagnosis of Common Skull Diseases -- Part II: Head and Neck -- 6: Differential Diagnosis of Basilar Disease -- 6.1 Differential Diagnosis of Anterior Cranial Fossa Diseases -- 6.2 Differential Diagnosis of Anterior Cranial Fossa Involved Diseases -- 6.3 Differential Diagnosis of Disease in the Slope Area -- 6.4 Differential Diagnosis of Disease in the Jugular Foramen Area -- 7: Differential Diagnosis of Orbital and Ocular Diseases -- 7.1 Differential Diagnosis of Preorbital Septal Disease -- 7.2 Differential Diagnosis of Common Ocular Diseases in Infants -- 7.3 Differential Diagnosis of Common Eyeball Diseases in Adults -- 7.4 Differential Diagnosis of Extraocular Muscle Thickening Disease -- 7.5 Differential Diagnosis of Orbital Tumors -- 7.6 Differential Diagnosis of Optic Nerve and Optic Nerve Sheath Lesions -- 7.7 Differential Diagnosis of Lacrimal Gland Tumors -- 8: Differential Diagnosis of Ear Diseases -- 8.1 Differential Diagnosis of Soft Tissue Mass in the Tympanum -- 8.2 Differential Diagnosis of Cystic Diseases of the Tip of Petrous Bone -- 8.3 Differential Diagnosis of Solid Lesions in Petrous Apex -- 9: Differential Diagnosis of Paranasal Sinus and Nasal Diseases -- 9.1 Differential Diagnosis of Common Nasal Diseases -- 9.2 Differential Diagnosis of Common Sinus Diseases -- 9.3 Differential Diagnosis of Sinus Cyst -- 10: Differential Diagnosis of Throat Disease -- 10.1 Differential Diagnosis of Common Neck Diseases -- 10.2 Differential Diagnosis of Neck Cystic Disease -- 10.3 Differential Diagnosis of Nasopharyngeal Diseases -- 10.4 Differential Diagnosis of Laryngeal Nodular Lesions. 10.5 Differential Diagnosis of Common Tumors in Salivary Glands -- 10.6 Differential Diagnosis of Common Thyroid Diseases -- 10.7 Differential Diagnosis of Common Parathyroid Diseases -- Part III: Spine and Spinal Cord -- 11: Differential Diagnosis of Diseases in Spine and Spinal Cord -- 11.1 Differential Diagnosis of Lumbar Disc Herniation -- 11.2 Differential Diagnosis of the Common Spinal Localized Lesions -- 11.3 Differential Diagnosis of Spinal Common Diffused Lesions -- 11.4 Differential Diagnosis of Common Diseases in the Sacrococcygeal Region -- 11.5 Differential Diagnosis of Common Diseases in the Vertebral Appendices -- 11.6 Differential Diagnosis of Common Diseases in the Spinal Cord -- 11.7 Differential Diagnosis of Common Diseases in the Extra Spinal Subdural -- 11.8 Differential Diagnosis of Congenital Diseases in the Spinal Canal (I) -- 11.9 Differential Diagnosis of Congenital Diseases in the Spinal Canal (II) -- Part IV: Musculoskeletal System -- 12: Differential Diagnosis of Bone and Marrow Diseases -- 12.1 Differential Diagnosis of Benign and Malignant Bone Tumors -- 12.2 Differential Diagnosis of Common Diseases of the Hand and Foot Bones -- 12.3 Differential Diagnosis of Common Benign Diseases in the Metaphysis of Long Tubular Bones -- 12.4 Differential Diagnosis of Common Benign Diseases of Long Tubular Bones Diaphysis -- 12.5 Differential Diagnosis of Common Infectious Diseases of Long Tubular Bone Diaphysis -- 12.6 Differential Diagnosis of Common Malignant Tumors in Long Tubular Bones -- 12.7 Differential Diagnosis of Common Diseases in Pelvis -- 13: Differential Diagnosis of Joint and Soft Tissue Diseases -- 13.1 Differential Diagnosis of Knee Joint Disease -- 13.2 Differential Diagnosis of Hip Joint Disease -- 13.3 Differential Diagnosis of Soft Tissue Disease -- Part V: Chest. 14: Differential Diagnosis of Respiratory System Diseases -- 14.1 Differential Diagnosis of the Solitary Pulmonary Nodule -- 14.2 Differential Diagnosis of the Benign and Malignant Tumors in the Chest Wall -- 14.3 Differential Diagnosis of the Solid Lesions in the Mediastinum -- 14.4 Differential Diagnosis of the Cystic Lesions in the Mediastinum -- 14.5 Differential Diagnosis of the Lesions in the Mediastinum Lymph Nodes -- 15: Differential Diagnosis of Heart and Aorta Diseases -- 15.1 Differential Diagnosis of the Myocardial Infarction -- 15.2 Differential Diagnosis of Common Diseases in the Myocardium and Pericardium -- 15.3 Differential Diagnosis of Common Myocardium Tumors -- 15.4 Differential Diagnosis of the Aortic Diseases -- 15.5 Differential Diagnosis of the Breast Benign and Malignant Tumors -- Part VI: Abdomen and Pelvis -- 16: Differential Diagnosis of Digestive Diseases -- 16.1 Differential Diagnosis of Solid Liver Disease (1) -- 16.2 Differential Diagnosis of Solid Liver Disease (2) -- 16.3 Differential Diagnosis of Liver Cystic Disease -- 16.4 Differential Diagnosis of Liver Nodular Disease -- 16.5 Differential Diagnosis of Pediatric Liver Tumors -- 16.6 Differential Diagnosis of Hepatic Diffuse Disease -- 16.7 Differential Diagnosis of Gallbladder Disease -- 16.8 Differential Diagnosis of Bile Duct Disease -- 16.9 Differential Diagnosis of Splenic Disease -- 16.10 Differential Diagnosis of Pancreatic Solid Disease -- 16.11 Differential Diagnosis of Pancreatic Cystic Disease -- 16.12 Differential Diagnosis of Gastric Diseases -- 16.13 Differential Diagnosis of Peritoneal Disease -- 16.14 Differential Diagnosis of Common Diseases in the Retroperitoneal Space -- 17: Differential Diagnosis of Urinary Tract Diseases -- 17.1 Differential Diagnosis of Renal Cystic Lesions. 17.2 Differential Diagnosis of Renal Tumors -- 17.3 Differential Diagnosis of Renal Pelvis Lesions -- 17.4 Differential Diagnosis of Adrenal Disease -- 17.5 Differential Diagnosis of Bladder Disease -- 18: Differential Diagnosis of Reproductive System Diseases -- 18.1 Differential Diagnosis of Prostate Disease -- 18.2 Differential Diagnosis of Common Diseases of the Uterus -- 18.3 Differential Diagnosis of Common Ovarian Diseases. |
| Record Nr. | UNINA-9910743254903321 |
Fan Guoguang
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| Singapore : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Computational diffusion MRI : International MICCAI Workshop, Lima, Peru, October 2020 / / edited by Noemi Gyori [and five others]
| Computational diffusion MRI : International MICCAI Workshop, Lima, Peru, October 2020 / / edited by Noemi Gyori [and five others] |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (301 pages) |
| Disciplina | 616.07548 |
| Collana | Mathematics and Visualization |
| Soggetto topico |
Optical data processing
Imatges per ressonància magnètica |
| Soggetto genere / forma |
Congressos
Llibres electrònics |
| ISBN | 3-030-73018-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Programme Committee -- Preface -- Contents -- Diffusion MRI Signal Acquisition -- Image Reconstruction from Accelerated Slice-Interleaved Diffusion Encoding Data -- 1 Introduction -- 2 Methods -- 2.1 SIDE Acquisition -- 2.2 Reconstruction -- 2.3 Optimization -- 3 Experiments -- 3.1 Materials -- 3.2 Results -- 4 Conclusion -- References -- Towards Learned Optimal q-Space Sampling in Diffusion MRI -- 1 Introduction -- 1.1 Main Contributions -- 2 Method -- 2.1 Forward Model: Sub-Sampling Layer -- 2.2 Reconstruction Model -- 2.3 Optimization -- 3 Experimental Evaluation -- 3.1 Dataset -- 3.2 Training Settings -- 3.3 Results and Discussion -- 4 Conclusion -- 5 Supplementary Materials -- References -- A Signal Peak Separation Indexpg for Axisymmetric B-Tensor Encoding -- 1 Introduction -- 2 Theory -- 2.1 A Toy Model of Fascicle Crossing Under B-Tensor Encoding -- 2.2 The Signal Peak Separation Index -- 3 Methods -- 4 Results -- 5 Discussion and Conclusion -- References -- Orientation Processing: Tractography and Visualization -- Improving Tractography Accuracy Using Dynamic Filtering -- 1 Introduction -- 2 Materials and Methods -- 2.1 Initial Set of Streamlines -- 2.2 Parametric Representation of the Streamlines -- 2.3 Optimization -- 2.4 Data and Experiments -- 3 Results and Discussion -- 4 Conclusions -- References -- Diffeomorphic Alignment of Along-Tract Diffusion Profiles from Tractography -- 1 Introduction -- 2 Alignment of Along-Tract Diffusion Measure Profiles -- 2.1 Representation -- 2.2 Objective Function for Joint Alignment -- 2.3 Alternating Minimization for Subject-Level and Tract-Level Alignment -- 3 Results -- 3.1 Data -- 3.2 Along-Tract FA Profiles Before and After Joint Alignment -- 3.3 Reduced Coefficient of Variation -- 3.4 Subject-Wise Inter-tract Correlations.
3.5 Intraclass Correlation Coefficient for Reliability Across Time Points -- 4 Discussion -- References -- Direct Reconstruction of Crossing Muscle Fibers in the Human Tongue Using a Deep Neural Network -- 1 Introduction -- 2 Methods -- 2.1 Training Data and Ground Truth -- 2.2 Fiber Estimation Network -- 2.3 Fiber Estimation Loss -- 2.4 Training Procedure -- 3 Experiments and Results -- 3.1 Quantitative Evaluation on Synthetic Tongue HARDI Data -- 3.2 Qualitative Results on Post-mortem Human Tongue Data -- 4 Discussion and Conclusions -- References -- Learning Anatomical Segmentations for Tractography from Diffusion MRI -- 1 Introduction -- 2 Materials and Methods -- 2.1 Data -- 2.2 Data Representations -- 2.3 Architecture -- 2.4 Training -- 2.5 Tracts -- 2.6 Evaluation Criteria -- 3 Results and Discussion -- 3.1 Evaluation 1: Q-Space Sampling Density -- 3.2 Evaluation 2: Input Representations -- 3.3 Evaluation 3: Generalization -- 3.4 Evaluation 4: Tract Similarity -- 4 Conclusion -- References -- Diffusion MRI Fiber Orientation Distribution Function Estimation Using Voxel-Wise Spherical U-Net -- 1 Introduction -- 2 Background and Method -- 2.1 Voxel-Wise Spherical U-Net -- 3 Dataset -- 4 Experiments and Implementation Details -- 5 Results and Conclusions -- References -- Microstructure Modeling and Representation -- Stick Stippling for Joint 3D Visualization of Diffusion MRI Fiber Orientations and Density -- 1 Introduction -- 2 Methods -- 2.1 Diffusion Modeling and the Fixel Representation -- 2.2 Fixel Glyph Visualization -- 3 Experiments and Results -- 3.1 Clinical Data Experiment -- 3.2 HCP Experiment -- 3.3 RESOLVE Experiment -- 4 Discussion and Conclusions -- References -- Q-Space Quantitative Diffusion MRI Measures Using a Stretched-Exponential Representation -- 1 Introduction -- 2 Theory -- 2.1 Diffusion MR Signal Representation. 2.2 Q-Space Domain Quantitative Measures -- 2.3 Numerical Implementation -- 2.4 Optimization of Stretched-Exponential Representation -- 3 Materials and Methods -- 3.1 Ex Vivo rat brain data -- 3.2 In Vivo Human brain data -- 3.3 Comparison to the Q-Space Measures from Different Methods -- 4 Results and Discussion -- 5 Conclusions -- References -- Repeatability of Soma and Neurite Metrics in Cortical and Subcortical Grey Matter -- 1 Introduction -- 2 Methods -- 2.1 Image Acquisition and Pre-processing -- 2.2 Image Processing and Analysis -- 3 Results -- 4 Discussion -- References -- DW-MRI Microstructure Model of Models Captured Via Single-Shell Bottleneck Deep Learning -- 1 Introduction -- 2 Related Work -- 3 Data Acquisition -- 4 Proposed Method -- 5 Results -- 6 Discussion -- References -- Deep Learning Model Fitting for Diffusion-Relaxometry: A Comparative Study -- 1 Introduction -- 2 Methods -- 2.1 qMRI Model Fitting with DNNs -- 2.2 In Silico Study -- 2.3 In Vivo study -- 3 Results -- 4 Discussion -- 5 Conclusion -- References -- Pretraining Improves Deep Learning Based Tissue Microstructure Estimation -- 1 Introduction -- 2 Methods -- 2.1 Problem Formulation -- 2.2 Signal Generation for Pretraining -- 2.3 Backbone Deep Network -- 2.4 Pretraining with the Auxiliary Dataset and Fine-Tuning -- 2.5 Implementation Details -- 3 Results -- 4 Discussion -- 5 Conclusion -- References -- Signal Augmentation and Super Resolution -- Enhancing Diffusion Signal Augmentation Using Spherical Convolutions -- 1 Introduction -- 2 Signal Augmentation -- 2.1 Deep Learning Models -- 2.2 Spherical Deep Learning Models -- 2.3 Material -- 3 Evaluation -- 3.1 Results -- 4 Discussion -- 5 Conclusion -- References -- Hybrid Graph Convolutional Neural Networks for Super Resolution of DW Images -- 1 Introduction -- 2 Dataset -- 3 Methods. 3.1 Coarse SR Prediction in 3D Grid Structure Space -- 3.2 Refinement by GCNN in Diffusion Gradient Space -- 3.3 Loss Function -- 4 Experiments -- 5 Conclusion -- References -- Manifold-Aware CycleGAN for High-Resolution Structural-to-DTI Synthesis -- 1 Introduction -- 2 Method -- 2.1 Log-Euclidean Metric -- 2.2 Adversarial Loss -- 2.3 Cycle Consistency Loss -- 2.4 Manifold-Aware Wasserstein CycleGAN -- 3 Experiments -- 4 Discussion and Conclusion -- References -- Diffusion MRI Applications -- Beyond Lesion-Load: Tractometry-Based Metrics for Characterizing White Matter Lesions within Fibre Pathways -- 1 Introduction -- 2 Theory and Methods -- 2.1 Clinical Assessment -- 2.2 Acquisition -- 2.3 Processing -- 2.4 Proposed Metrics -- 3 Results -- 3.1 Lesion Mapping -- 3.2 Volumetric Metrics -- 3.3 Tractometry-Based Metrics -- 4 Discussion and Conclusion -- References -- Multi-modal Brain Age Estimation: A Comparative Study Confirms the Importance of Microstructure -- 1 Introduction -- 2 Data and Materials -- 3 Methods -- 3.1 Brain Age Estimation -- 3.2 Associations with IDPs and Non-IDP Variables -- 4 Results -- 4.1 Brain Age Estimation -- 4.2 Association with Brain IDPs -- 4.3 Association with Cardiac Variables -- 5 Discussion -- References -- Longitudinal Parcellation of the Infant Cortex Using Multi-modal Connectome Harmonics -- 1 Introduction -- 2 Methods -- 2.1 Data and Preprocessing -- 2.2 Connectivity Matrices -- 2.3 Iterative Multi-modal Parcellation Via Connectome Harmonics -- 2.4 Optimal Cluster Number Determination -- 3 Results -- 3.1 Homogeneity -- 3.2 Community Detection -- 4 Discussion -- 5 Conclusion -- References -- Automatic Segmentation of Dentate Nuclei for Microstructure Assessment: Example of Application to Temporal Lobe Epilepsy Patients -- 1 Introduction -- 2 Methods -- 2.1 Subjects -- 2.2 MRI Protocol -- 2.3 DWI Processing. 2.4 DNs Segmentation -- 2.5 Post Processing for OPAL and CNN -- 2.6 Quantitative Evaluation -- 2.7 Comparison of Automatic Methods -- 2.8 Clinical Application to TLE Data -- 3 Results -- 3.1 Comparison of the Three Automatic Methods -- 3.2 Application to TLE Dataset -- 4 Discussion -- 5 Conclusion -- References -- Two Parallel Stages Deep Learning Network for Anterior Visual Pathway Segmentation -- 1 Introduction -- 2 Methods -- 2.1 Data Preprocessing -- 2.2 Two Parallel Stages Network Architecture -- 3 Experiments -- 3.1 Dataset -- 3.2 Implementation Details -- 3.3 Results -- 4 Conclusion -- References -- Exploring DTI Benchmark Databases Through Visual Analytics -- 1 Introduction -- 2 Related Work -- 3 Use Case -- 4 Implementation -- 5 Discussion -- 6 Conclusions and Future Work -- References -- Index. |
| Record Nr. | UNISA-996466396303316 |
| Cham, Switzerland : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Computational diffusion MRI : International MICCAI Workshop, Lima, Peru, October 2020 / / edited by Noemi Gyori [and five others]
| Computational diffusion MRI : International MICCAI Workshop, Lima, Peru, October 2020 / / edited by Noemi Gyori [and five others] |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (301 pages) |
| Disciplina | 616.07548 |
| Collana | Mathematics and Visualization |
| Soggetto topico |
Optical data processing
Imatges per ressonància magnètica |
| Soggetto genere / forma |
Congressos
Llibres electrònics |
| ISBN | 3-030-73018-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Programme Committee -- Preface -- Contents -- Diffusion MRI Signal Acquisition -- Image Reconstruction from Accelerated Slice-Interleaved Diffusion Encoding Data -- 1 Introduction -- 2 Methods -- 2.1 SIDE Acquisition -- 2.2 Reconstruction -- 2.3 Optimization -- 3 Experiments -- 3.1 Materials -- 3.2 Results -- 4 Conclusion -- References -- Towards Learned Optimal q-Space Sampling in Diffusion MRI -- 1 Introduction -- 1.1 Main Contributions -- 2 Method -- 2.1 Forward Model: Sub-Sampling Layer -- 2.2 Reconstruction Model -- 2.3 Optimization -- 3 Experimental Evaluation -- 3.1 Dataset -- 3.2 Training Settings -- 3.3 Results and Discussion -- 4 Conclusion -- 5 Supplementary Materials -- References -- A Signal Peak Separation Indexpg for Axisymmetric B-Tensor Encoding -- 1 Introduction -- 2 Theory -- 2.1 A Toy Model of Fascicle Crossing Under B-Tensor Encoding -- 2.2 The Signal Peak Separation Index -- 3 Methods -- 4 Results -- 5 Discussion and Conclusion -- References -- Orientation Processing: Tractography and Visualization -- Improving Tractography Accuracy Using Dynamic Filtering -- 1 Introduction -- 2 Materials and Methods -- 2.1 Initial Set of Streamlines -- 2.2 Parametric Representation of the Streamlines -- 2.3 Optimization -- 2.4 Data and Experiments -- 3 Results and Discussion -- 4 Conclusions -- References -- Diffeomorphic Alignment of Along-Tract Diffusion Profiles from Tractography -- 1 Introduction -- 2 Alignment of Along-Tract Diffusion Measure Profiles -- 2.1 Representation -- 2.2 Objective Function for Joint Alignment -- 2.3 Alternating Minimization for Subject-Level and Tract-Level Alignment -- 3 Results -- 3.1 Data -- 3.2 Along-Tract FA Profiles Before and After Joint Alignment -- 3.3 Reduced Coefficient of Variation -- 3.4 Subject-Wise Inter-tract Correlations.
3.5 Intraclass Correlation Coefficient for Reliability Across Time Points -- 4 Discussion -- References -- Direct Reconstruction of Crossing Muscle Fibers in the Human Tongue Using a Deep Neural Network -- 1 Introduction -- 2 Methods -- 2.1 Training Data and Ground Truth -- 2.2 Fiber Estimation Network -- 2.3 Fiber Estimation Loss -- 2.4 Training Procedure -- 3 Experiments and Results -- 3.1 Quantitative Evaluation on Synthetic Tongue HARDI Data -- 3.2 Qualitative Results on Post-mortem Human Tongue Data -- 4 Discussion and Conclusions -- References -- Learning Anatomical Segmentations for Tractography from Diffusion MRI -- 1 Introduction -- 2 Materials and Methods -- 2.1 Data -- 2.2 Data Representations -- 2.3 Architecture -- 2.4 Training -- 2.5 Tracts -- 2.6 Evaluation Criteria -- 3 Results and Discussion -- 3.1 Evaluation 1: Q-Space Sampling Density -- 3.2 Evaluation 2: Input Representations -- 3.3 Evaluation 3: Generalization -- 3.4 Evaluation 4: Tract Similarity -- 4 Conclusion -- References -- Diffusion MRI Fiber Orientation Distribution Function Estimation Using Voxel-Wise Spherical U-Net -- 1 Introduction -- 2 Background and Method -- 2.1 Voxel-Wise Spherical U-Net -- 3 Dataset -- 4 Experiments and Implementation Details -- 5 Results and Conclusions -- References -- Microstructure Modeling and Representation -- Stick Stippling for Joint 3D Visualization of Diffusion MRI Fiber Orientations and Density -- 1 Introduction -- 2 Methods -- 2.1 Diffusion Modeling and the Fixel Representation -- 2.2 Fixel Glyph Visualization -- 3 Experiments and Results -- 3.1 Clinical Data Experiment -- 3.2 HCP Experiment -- 3.3 RESOLVE Experiment -- 4 Discussion and Conclusions -- References -- Q-Space Quantitative Diffusion MRI Measures Using a Stretched-Exponential Representation -- 1 Introduction -- 2 Theory -- 2.1 Diffusion MR Signal Representation. 2.2 Q-Space Domain Quantitative Measures -- 2.3 Numerical Implementation -- 2.4 Optimization of Stretched-Exponential Representation -- 3 Materials and Methods -- 3.1 Ex Vivo rat brain data -- 3.2 In Vivo Human brain data -- 3.3 Comparison to the Q-Space Measures from Different Methods -- 4 Results and Discussion -- 5 Conclusions -- References -- Repeatability of Soma and Neurite Metrics in Cortical and Subcortical Grey Matter -- 1 Introduction -- 2 Methods -- 2.1 Image Acquisition and Pre-processing -- 2.2 Image Processing and Analysis -- 3 Results -- 4 Discussion -- References -- DW-MRI Microstructure Model of Models Captured Via Single-Shell Bottleneck Deep Learning -- 1 Introduction -- 2 Related Work -- 3 Data Acquisition -- 4 Proposed Method -- 5 Results -- 6 Discussion -- References -- Deep Learning Model Fitting for Diffusion-Relaxometry: A Comparative Study -- 1 Introduction -- 2 Methods -- 2.1 qMRI Model Fitting with DNNs -- 2.2 In Silico Study -- 2.3 In Vivo study -- 3 Results -- 4 Discussion -- 5 Conclusion -- References -- Pretraining Improves Deep Learning Based Tissue Microstructure Estimation -- 1 Introduction -- 2 Methods -- 2.1 Problem Formulation -- 2.2 Signal Generation for Pretraining -- 2.3 Backbone Deep Network -- 2.4 Pretraining with the Auxiliary Dataset and Fine-Tuning -- 2.5 Implementation Details -- 3 Results -- 4 Discussion -- 5 Conclusion -- References -- Signal Augmentation and Super Resolution -- Enhancing Diffusion Signal Augmentation Using Spherical Convolutions -- 1 Introduction -- 2 Signal Augmentation -- 2.1 Deep Learning Models -- 2.2 Spherical Deep Learning Models -- 2.3 Material -- 3 Evaluation -- 3.1 Results -- 4 Discussion -- 5 Conclusion -- References -- Hybrid Graph Convolutional Neural Networks for Super Resolution of DW Images -- 1 Introduction -- 2 Dataset -- 3 Methods. 3.1 Coarse SR Prediction in 3D Grid Structure Space -- 3.2 Refinement by GCNN in Diffusion Gradient Space -- 3.3 Loss Function -- 4 Experiments -- 5 Conclusion -- References -- Manifold-Aware CycleGAN for High-Resolution Structural-to-DTI Synthesis -- 1 Introduction -- 2 Method -- 2.1 Log-Euclidean Metric -- 2.2 Adversarial Loss -- 2.3 Cycle Consistency Loss -- 2.4 Manifold-Aware Wasserstein CycleGAN -- 3 Experiments -- 4 Discussion and Conclusion -- References -- Diffusion MRI Applications -- Beyond Lesion-Load: Tractometry-Based Metrics for Characterizing White Matter Lesions within Fibre Pathways -- 1 Introduction -- 2 Theory and Methods -- 2.1 Clinical Assessment -- 2.2 Acquisition -- 2.3 Processing -- 2.4 Proposed Metrics -- 3 Results -- 3.1 Lesion Mapping -- 3.2 Volumetric Metrics -- 3.3 Tractometry-Based Metrics -- 4 Discussion and Conclusion -- References -- Multi-modal Brain Age Estimation: A Comparative Study Confirms the Importance of Microstructure -- 1 Introduction -- 2 Data and Materials -- 3 Methods -- 3.1 Brain Age Estimation -- 3.2 Associations with IDPs and Non-IDP Variables -- 4 Results -- 4.1 Brain Age Estimation -- 4.2 Association with Brain IDPs -- 4.3 Association with Cardiac Variables -- 5 Discussion -- References -- Longitudinal Parcellation of the Infant Cortex Using Multi-modal Connectome Harmonics -- 1 Introduction -- 2 Methods -- 2.1 Data and Preprocessing -- 2.2 Connectivity Matrices -- 2.3 Iterative Multi-modal Parcellation Via Connectome Harmonics -- 2.4 Optimal Cluster Number Determination -- 3 Results -- 3.1 Homogeneity -- 3.2 Community Detection -- 4 Discussion -- 5 Conclusion -- References -- Automatic Segmentation of Dentate Nuclei for Microstructure Assessment: Example of Application to Temporal Lobe Epilepsy Patients -- 1 Introduction -- 2 Methods -- 2.1 Subjects -- 2.2 MRI Protocol -- 2.3 DWI Processing. 2.4 DNs Segmentation -- 2.5 Post Processing for OPAL and CNN -- 2.6 Quantitative Evaluation -- 2.7 Comparison of Automatic Methods -- 2.8 Clinical Application to TLE Data -- 3 Results -- 3.1 Comparison of the Three Automatic Methods -- 3.2 Application to TLE Dataset -- 4 Discussion -- 5 Conclusion -- References -- Two Parallel Stages Deep Learning Network for Anterior Visual Pathway Segmentation -- 1 Introduction -- 2 Methods -- 2.1 Data Preprocessing -- 2.2 Two Parallel Stages Network Architecture -- 3 Experiments -- 3.1 Dataset -- 3.2 Implementation Details -- 3.3 Results -- 4 Conclusion -- References -- Exploring DTI Benchmark Databases Through Visual Analytics -- 1 Introduction -- 2 Related Work -- 3 Use Case -- 4 Implementation -- 5 Discussion -- 6 Conclusions and Future Work -- References -- Index. |
| Record Nr. | UNINA-9910502988403321 |
| Cham, Switzerland : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Mathematical Modeling of the Human Brain : From Magnetic Resonance Images to Finite Element Simulation / Kent-André Mardal, Marie E. Rognes, Travis B. Thompson, Lars Magnus Valnes
| Mathematical Modeling of the Human Brain : From Magnetic Resonance Images to Finite Element Simulation / Kent-André Mardal, Marie E. Rognes, Travis B. Thompson, Lars Magnus Valnes |
| Autore | Mardal Kent-André |
| Edizione | [1st edition.] |
| Pubbl/distr/stampa | Cham, : Springer Nature, 2022 |
| Descrizione fisica | 1 online resource (129 pages) : (XVI, 118 p. 32 illus., 25 illus. in color. :) |
| Altri autori (Persone) |
RognesMarie E
ThompsonTravis B ValnesLars Magnus |
| Collana | Simula SpringerBriefs on Computing |
| Soggetto topico |
Human physiology
Biomathematics Mathematical models Cervell Imatges per ressonància magnètica Models matemàtics |
| Soggetto genere / forma | Llibres electrònics |
| Soggetto non controllato |
magnetic resonance imaging
Mesh generation mathematical modeling finite element methods scientific computing |
| ISBN | 3-030-95136-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Introduction --Working with magnetic resonance images of the brain --From T1 images to numerical simulation --Introducing heterogeneities --Introducing directionality with diffusion tensors --Simulating anisotropic diffusion in heterogeneous brain regions --Concluding remarks and outlook --References --Index. |
| Record Nr. | UNINA-9910548277003321 |
|
Mardal Kent-André
|
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| Cham, : Springer Nature, 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Mathematical Modeling of the Human Brain : From Magnetic Resonance Images to Finite Element Simulation / Kent-André Mardal, Marie E. Rognes, Travis B. Thompson, Lars Magnus Valnes
| Mathematical Modeling of the Human Brain : From Magnetic Resonance Images to Finite Element Simulation / Kent-André Mardal, Marie E. Rognes, Travis B. Thompson, Lars Magnus Valnes |
| Autore | Mardal Kent-André |
| Edizione | [1st edition.] |
| Pubbl/distr/stampa | Cham, : Springer Nature, 2022 |
| Descrizione fisica | 1 online resource (129 pages) : (XVI, 118 p. 32 illus., 25 illus. in color. :) |
| Altri autori (Persone) |
RognesMarie E
ThompsonTravis B ValnesLars Magnus |
| Collana | Simula SpringerBriefs on Computing |
| Soggetto topico |
Human physiology
Biomathematics Mathematical models Cervell Imatges per ressonància magnètica Models matemàtics |
| Soggetto genere / forma | Llibres electrònics |
| Soggetto non controllato |
magnetic resonance imaging
Mesh generation mathematical modeling finite element methods scientific computing |
| ISBN | 3-030-95136-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Introduction --Working with magnetic resonance images of the brain --From T1 images to numerical simulation --Introducing heterogeneities --Introducing directionality with diffusion tensors --Simulating anisotropic diffusion in heterogeneous brain regions --Concluding remarks and outlook --References --Index. |
| Record Nr. | UNISA-996466419203316 |
|
Mardal Kent-André
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| Cham, : Springer Nature, 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
MRI of degenerative disease of the spine : a case-based atlas / / Paola D'Aprile, Alfredo Tarantino
| MRI of degenerative disease of the spine : a case-based atlas / / Paola D'Aprile, Alfredo Tarantino |
| Autore | D'Aprile Paola |
| Edizione | [Second edition.] |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (184 pages) |
| Disciplina | 616.73 |
| Soggetto topico |
Spine - Diseases
Malalties de la columna vertebral Diagnòstic per la imatge Imatges per ressonància magnètica |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 3-030-73707-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910492147003321 |
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D'Aprile Paola
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| Cham, Switzerland : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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The Physics of Clinical MR Taught Through Images / / by Val M. Runge, Johannes T. Heverhagen
| The Physics of Clinical MR Taught Through Images / / by Val M. Runge, Johannes T. Heverhagen |
| Autore | Runge Val M. |
| Edizione | [5th ed. 2022.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022 |
| Descrizione fisica | 1 online resource (371 pages) |
| Disciplina | 616.07548 |
| Soggetto topico |
Radiology
Medical physics Neurology Nervous system - Surgery Medical Physics Neurosurgery Imatges per ressonància magnètica Radiologia mèdica |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 3-030-85413-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Section I. Hardware -- Section II. Basic Imaging Physics -- Section III. Basic Image Acquisition -- Section IV. Advanced Image Acquisition -- Section V. Flow -- Section VI. Tissue- -- Section VII. Artifacts, Including Those Due to Motion, and the Recluction Thereof -- Section VIII. Further Improving Diagnostic Quality, Technologic Innovation -- Section IX Recent Innovations -- Section X Appendix. |
| Record Nr. | UNINA-9910574060203321 |
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Runge Val M.
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| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Preclinical MRI of the Kidney [[electronic resource] ] : Methods and Protocols / / edited by Andreas Pohlmann, Thoralf Niendorf
| Preclinical MRI of the Kidney [[electronic resource] ] : Methods and Protocols / / edited by Andreas Pohlmann, Thoralf Niendorf |
| Autore | Pohlmann Andreas |
| Edizione | [1st ed. 2021.] |
| Pubbl/distr/stampa | Springer Nature, 2021 |
| Descrizione fisica | 1 online resource (XIV, 725 p. 202 illus., 169 illus. in color.) |
| Disciplina | 543 |
| Collana | Methods in Molecular Biology |
| Soggetto topico |
Analytical chemistry
Biology—Technique Radiology Physiology Medicine—Research Biology—Research Analytical Chemistry Biological Techniques Biomedical Research Ronyó Fisiologia humana Imatges per ressonància magnètica |
| Soggetto genere / forma | Llibres electrònics |
| Soggetto non controllato |
Analytical Chemistry
Biological Techniques Imaging / Radiology Physiology Biomedicine, general Biological Imaging Radiology Renal Physiology Preclinical Research renal pathophysiology diabetic nephropathy circadian rhythm Magnetic field strength Radio frequency coils NCMRA CEMRA Open Access Biology, life sciences Scientific equipment, experiments & techniques Medical imaging Life sciences: general issues Medical research |
| ISBN | 1-0716-0978-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Recommendations for Preclinical Renal MRI: A Comprehensive Open-Access Protocol Collection to Improve Training, Reproducibility, and Comparability of Studies -- Animal Models of Renal Pathophysiology and Disease -- Preparation and Monitoring of Small Animals in Renal MRI -- Reversible (Patho-)Physiologically Relevant Test Interventions: Rationale and Examples -- Preparation of Ex Vivo Rodent Phantoms for Developing, Testing, and Training MR Imaging of the Kidney and Other Organs -- Quantitative Assessment of Renal Perfusion and Oxygenation by Invasive Probes: Basic Concepts -- Ultrasound and Photoacoustic Imaging of the Kidney: Basic Concepts and Protocols -- Hardware Considerations for Preclinical Magnetic Resonance of the Kidney -- MRI Mapping of Renal T1: Basic Concept -- MRI Mapping of the Blood Oxygenation Sensitive Parameter T2* in the Kidney: Basic Concept -- Renal Diffusion Weighted Imaging (DWI) for Apparent Diffusion Coefficient (ADC), Intra Voxel Incoherent Motion (IVIM), and Diffusion Tensor Imaging (DTI): Basic Concept -- Dynamic Contrast Enhancement (DCE)-MRI Derived Renal Perfusion and Filtration: Basic Concepts -- Non-Invasive Renal Perfusion Measurement Using Arterial Spin Labelling (ASL) MRI: Basic Concept -- Renal pH Imaging Using Chemical Exchange Saturation Transfer (CEST)-MRI: Basic Concepts -- Sodium (23Na) MRI of the Kidney: Basic Concept -- Hyperpolarized Carbon (13C) MRI of the Kidneys: Basic Concepts -- Functional Imaging Using Fluorine (19F) MR Methods: Basic Concepts -- MR Elastography of the Abdomen: Basic Concepts -- Monitoring Renal Hemodynamics and Oxygenation by Invasive Probes: Experimental Protocol -- Essential Practical Steps for MRI of the Kidney in Experimental Research -- Assessment of Renal Volume with MRI: Experimental Protocol -- Experimental Protocols for MRI Mapping of Renal T1 -- Experimental Protocols for MRI Mapping of the Blood Oxygenation Sensitive Parameters T2* and T2 in the Kidney -- Renal MRI Diffusion: Experimental Protocol -- Dynamic Contrast Enhanced (DCE)-MRI Derived Renal Perfusion and Filtration: Experimental Protocol -- Renal Blood Flow Using Arterial Spin Labeling (ASL)-MRI: Experimental Protocol and Principles -- Renal pH Mapping Using Chemical Exchange Saturation Transfer (CEST)-MRI: Experimental Protocol -- Sodium (23Na) MRI of the Kidney: Experimental Protocol -- Hyperpolarized Carbon (13C) MRI of the Kidney: Experimental Protocol -- Fluorine (19F) MRI for Assessing Inflammatory Cells in the Kidney: Experimental Protocol -- Fluorine (19F) MRI to Measure Renal Oxygen Tension and Blood Volume: Experimental Protocol -- MR Elastography of the Abdomen: Experimental Protocols -- Subsegmentation of the Kidney in Experimental MR Images Using Morphology-Based Regions-of-Interest or Multiple-Layer Concentric Objects -- De-Noising for Improved Parametric MRI of the Kidney: Protocol for Non-Local Means Filtering -- Analysis Protocols for MRI Mapping of Renal T1 -- Analysis Protocols for MRI Mapping of the Blood Oxygenation Sensitive Parameters T2* and T2 in the Kidney -- Analysis of Renal Diffusion Weighted Imaging (DWI) Using Apparent Diffusion Coefficient (ADC) and Intra Voxel Incoherent Motion (IVIM) Models -- Analysis Protocol for Dynamic Contrast Enhanced (DCE)-MRI of Renal Perfusion and Filtration -- Quantitative Analysis of Renal Perfusion by Arterial Spin Labeling -- Analysis Protocol for the Quantification of Renal pH Using Chemical Exchange Saturation Transfer (CEST)-MRI -- Analysis Protocol for Renal Sodium (23Na) MR Imaging -- Analysis Methods for Hyperpolarized Carbon (13C) MRI of the Kidney -- Data Preparation Protocol for Low Signal-to-Noise Ratio Fluorine-19 MRI. |
| Record Nr. | UNINA-9910476759403321 |
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Pohlmann Andreas
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| Springer Nature, 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Statistical Atlases and Computational Models of the Heart. M&Ms and EMIDEC Challenges [[electronic resource] ] : 11th International Workshop, STACOM 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, Revised Selected Papers / / edited by Esther Puyol Anton, Mihaela Pop, Maxime Sermesant, Victor Campello, Alain Lalande, Karim Lekadir, Avan Suinesiaputra, Oscar Camara, Alistair Young
| Statistical Atlases and Computational Models of the Heart. M&Ms and EMIDEC Challenges [[electronic resource] ] : 11th International Workshop, STACOM 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, Revised Selected Papers / / edited by Esther Puyol Anton, Mihaela Pop, Maxime Sermesant, Victor Campello, Alain Lalande, Karim Lekadir, Avan Suinesiaputra, Oscar Camara, Alistair Young |
| Edizione | [1st ed. 2021.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021 |
| Descrizione fisica | 1 online resource (XV, 417 p. 176 illus., 165 illus. in color.) |
| Disciplina | 621.367 |
| Collana | Image Processing, Computer Vision, Pattern Recognition, and Graphics |
| Soggetto topico |
Computer vision
Machine learning Pattern recognition systems Social sciences - Data processing Education - Data processing Computer Vision Machine Learning Automated Pattern Recognition Computer Application in Social and Behavioral Sciences Computers and Education Aprenentatge automàtic Intel·ligència artificial Imatges per ressonància magnètica Malalties cardiovasculars |
| Soggetto genere / forma |
Congressos
Llibres electrònics |
| ISBN | 3-030-68107-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Regular papers -- A persistent homology-based topological loss function for multi-class CNN segmentation of cardiac MRI -- Automatic multiplanar CT reformatting from trans-axial into left ventricle short-axis view -- Graph convolutional regression of cardiac depolarization from sparse endocardial maps -- A cartesian grid representation of left atrial appendages for deep learning based estimation of thrombogenic risk predictors -- Measure Anatomical Thickness from Cardiac MRI with Deep Neural Networks -- Modelling Fine-rained Cardiac Motion via Spatio-temporal Graph Convolutional Networks to Boost the Diagnosis of Heart Conditions- Towards mesh-free patient-specific mitral valve modeling -- PIEMAP: Personalized Inverse Eikonal Model from cardiac Electro-Anatomical Maps -- Automatic Detection of Landmarks for Fast Cardiac MR Image Registration -- Quality-aware semi-supervised learning for CMR segmentation -- Estimation of imaging biomarker’s progression in post-infarct patients using cross-sectional data -- PC-U Net: Learning to Jointly Reconstruct and Segment the Cardiac Walls in 3D from CT Data -- Shape constrained CNN for cardiac MR segmentation with simultaneous prediction of shape and pose parameters -- Left atrial ejection fraction estimation using SEGANet for fully automated segmentation of CINE MRI -- Estimation of Cardiac Valve Annuli Motion with Deep Learning -- 4D Flow Magnetic Resonance Imaging for Left Atrial Haemodynamic Characterization and Model Calibration -- Segmentation-free Estimation of Aortic Diameters from MRI Using Deep Learning -- M&Ms challenge -- Histogram Matching Augmentation for Domain Adaptation with Application to Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Image Segmentation -- Disentangled Representations for Domain-generalized Cardiac Segmentation -- A 2-step Deep Learning method with Domain Adaptation for Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Magnetic Resonance Segmentation -- Random Style Transfer based Domain Generalization Networks Integrating Shape and Spatial Information -- Semi-supervised Cardiac Image Segmentation via Label Propagation and Style Transfer -- Domain-Adversarial Learning for Multi-Centre, Multi-Vendor, and Multi-Disease Cardiac MR Image Segmentation -- Studying Robustness of Segmantic Segmentation under Domain Shift in cardiac MRI -- A deep convolutional neural network approach for the segmentation of cardiac structures from MRI sequences -- Multi-center, Multi-vendor, and Multi-disease Cardiac Image Segmentation Using Scale-Independent Multi-Gate UNET -- Adaptive Preprocessing for Generalization in Cardiac MR Image Segmentation -- Deidentifying MRI data domain by iterative backpropagation -- A generalizable deep-learning approach for cardiac magnetic resonance image segmentation using image augmentation and attention U-Net -- Generalisable Cardiac Structure Segmentation via Attentional and Stacked Image Adaptation -- Style-invariant Cardiac Image Segmentation with Test-time Augmentation -- EMIDEC challenge -- Comparison of a Hybrid Mixture Model and a CNN for the Segmentation of Myocardial Pathologies in Delayed Enhancement MRI -- Cascaded Convolutional Neural Network for Automatic Myocardial Infarction Segmentation from Delayed-Enhancement Cardiac MRI -- Automatic Myocardial Disease Prediction From Delayed-Enhancement Cardiac MRI and Clinical Information -- SM2N2: A Stacked Architecture for Multimodal Data and its Application to Myocardial Infarction Detection -- A Hybrid Network for Automatic Myocardial Infarction Segmentation in Delayed Enhancement-MRI -- Efficient 3D deep learning for myocardial diseases segmentation -- Deep-learning-based myocardial pathology detection -- Automatic Myocardial Infarction Evaluation from Delayed-Enhancement Cardiac MRI using Deep Convolutional Networks -- Uncertainty-based Segmentation of Myocardial Infarction Areas on Cardiac MR images -- Anatomy Prior Based U-net for Pathology Segmentation with Attention -- Automatic Scar Segmentation from DE-MRI Using 2D Dilated UNet with Rotation-based Augmentation -- Classification of pathological cases of myocardial infarction using Convolutional Neural Network and Random Forest. . |
| Record Nr. | UNISA-996464521503316 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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