Info
- Utilizzare la checkbox di selezione a fianco di ciascun documento per attivare le funzionalità di stampa, invio email, download nei formati disponibili del (i) record.
Emerging techniques in imaging, modelling and visualization for cardiovascular diagnosis and therapy / / edited by Mihaela Pop, Cristian A Linte
| Emerging techniques in imaging, modelling and visualization for cardiovascular diagnosis and therapy / / edited by Mihaela Pop, Cristian A Linte |
| Pubbl/distr/stampa | Basel, Switzerland : , : MDPI - Multidisciplinary Digital Publishing Institute, , 2023 |
| Descrizione fisica | 1 online resource (308 pages) |
| Disciplina | 616.1 |
| Soggetto topico | Cardiovascular system - Diseases |
| ISBN | 3-0365-7101-9 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910719779403321 |
| Basel, Switzerland : , : MDPI - Multidisciplinary Digital Publishing Institute, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Functional Imaging and Modelling of the Heart [[electronic resource] ] : 9th International Conference, FIMH 2017, Toronto, ON, Canada, June 11-13, 2017, Proceedings / / edited by Mihaela Pop, Graham A Wright
| Functional Imaging and Modelling of the Heart [[electronic resource] ] : 9th International Conference, FIMH 2017, Toronto, ON, Canada, June 11-13, 2017, Proceedings / / edited by Mihaela Pop, Graham A Wright |
| Edizione | [1st ed. 2017.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2017 |
| Descrizione fisica | 1 online resource (XVI, 517 p. 233 illus.) |
| Disciplina | 611.12 |
| Collana | Image Processing, Computer Vision, Pattern Recognition, and Graphics |
| Soggetto topico |
Optical data processing
Artificial intelligence Bioinformatics Computer graphics Cardiology Radiology Image Processing and Computer Vision Artificial Intelligence Computational Biology/Bioinformatics Computer Graphics Imaging / Radiology |
| ISBN | 3-319-59448-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Novel imaging and analysis methods for myocardial tissue characterization and remodeling -- Advanced cardiac image analysis tools for diagnostic and interventions -- Electrophysiology: mapping and biophysical modeling -- Biomechanics and flow: modeling and tissue property measurements. |
| Record Nr. | UNISA-996466178803316 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2017 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Functional Imaging and Modelling of the Heart : 9th International Conference, FIMH 2017, Toronto, ON, Canada, June 11-13, 2017, Proceedings / / edited by Mihaela Pop, Graham A Wright
| Functional Imaging and Modelling of the Heart : 9th International Conference, FIMH 2017, Toronto, ON, Canada, June 11-13, 2017, Proceedings / / edited by Mihaela Pop, Graham A Wright |
| Edizione | [1st ed. 2017.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2017 |
| Descrizione fisica | 1 online resource (XVI, 517 p. 233 illus.) |
| Disciplina | 611.12 |
| Collana | Image Processing, Computer Vision, Pattern Recognition, and Graphics |
| Soggetto topico |
Computer vision
Artificial intelligence Bioinformatics Computer graphics Cardiology Radiology Computer Vision Artificial Intelligence Computational and Systems Biology Computer Graphics |
| ISBN | 3-319-59448-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Novel imaging and analysis methods for myocardial tissue characterization and remodeling -- Advanced cardiac image analysis tools for diagnostic and interventions -- Electrophysiology: mapping and biophysical modeling -- Biomechanics and flow: modeling and tissue property measurements. |
| Record Nr. | UNINA-9910483375903321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2017 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Statistical Atlases and Computational Models of the Heart [[electronic resource] ] : First International Workshop, STACOM 2010, and Cardiac Electrophysical Simulation Challenge, CESC 2010, Held in Conjunction with MICCAI 2010, Beijing, China, September 20, 2010, Proceedings / / edited by Oscar Camara, Mihaela Pop, Kawal Rhode, Maxime Sermesant, Nic Smith, Alistair Young
| Statistical Atlases and Computational Models of the Heart [[electronic resource] ] : First International Workshop, STACOM 2010, and Cardiac Electrophysical Simulation Challenge, CESC 2010, Held in Conjunction with MICCAI 2010, Beijing, China, September 20, 2010, Proceedings / / edited by Oscar Camara, Mihaela Pop, Kawal Rhode, Maxime Sermesant, Nic Smith, Alistair Young |
| Edizione | [1st ed. 2010.] |
| Pubbl/distr/stampa | Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2010 |
| Descrizione fisica | 1 online resource (XII, 292 p. 140 illus.) |
| Disciplina | 006 |
| Collana | Image Processing, Computer Vision, Pattern Recognition, and Graphics |
| Soggetto topico |
Artificial intelligence
Optical data processing Bioinformatics Information storage and retrieval User interfaces (Computer systems) Multimedia information systems Artificial Intelligence Image Processing and Computer Vision Computational Biology/Bioinformatics Information Storage and Retrieval User Interfaces and Human Computer Interaction Multimedia Information Systems |
| Soggetto genere / forma | Kongress |
| ISBN |
1-280-38911-7
9786613567031 3-642-15835-8 |
| Classificazione | 610 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Keynote Presentations -- Atlas Construction and Image Analysis Using Statistical Cardiac Models -- Patient-Specific Modeling of the Heart: Applications to Cardiovascular Disease Management -- The Generation of Patient-Specific Heart Models for Diagnosis and Interventions -- Methods and Infrastructure for Atlas Construction -- The Cardiac Atlas Project: Rationale, Design and Procedures -- The Cardiac Atlas Project: Preliminary Description of Heart Shape in Patients with Myocardial Infarction -- The Cardiac Atlas Project: Development of a Framework Integrating Cardiac Images and Models -- Atlas-Based Quantification of Myocardial Motion Abnormalities: Added-value for the Understanding of CRT Outcome? -- Towards High-Resolution Cardiac Atlases: Ventricular Anatomy Descriptors for a Standardized Reference Frame -- Structure and Flow -- Robust Atlas-Based Segmentation of Highly Variable Anatomy: Left Atrium Segmentation -- Atlas-Based Reduced Models of Blood Flows for Fast Patient-Specific Simulations -- Image and Physiological Data Fusion for Guidance and Modelling of Cardiac Resynchronization Therapy Procedures -- A Multi-method Approach towards Understanding the Pathophysiology of Aortic Dissections – The Complementary Role of In-Silico, In-Vitro and In-Vivo Information -- Endowing Canonical Geometries to Cardiac Structures -- Automatic Segmentation of Left Atrial Geometry from Contrast-Enhanced Magnetic Resonance Images Using a Probabilistic Atlas -- Interactive Cardiac Image Analysis for Biventricular Function of the Human Heart -- Cardiac Motion Estimation Using a ProActive Deformable Model: Evaluation and Sensitivity Analysis -- Investigating Heart Failure Using Ventricular Imaging and Modelling -- Incorporating Low-Level Constraints for the Retrieval of Personalised Heart Models from Dynamic MRI -- Volumetric Myocardial Mechanics from 3D+t Ultrasound Data with Multi-model Tracking -- Mechanics and Motion -- Cardiac Active Contraction Parameters Estimated from Magnetic Resonance Imaging -- Electrophysiology and Electrical Activation -- Recovering Cardiac Electrical Activity from Medical Image Sequence: A Model-Based Approach -- Non-invasive Activation Times Estimation Using 3D Echocardiography -- Modeling Drug Effects on Personalized 3D Models of the Heart: A Simulation Study -- How Much Geometrical Detail Do We Need in Cardiac Electrophysiological Imaging? A Generic Heart-Torso Representation for Fast Subject-Specific Customization -- Influence of Geometric Variations on LV Activation Times: A Study on an Atlas-Based Virtual Population -- Computational Electrophysiological Simulation Challenge (CESC 2010) -- Generic Conduction Parameters for Predicting Activation Waves in Customised Cardiac Electrophysiology Models -- A Statistical Physiological-Model-Constrained Framework for Computational Imaging of Subject-Specific Volumetric Cardiac Electrophysiology Using Optical Imaging and MRI Data -- Estimation of Reaction, Diffusion and Restitution Parameters for a 3D Myocardial Model Using Optical Mapping and MRI -- Personalization of Fast Conduction Purkinje System in Eikonal-Based Electrophysiological Models with Optical Mapping Data. |
| Record Nr. | UNISA-996465939803316 |
| Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2010 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Statistical Atlases and Computational Models of the Heart. ACDC and MMWHS Challenges [[electronic resource] ] : 8th International Workshop, STACOM 2017, Held in Conjunction with MICCAI 2017, Quebec City, Canada, September 10-14, 2017, Revised Selected Papers / / edited by Mihaela Pop, Maxime Sermesant, Pierre-Marc Jodoin, Alain Lalande, Xiahai Zhuang, Guang Yang, Alistair Young, Olivier Bernard
| Statistical Atlases and Computational Models of the Heart. ACDC and MMWHS Challenges [[electronic resource] ] : 8th International Workshop, STACOM 2017, Held in Conjunction with MICCAI 2017, Quebec City, Canada, September 10-14, 2017, Revised Selected Papers / / edited by Mihaela Pop, Maxime Sermesant, Pierre-Marc Jodoin, Alain Lalande, Xiahai Zhuang, Guang Yang, Alistair Young, Olivier Bernard |
| Edizione | [1st ed. 2018.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2018 |
| Descrizione fisica | 1 online resource (XIII, 260 p. 94 illus.) |
| Disciplina | 611.12 |
| Collana | Image Processing, Computer Vision, Pattern Recognition, and Graphics |
| Soggetto topico |
Optical data processing
Artificial intelligence Image Processing and Computer Vision Artificial Intelligence |
| ISBN | 3-319-75541-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Organization -- Contents -- Regular Papers -- Multiview Machine Learning Using an Atlas of Cardiac Cycle Motion -- 1 Introduction -- 2 Materials -- 3 Methods -- 3.1 Motion Atlas Formation -- 3.2 Multiview Classification -- 4 Experiments and Results -- 5 Discussion -- References -- Joint Myocardial Registration and Segmentation of Cardiac BOLD MRI -- 1 Introduction -- 2 Background -- 3 Methods -- 3.1 Dictionary Learning Based Image Segmentation -- 3.2 Graph-Based Joint Optimization -- 3.3 Dictionary Update -- 4 Experimental Results -- 4.1 Data Preparation and Implementation Details -- 4.2 Visual Evaluation -- 4.3 Quantitative Comparison -- 4.4 CAP Dataset -- 5 Conclusion -- References -- Transfer Learning for the Fully Automatic Segmentation of Left Ventricle Myocardium in Porcine Cardiac Cine MR Images -- Abstract -- 1 Introduction -- 2 Method -- 2.1 Data Description -- 2.2 Image Preprocessing -- 2.3 CNN Architecture and Training Setup -- 2.4 Transfer Learning -- 3 Experiments and Results -- 4 Conclusion and Discussions -- References -- Left Atrial Appendage Neck Modeling for Closure Surgery -- 1 Introduction -- 2 LAA Segmentation -- 3 LAA Neck Modeling -- 3.1 Auto-Detection of the Ostium of the LAA -- 3.2 Establishment of the Standard Coordinate System Based on the Ostium Plane -- 3.3 Auto-Building of Circumscribed Cylindrical Model of LAA Neck -- 4 Experiments and Results -- 4.1 Dataset -- 4.2 Ground Truth -- 4.3 Evaluation -- 5 Conclusion -- References -- Detection of Substances in the Left Atrial Appendage by Spatiotemporal Motion Analysis Based on 4D-CT -- 1 Introduction -- 2 Method -- 2.1 Extraction of Optical Flow Fields of Adjacent Phase -- 2.2 The Tracking of Key Voxels in Whole Cardiac Cycle -- 2.3 Hierarchical Clustering of All Trajectory Curves.
2.4 Time-Frequency Analysis of the Track Curve of Critical Lumps - to Realize the Stress and Strain Detection of Lumps -- 3 Experiment and Discussion -- 3.1 Dataset -- 3.2 Evaluation and Results -- 4 Conclusion -- References -- Estimation of Healthy and Fibrotic Tissue Distributions in DE-CMR Incorporating CINE-CMR in an EM Algorithm -- 1 Introduction -- 2 Methods -- 3 Experimental Results -- 4 Conclusions -- References -- Multilevel Non-parametric Groupwise Registration in Cardiac MRI: Application to Explanted Porcine Hearts -- 1 Introduction -- 2 Methods -- 2.1 Data Acquisition -- 2.2 Pairwise Registration of the Anatomical MR Images -- 3 Groupwise Registration -- 4 Results -- 5 Future Work and Conclusions -- References -- ACDC Challenge -- GridNet with Automatic Shape Prior Registration for Automatic MRI Cardiac Segmentation -- 1 Introduction -- 2 Our Method -- 2.1 Shape Prior -- 2.2 Loss -- 2.3 Proposed Network -- 3 Experimental Setup and Results -- 3.1 Dataset, Evaluation Criteria, and Other Methods -- 3.2 Experimental Results -- 4 Conclusion -- References -- A Radiomics Approach to Computer-Aided Diagnosis with Cardiac Cine-MRI -- 1 Introduction -- 2 Method -- 2.1 Data Description -- 2.2 Semi-automatic Segmentation -- 2.3 Radiomics Features for Cardiac Diagnosis -- 2.4 Classification Method -- 2.5 Radiomic Feature Selection -- 3 Results -- 4 Conclusions -- References -- Fast Fully-Automatic Cardiac Segmentation in MRI Using MRF Model Optimization, Substructures Tracking and B-Spline Smoothing -- 1 Introduction -- 2 Automatic Localization of the Heart -- 3 Segmentation of an ED Phase Slice in Between Base and Mid-Ventricle -- 4 Segmentation Based on Tracking the Cardiac Substructures in ED Phase -- 5 Segmentation in the ES Phase -- 6 Left Ventricle Epicardial Boundary Smoothing -- 7 Global Results and First Conclusions -- References. Automatic Segmentation and Disease Classification Using Cardiac Cine MR Images -- 1 Introduction -- 2 Data -- 3 Methods -- 3.1 Segmentation -- 3.2 Diagnosis -- 4 Experiments and Results -- 4.1 Segmentation Results -- 4.2 Diagnosis Results -- 5 Discussion and Conclusion -- References -- An Exploration of 2D and 3D Deep Learning Techniques for Cardiac MR Image Segmentation -- 1 Introduction -- 2 Method -- 2.1 Pre-Processing -- 2.2 Network Architectures -- 2.3 Optimisation -- 2.4 Post-Processing -- 3 Experiments and Results -- 3.1 Data -- 3.2 Evaluation Measures -- 3.3 Experiment 1: Comparison of Loss Functions -- 3.4 Experiment 2: Comparison of Network Architectures -- 3.5 Discussion and Conclusion -- References -- Automatic Cardiac Disease Assessment on cine-MRI via Time-Series Segmentation and Domain Specific Features -- 1 Introduction -- 2 Methods -- 2.1 Cardiac cine-MRI Dataset -- 2.2 Segmentation -- 2.3 Cardiac Disease Classification -- 3 Results -- 4 Discussion -- References -- 2D-3D Fully Convolutional Neural Networks for Cardiac MR Segmentation -- 1 Introduction -- 2 Method -- 2.1 Network Architecture -- 2.2 Dataset, Preprocessing and Augmentation -- 2.3 Training -- 2.4 Optimization Function -- 3 Results -- 4 Discussion -- 5 Conclusion -- References -- Densely Connected Fully Convolutional Network for Short-Axis Cardiac Cine MR Image Segmentation and Heart Diagnosis Using Random Forest -- 1 Introduction and Related Work -- 2 Our Method -- 2.1 Data Pre-processing Pipeline -- 2.2 Proposed Network Architecture: Densely Connected Fully Convolutional Network (DFCN) -- 2.3 Loss Function -- 2.4 Post-processing -- 2.5 Cardiac Disease Diagnosis -- 3 Experimental Setup and Results -- 3.1 Dataset and Evaluation Criteria -- 3.2 Experimental Results -- 3.3 Conclusion -- References. Class-Balanced Deep Neural Network for Automatic Ventricular Structure Segmentation -- 1 Introduction -- 2 Methodology -- 2.1 Efficient Semantic Labeling with 3D FCN -- 2.2 Transfer Learning from C3D Model -- 2.3 Promote Training with Deep Supervision -- 2.4 Investigation of Class-Balanced Loss -- 3 Experimental Results -- 4 Conclusions -- References -- Automatic Segmentation of LV and RV in Cardiac MRI -- Abstract -- 1 Introduction -- 2 Methods -- 2.1 Dataset -- 2.2 Preprocessing -- 2.3 Architecture -- 3 Experimental Results -- 3.1 Implemented Details -- 3.2 Results and Quantitative Analysis with Other Methods -- 4 Conclusion and Discussion -- Acknowledgement -- References -- Automatic Multi-Atlas Segmentation of Myocardium with SVF-Net -- 1 Introduction -- 2 Rigid Alignment by Landmarks Detection -- 3 Non-rigid Diffeomorphic Registration with SVF-Net -- 4 Label Fusion Method -- 5 Results and Discussion -- 6 Conclusion -- References -- MM-WHS Challenge -- 3D Convolutional Networks for Fully Automatic Fine-Grained Whole Heart Partition -- 1 Introduction -- 2 Methodology -- 2.1 Dense Semantic Labeling with 3D FCN -- 2.2 Knowledge Transfer from C3D Model -- 2.3 Promote Training with Deep Supervision -- 2.4 Multi-class Balanced Loss Function -- 3 Experimental Results -- 4 Conclusions -- References -- Multi-label Whole Heart Segmentation Using CNNs and Anatomical Label Configurations -- 1 Introduction -- 2 Method -- 3 Experimental Setup -- 4 Results and Discussion -- 5 Conclusion -- References -- Multi-Planar Deep Segmentation Networks for Cardiac Substructures from MRI and CT -- 1 Introduction -- 2 Multi-Object Multi-Planar CNN (MO-MP-CNN) -- 3 Experimental Results -- 4 Discussion and Conclusion -- References -- Local Probabilistic Atlases and a Posteriori Correction for the Segmentation of Heart Images -- 1 Introduction -- 2 Methods. 2.1 Construction of the a Priori Information -- 2.2 Segmentation -- 2.3 A Posteriori Correction -- 3 Experiments -- 4 Results -- 5 Conclusion -- References -- Hybrid Loss Guided Convolutional Networks for Whole Heart Parsing -- 1 Introduction -- 2 Methodology -- 2.1 Intensity Calibration as Preprocessing -- 2.2 Enhance the Training of 3D FCN -- 2.3 Hybrid Loss Guided Class-Balanced Segmentation -- 3 Experimental Results -- 4 Conclusions -- References -- 3D Deeply-Supervised U-Net Based Whole Heart Segmentation -- 1 Introduction -- 2 Method -- 2.1 Data Pre-processing -- 2.2 Network Architecture -- 3 Experiments and Results -- 3.1 Data -- 3.2 Performance on Training Set -- 3.3 Performance on Testing Set -- 4 Discussion and Conclusion -- References -- MRI Whole Heart Segmentation Using Discrete Nonlinear Registration and Fast Non-local Fusion -- 1 Introduction and Related Work -- 2 Discrete Registration -- 3 Non-local Label Fusion -- 4 Multi-label Random Walk Regularisation -- 5 Discussion and Conclusion -- References -- Automatic Whole Heart Segmentation Using Deep Learning and Shape Context -- 1 Introduction -- 2 Methods -- 2.1 2.5D Segmentation Using Orthogonal U-Nets -- 2.2 Shape Context Generation -- 2.3 Shape-Context Guided U-Net -- 2.4 Implementation Details -- 3 Results -- 4 Discussion and Conclusion -- References -- Automatic Whole Heart Segmentation in CT Images Based on Multi-atlas Image Registration -- Abstract -- 1 Introduction -- 2 Methodology -- 2.1 A Three-Step Multi-atlas-Based Whole Heart Segmentation -- 2.2 Multiple Atlas Images -- 3 Experimental Results -- 4 Conclusion -- References -- Author Index. |
| Record Nr. | UNISA-996465519703316 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2018 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Statistical Atlases and Computational Models of the Heart. ACDC and MMWHS Challenges : 8th International Workshop, STACOM 2017, Held in Conjunction with MICCAI 2017, Quebec City, Canada, September 10-14, 2017, Revised Selected Papers / / edited by Mihaela Pop, Maxime Sermesant, Pierre-Marc Jodoin, Alain Lalande, Xiahai Zhuang, Guang Yang, Alistair Young, Olivier Bernard
| Statistical Atlases and Computational Models of the Heart. ACDC and MMWHS Challenges : 8th International Workshop, STACOM 2017, Held in Conjunction with MICCAI 2017, Quebec City, Canada, September 10-14, 2017, Revised Selected Papers / / edited by Mihaela Pop, Maxime Sermesant, Pierre-Marc Jodoin, Alain Lalande, Xiahai Zhuang, Guang Yang, Alistair Young, Olivier Bernard |
| Edizione | [1st ed.. 2018.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2018 |
| Descrizione fisica | 1 online resource (XIII, 260 p. 94 illus.) |
| Disciplina | 611.12 |
| Collana | Image Processing, Computer Vision, Pattern Recognition, and Graphics |
| Soggetto topico |
Computer vision
Artificial intelligence Computer Vision Artificial Intelligence |
| ISBN |
9783319755410
3319755412 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Organization -- Contents -- Regular Papers -- Multiview Machine Learning Using an Atlas of Cardiac Cycle Motion -- 1 Introduction -- 2 Materials -- 3 Methods -- 3.1 Motion Atlas Formation -- 3.2 Multiview Classification -- 4 Experiments and Results -- 5 Discussion -- References -- Joint Myocardial Registration and Segmentation of Cardiac BOLD MRI -- 1 Introduction -- 2 Background -- 3 Methods -- 3.1 Dictionary Learning Based Image Segmentation -- 3.2 Graph-Based Joint Optimization -- 3.3 Dictionary Update -- 4 Experimental Results -- 4.1 Data Preparation and Implementation Details -- 4.2 Visual Evaluation -- 4.3 Quantitative Comparison -- 4.4 CAP Dataset -- 5 Conclusion -- References -- Transfer Learning for the Fully Automatic Segmentation of Left Ventricle Myocardium in Porcine Cardiac Cine MR Images -- Abstract -- 1 Introduction -- 2 Method -- 2.1 Data Description -- 2.2 Image Preprocessing -- 2.3 CNN Architecture and Training Setup -- 2.4 Transfer Learning -- 3 Experiments and Results -- 4 Conclusion and Discussions -- References -- Left Atrial Appendage Neck Modeling for Closure Surgery -- 1 Introduction -- 2 LAA Segmentation -- 3 LAA Neck Modeling -- 3.1 Auto-Detection of the Ostium of the LAA -- 3.2 Establishment of the Standard Coordinate System Based on the Ostium Plane -- 3.3 Auto-Building of Circumscribed Cylindrical Model of LAA Neck -- 4 Experiments and Results -- 4.1 Dataset -- 4.2 Ground Truth -- 4.3 Evaluation -- 5 Conclusion -- References -- Detection of Substances in the Left Atrial Appendage by Spatiotemporal Motion Analysis Based on 4D-CT -- 1 Introduction -- 2 Method -- 2.1 Extraction of Optical Flow Fields of Adjacent Phase -- 2.2 The Tracking of Key Voxels in Whole Cardiac Cycle -- 2.3 Hierarchical Clustering of All Trajectory Curves.
2.4 Time-Frequency Analysis of the Track Curve of Critical Lumps - to Realize the Stress and Strain Detection of Lumps -- 3 Experiment and Discussion -- 3.1 Dataset -- 3.2 Evaluation and Results -- 4 Conclusion -- References -- Estimation of Healthy and Fibrotic Tissue Distributions in DE-CMR Incorporating CINE-CMR in an EM Algorithm -- 1 Introduction -- 2 Methods -- 3 Experimental Results -- 4 Conclusions -- References -- Multilevel Non-parametric Groupwise Registration in Cardiac MRI: Application to Explanted Porcine Hearts -- 1 Introduction -- 2 Methods -- 2.1 Data Acquisition -- 2.2 Pairwise Registration of the Anatomical MR Images -- 3 Groupwise Registration -- 4 Results -- 5 Future Work and Conclusions -- References -- ACDC Challenge -- GridNet with Automatic Shape Prior Registration for Automatic MRI Cardiac Segmentation -- 1 Introduction -- 2 Our Method -- 2.1 Shape Prior -- 2.2 Loss -- 2.3 Proposed Network -- 3 Experimental Setup and Results -- 3.1 Dataset, Evaluation Criteria, and Other Methods -- 3.2 Experimental Results -- 4 Conclusion -- References -- A Radiomics Approach to Computer-Aided Diagnosis with Cardiac Cine-MRI -- 1 Introduction -- 2 Method -- 2.1 Data Description -- 2.2 Semi-automatic Segmentation -- 2.3 Radiomics Features for Cardiac Diagnosis -- 2.4 Classification Method -- 2.5 Radiomic Feature Selection -- 3 Results -- 4 Conclusions -- References -- Fast Fully-Automatic Cardiac Segmentation in MRI Using MRF Model Optimization, Substructures Tracking and B-Spline Smoothing -- 1 Introduction -- 2 Automatic Localization of the Heart -- 3 Segmentation of an ED Phase Slice in Between Base and Mid-Ventricle -- 4 Segmentation Based on Tracking the Cardiac Substructures in ED Phase -- 5 Segmentation in the ES Phase -- 6 Left Ventricle Epicardial Boundary Smoothing -- 7 Global Results and First Conclusions -- References. Automatic Segmentation and Disease Classification Using Cardiac Cine MR Images -- 1 Introduction -- 2 Data -- 3 Methods -- 3.1 Segmentation -- 3.2 Diagnosis -- 4 Experiments and Results -- 4.1 Segmentation Results -- 4.2 Diagnosis Results -- 5 Discussion and Conclusion -- References -- An Exploration of 2D and 3D Deep Learning Techniques for Cardiac MR Image Segmentation -- 1 Introduction -- 2 Method -- 2.1 Pre-Processing -- 2.2 Network Architectures -- 2.3 Optimisation -- 2.4 Post-Processing -- 3 Experiments and Results -- 3.1 Data -- 3.2 Evaluation Measures -- 3.3 Experiment 1: Comparison of Loss Functions -- 3.4 Experiment 2: Comparison of Network Architectures -- 3.5 Discussion and Conclusion -- References -- Automatic Cardiac Disease Assessment on cine-MRI via Time-Series Segmentation and Domain Specific Features -- 1 Introduction -- 2 Methods -- 2.1 Cardiac cine-MRI Dataset -- 2.2 Segmentation -- 2.3 Cardiac Disease Classification -- 3 Results -- 4 Discussion -- References -- 2D-3D Fully Convolutional Neural Networks for Cardiac MR Segmentation -- 1 Introduction -- 2 Method -- 2.1 Network Architecture -- 2.2 Dataset, Preprocessing and Augmentation -- 2.3 Training -- 2.4 Optimization Function -- 3 Results -- 4 Discussion -- 5 Conclusion -- References -- Densely Connected Fully Convolutional Network for Short-Axis Cardiac Cine MR Image Segmentation and Heart Diagnosis Using Random Forest -- 1 Introduction and Related Work -- 2 Our Method -- 2.1 Data Pre-processing Pipeline -- 2.2 Proposed Network Architecture: Densely Connected Fully Convolutional Network (DFCN) -- 2.3 Loss Function -- 2.4 Post-processing -- 2.5 Cardiac Disease Diagnosis -- 3 Experimental Setup and Results -- 3.1 Dataset and Evaluation Criteria -- 3.2 Experimental Results -- 3.3 Conclusion -- References. Class-Balanced Deep Neural Network for Automatic Ventricular Structure Segmentation -- 1 Introduction -- 2 Methodology -- 2.1 Efficient Semantic Labeling with 3D FCN -- 2.2 Transfer Learning from C3D Model -- 2.3 Promote Training with Deep Supervision -- 2.4 Investigation of Class-Balanced Loss -- 3 Experimental Results -- 4 Conclusions -- References -- Automatic Segmentation of LV and RV in Cardiac MRI -- Abstract -- 1 Introduction -- 2 Methods -- 2.1 Dataset -- 2.2 Preprocessing -- 2.3 Architecture -- 3 Experimental Results -- 3.1 Implemented Details -- 3.2 Results and Quantitative Analysis with Other Methods -- 4 Conclusion and Discussion -- Acknowledgement -- References -- Automatic Multi-Atlas Segmentation of Myocardium with SVF-Net -- 1 Introduction -- 2 Rigid Alignment by Landmarks Detection -- 3 Non-rigid Diffeomorphic Registration with SVF-Net -- 4 Label Fusion Method -- 5 Results and Discussion -- 6 Conclusion -- References -- MM-WHS Challenge -- 3D Convolutional Networks for Fully Automatic Fine-Grained Whole Heart Partition -- 1 Introduction -- 2 Methodology -- 2.1 Dense Semantic Labeling with 3D FCN -- 2.2 Knowledge Transfer from C3D Model -- 2.3 Promote Training with Deep Supervision -- 2.4 Multi-class Balanced Loss Function -- 3 Experimental Results -- 4 Conclusions -- References -- Multi-label Whole Heart Segmentation Using CNNs and Anatomical Label Configurations -- 1 Introduction -- 2 Method -- 3 Experimental Setup -- 4 Results and Discussion -- 5 Conclusion -- References -- Multi-Planar Deep Segmentation Networks for Cardiac Substructures from MRI and CT -- 1 Introduction -- 2 Multi-Object Multi-Planar CNN (MO-MP-CNN) -- 3 Experimental Results -- 4 Discussion and Conclusion -- References -- Local Probabilistic Atlases and a Posteriori Correction for the Segmentation of Heart Images -- 1 Introduction -- 2 Methods. 2.1 Construction of the a Priori Information -- 2.2 Segmentation -- 2.3 A Posteriori Correction -- 3 Experiments -- 4 Results -- 5 Conclusion -- References -- Hybrid Loss Guided Convolutional Networks for Whole Heart Parsing -- 1 Introduction -- 2 Methodology -- 2.1 Intensity Calibration as Preprocessing -- 2.2 Enhance the Training of 3D FCN -- 2.3 Hybrid Loss Guided Class-Balanced Segmentation -- 3 Experimental Results -- 4 Conclusions -- References -- 3D Deeply-Supervised U-Net Based Whole Heart Segmentation -- 1 Introduction -- 2 Method -- 2.1 Data Pre-processing -- 2.2 Network Architecture -- 3 Experiments and Results -- 3.1 Data -- 3.2 Performance on Training Set -- 3.3 Performance on Testing Set -- 4 Discussion and Conclusion -- References -- MRI Whole Heart Segmentation Using Discrete Nonlinear Registration and Fast Non-local Fusion -- 1 Introduction and Related Work -- 2 Discrete Registration -- 3 Non-local Label Fusion -- 4 Multi-label Random Walk Regularisation -- 5 Discussion and Conclusion -- References -- Automatic Whole Heart Segmentation Using Deep Learning and Shape Context -- 1 Introduction -- 2 Methods -- 2.1 2.5D Segmentation Using Orthogonal U-Nets -- 2.2 Shape Context Generation -- 2.3 Shape-Context Guided U-Net -- 2.4 Implementation Details -- 3 Results -- 4 Discussion and Conclusion -- References -- Automatic Whole Heart Segmentation in CT Images Based on Multi-atlas Image Registration -- Abstract -- 1 Introduction -- 2 Methodology -- 2.1 A Three-Step Multi-atlas-Based Whole Heart Segmentation -- 2.2 Multiple Atlas Images -- 3 Experimental Results -- 4 Conclusion -- References -- Author Index. |
| Record Nr. | UNINA-9910349458403321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2018 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Statistical Atlases and Computational Models of the Heart. Atrial Segmentation and LV Quantification Challenges [[electronic resource] ] : 9th International Workshop, STACOM 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16, 2018, Revised Selected Papers / / edited by Mihaela Pop, Maxime Sermesant, Jichao Zhao, Shuo Li, Kristin McLeod, Alistair Young, Kawal Rhode, Tommaso Mansi
| Statistical Atlases and Computational Models of the Heart. Atrial Segmentation and LV Quantification Challenges [[electronic resource] ] : 9th International Workshop, STACOM 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16, 2018, Revised Selected Papers / / edited by Mihaela Pop, Maxime Sermesant, Jichao Zhao, Shuo Li, Kristin McLeod, Alistair Young, Kawal Rhode, Tommaso Mansi |
| Edizione | [1st ed. 2019.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 |
| Descrizione fisica | 1 online resource (XIV, 487 p. 216 illus., 192 illus. in color.) |
| Disciplina | 006.3 |
| Collana | Image Processing, Computer Vision, Pattern Recognition, and Graphics |
| Soggetto topico |
Optical data processing
Artificial intelligence Computer communication systems Data mining Image Processing and Computer Vision Artificial Intelligence Computer Communication Networks Data Mining and Knowledge Discovery |
| ISBN | 3-030-12029-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Cardiac imaging and image processing -- Machine learning applied to cardiac imaging and image analysis -- Atlas construction -- Statistical modelling of cardiac function across different patient populations -- Cardiac computational physiology -- Model customization -- Atlas based functional analysis -- Ontological schemata for data and results -- Integrated functional and structural analyses -- Pre-clinical and clinical applicability of these methods. |
| Record Nr. | UNISA-996466443503316 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Statistical Atlases and Computational Models of the Heart. Atrial Segmentation and LV Quantification Challenges : 9th International Workshop, STACOM 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16, 2018, Revised Selected Papers / / edited by Mihaela Pop, Maxime Sermesant, Jichao Zhao, Shuo Li, Kristin McLeod, Alistair Young, Kawal Rhode, Tommaso Mansi
| Statistical Atlases and Computational Models of the Heart. Atrial Segmentation and LV Quantification Challenges : 9th International Workshop, STACOM 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16, 2018, Revised Selected Papers / / edited by Mihaela Pop, Maxime Sermesant, Jichao Zhao, Shuo Li, Kristin McLeod, Alistair Young, Kawal Rhode, Tommaso Mansi |
| Edizione | [1st ed. 2019.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 |
| Descrizione fisica | 1 online resource (XIV, 487 p. 216 illus., 192 illus. in color.) |
| Disciplina |
006.3
616.120757 |
| Collana | Image Processing, Computer Vision, Pattern Recognition, and Graphics |
| Soggetto topico |
Computer vision
Artificial intelligence Computer networks Data mining Computer Vision Artificial Intelligence Computer Communication Networks Data Mining and Knowledge Discovery |
| ISBN | 3-030-12029-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Cardiac imaging and image processing -- Machine learning applied to cardiac imaging and image analysis -- Atlas construction -- Statistical modelling of cardiac function across different patient populations -- Cardiac computational physiology -- Model customization -- Atlas based functional analysis -- Ontological schemata for data and results -- Integrated functional and structural analyses -- Pre-clinical and clinical applicability of these methods. |
| Record Nr. | UNINA-9910337573303321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Statistical Atlases and Computational Models of the Heart. Imaging and Modelling Challenges [[electronic resource] ] : 7th International Workshop, STACOM 2016, Held in Conjunction with MICCAI 2016, Athens, Greece, October 17, 2016, Revised Selected Papers / / edited by Tommaso Mansi, Kristin McLeod, Mihaela Pop, Kawal Rhode, Maxime Sermesant, Alistair Young
| Statistical Atlases and Computational Models of the Heart. Imaging and Modelling Challenges [[electronic resource] ] : 7th International Workshop, STACOM 2016, Held in Conjunction with MICCAI 2016, Athens, Greece, October 17, 2016, Revised Selected Papers / / edited by Tommaso Mansi, Kristin McLeod, Mihaela Pop, Kawal Rhode, Maxime Sermesant, Alistair Young |
| Edizione | [1st ed. 2017.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2017 |
| Descrizione fisica | 1 online resource (XI, 230 p. 108 illus.) |
| Disciplina | 611.12 |
| Collana | Image Processing, Computer Vision, Pattern Recognition, and Graphics |
| Soggetto topico |
Optical data processing
Health informatics Computer simulation Mathematical statistics Pattern recognition Cardiology Image Processing and Computer Vision Health Informatics Simulation and Modeling Probability and Statistics in Computer Science Pattern Recognition |
| ISBN | 3-319-52718-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Function across different patient populations -- Cardiac mapping -- Cardiac computational physiology -- Model customization -- Image-based modelling and image-guided interventional procedures -- Atlas based functional analysis.-Ontological schemata for data and results -- Integrated functional and structural analyses -- Pre-clinical and clinical applicability of the methods described. |
| Record Nr. | UNISA-996465753603316 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2017 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Statistical Atlases and Computational Models of the Heart. Imaging and Modelling Challenges : 7th International Workshop, STACOM 2016, Held in Conjunction with MICCAI 2016, Athens, Greece, October 17, 2016, Revised Selected Papers / / edited by Tommaso Mansi, Kristin McLeod, Mihaela Pop, Kawal Rhode, Maxime Sermesant, Alistair Young
| Statistical Atlases and Computational Models of the Heart. Imaging and Modelling Challenges : 7th International Workshop, STACOM 2016, Held in Conjunction with MICCAI 2016, Athens, Greece, October 17, 2016, Revised Selected Papers / / edited by Tommaso Mansi, Kristin McLeod, Mihaela Pop, Kawal Rhode, Maxime Sermesant, Alistair Young |
| Edizione | [1st ed. 2017.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2017 |
| Descrizione fisica | 1 online resource (XI, 230 p. 108 illus.) |
| Disciplina | 611.12 |
| Collana | Image Processing, Computer Vision, Pattern Recognition, and Graphics |
| Soggetto topico |
Computer vision
Medical informatics Computer simulation Computer science - Mathematics Mathematical statistics Pattern recognition systems Cardiology Computer Vision Health Informatics Computer Modelling Probability and Statistics in Computer Science Automated Pattern Recognition |
| ISBN | 3-319-52718-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Function across different patient populations -- Cardiac mapping -- Cardiac computational physiology -- Model customization -- Image-based modelling and image-guided interventional procedures -- Atlas based functional analysis.-Ontological schemata for data and results -- Integrated functional and structural analyses -- Pre-clinical and clinical applicability of the methods described. |
| Record Nr. | UNINA-9910483531403321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2017 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
