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.
Intelligent Orthopaedics [[electronic resource] ] : Artificial Intelligence and Smart Image-guided Technology for Orthopaedics / / edited by Guoyan Zheng, Wei Tian, Xiahai Zhuang
| Intelligent Orthopaedics [[electronic resource] ] : Artificial Intelligence and Smart Image-guided Technology for Orthopaedics / / edited by Guoyan Zheng, Wei Tian, Xiahai Zhuang |
| Edizione | [1st ed. 2018.] |
| Pubbl/distr/stampa | Singapore : , : Springer Singapore : , : Imprint : Springer, , 2018 |
| Descrizione fisica | 1 online resource (353 pages) |
| Disciplina | 616.7 |
| Collana | Advances in Experimental Medicine and Biology |
| Soggetto topico |
Biomedical engineering
Orthopedics Computational intelligence Biomedical Engineering/Biotechnology Computational Intelligence |
| ISBN | 981-13-1396-2 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Chapter 1. Computer Aided Orthopaedic Surgery: Stateof-the-Art and Future Perspectives -- Chapter 2. Computer Aided Orthopaedic Surgery: incremental shift or paradigm change? -- Chapter 3. CAMISS concept and its clinical application -- Chapter 4. Surgical Navigation in Orthopaedics: Workflow and System Review -- Chapter 5. Multi-Object Model-based Multi-Atlas Segmentation Constrained Grid Cut for Automatic Segmentation of Lumbar Vertebrae from CT Images -- Chapter 6. Deep Learning-based Automatic Segmentation of the Proximal Femur from MR Images -- Chapter 7. Muscle Segmentation for Orthopaedic Interventions -- Chapter 8. 3X-Knee: A Novel Technology for 3D Pre-operative Planning and Post-operative Evaluation of TKA based on 2DX-rays -- Chapter 9. Atlas-based 3D Intensity Volume Reconstruction from 2D Long Leg Standing X-rays: Application to Hard and Soft Tissues in Lower Extremity -- Chapter 10. 3D Ultrasound for Orthopedic Interventions -- Chapter 11. A novel ultrasound-based lower extremity motion tracking system -- Chapter 12. Computer Assisted Planning, Simulation and Navigation System for Periacetabular Osteotomy -- Chapter 13. Biomechanical Optimization-based Planning of Periacetabular Osteotomy -- Chapter 14. Biomechanical Guidance System for Peri-Acetabular Osteotomy -- Chapter 15. GravityAssistedNavigationSystemforTotal HipArthroplasty -- Chapter 16. 3D Visualization and Augmented Reality for Orthopaedics -- Chapter 17. Intelligent HMI in orthopaedic navigation -- Chapter 18. Patient-Specific Surgical Guidance System for Intelligent Orthopaedics -- Chapter 19. Intelligent control for human-robot cooperation in orthopaedics surgery -- Chapter 20. Multilevel Fuzzy Control Based on Force Information in Robot-assisted Decompressive Laminectomy -- Chapter 21. Potentional risk of intelligent technologies in clinical orthopaedics -- Chapter 22. Clinical Application of Navigation in the Surgical Treatment of a Pelvic Ring Injury and Acetabular Fracture -- Chapter 23. Patient specific surgical guide for total hip arthroplasty -- Chapter 24. Computer navigation in orthopaedic tumour surgery -- Chapter 25. Sensor Based Soft Tissue Balancing in Total Knee Arthroplasty -- Chapter 26. Implant Orientation Measurement after THA Using the EOS X-Ray Image Acquisition System -- Chapter 27. 3D Printing in Spine Surgery. |
| Record Nr. | UNINA-9910298427803321 |
| Singapore : , : Springer Singapore : , : Imprint : Springer, , 2018 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Myocardial pathology segmentation combining multi-sequence cardiac magnetic resonance images : first challenge, MyoPS 2020, held in conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, Proceedings / / Xiahai Zhuang, Lei Li (edsitors)
| Myocardial pathology segmentation combining multi-sequence cardiac magnetic resonance images : first challenge, MyoPS 2020, held in conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, Proceedings / / Xiahai Zhuang, Lei Li (edsitors) |
| Edizione | [1st ed. 2020.] |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (VIII, 177 p. 93 illus., 79 illus. in color.) |
| Disciplina | 616.07540285 |
| Collana | Lecture notes in computer science |
| Soggetto topico | Diagnostic imaging - Data processing |
| ISBN | 3-030-65651-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Stacked BCDU-net with semantic CMR synthesis: application to Myocardial PathologySegmentation challenge -- EfficientSeg: A Simple but Efficient Solution to Myocardial Pathology Segmentation Challenge -- Two-stage Method for Segmentation of the Myocardial Scars and Edema on Multi-sequence Cardiac Magnetic Resonance -- Multi-Modality Pathology Segmentation Framework: Application to Cardiac Magnetic Resonance Images -- Myocardial Edema and Scar Segmentation using a Coarse-to-Fine Framework with Weighted Ensemble -- Exploring ensemble applications for multi-sequence myocardial pathology segmentation -- Max-Fusion U-Net for Multi-Modal Pathology Segmentation with Attention and Dynamic Resampling -- Fully automated deep learning based segmentation of normal, infarcted and edema regions from multiple cardiac MRI sequences -- CMS-UNet: Cardiac Multi-task Segmentation in MRI with a U-shaped Network -- Automatic Myocardial Scar Segmentation from Multi-Sequence Cardiac MRI using Fully Convolutional Densenet with Inception and Squeeze-Excitation Module -- Dual Attention U-net for Multi-Sequence Cardiac MR Images Segmentation -- Accurate Myocardial Pathology Segmentation with Residual U-Net -- Stacked and Parallel U-Nets with Multi-Output for Myocardial Pathology Segmentation -- Dual-path Feature Aggregation Network Combined Multi-layer Fusion for Myocardial Pathology Segmentation with Multi-sequence Cardiac MR -- Cascaded Framework with Complementary CMR Information for Myocardial Pathology Segmentation -- CMRadjustNet: Recognition and standardization of cardiac MRI orientation via multi-tasking learning and deep neural networks. |
| Record Nr. | UNINA-9910447248303321 |
| Cham, Switzerland : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Myocardial pathology segmentation combining multi-sequence cardiac magnetic resonance images : first challenge, MyoPS 2020, held in conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, Proceedings / / Xiahai Zhuang, Lei Li (edsitors)
| Myocardial pathology segmentation combining multi-sequence cardiac magnetic resonance images : first challenge, MyoPS 2020, held in conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, Proceedings / / Xiahai Zhuang, Lei Li (edsitors) |
| Edizione | [1st ed. 2020.] |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (VIII, 177 p. 93 illus., 79 illus. in color.) |
| Disciplina | 616.07540285 |
| Collana | Lecture notes in computer science |
| Soggetto topico | Diagnostic imaging - Data processing |
| ISBN | 3-030-65651-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Stacked BCDU-net with semantic CMR synthesis: application to Myocardial PathologySegmentation challenge -- EfficientSeg: A Simple but Efficient Solution to Myocardial Pathology Segmentation Challenge -- Two-stage Method for Segmentation of the Myocardial Scars and Edema on Multi-sequence Cardiac Magnetic Resonance -- Multi-Modality Pathology Segmentation Framework: Application to Cardiac Magnetic Resonance Images -- Myocardial Edema and Scar Segmentation using a Coarse-to-Fine Framework with Weighted Ensemble -- Exploring ensemble applications for multi-sequence myocardial pathology segmentation -- Max-Fusion U-Net for Multi-Modal Pathology Segmentation with Attention and Dynamic Resampling -- Fully automated deep learning based segmentation of normal, infarcted and edema regions from multiple cardiac MRI sequences -- CMS-UNet: Cardiac Multi-task Segmentation in MRI with a U-shaped Network -- Automatic Myocardial Scar Segmentation from Multi-Sequence Cardiac MRI using Fully Convolutional Densenet with Inception and Squeeze-Excitation Module -- Dual Attention U-net for Multi-Sequence Cardiac MR Images Segmentation -- Accurate Myocardial Pathology Segmentation with Residual U-Net -- Stacked and Parallel U-Nets with Multi-Output for Myocardial Pathology Segmentation -- Dual-path Feature Aggregation Network Combined Multi-layer Fusion for Myocardial Pathology Segmentation with Multi-sequence Cardiac MR -- Cascaded Framework with Complementary CMR Information for Myocardial Pathology Segmentation -- CMRadjustNet: Recognition and standardization of cardiac MRI orientation via multi-tasking learning and deep neural networks. |
| Record Nr. | UNISA-996418294003316 |
| Cham, Switzerland : , : Springer, , [2021] | ||
| 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 |
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. | 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. Multi-Sequence CMR Segmentation, CRT-EPiggy and LV Full Quantification Challenges [[electronic resource] ] : 10th International Workshop, STACOM 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13, 2019, Revised Selected Papers / / edited by Mihaela Pop, Maxime Sermesant, Oscar Camara, Xiahai Zhuang, Shuo Li, Alistair Young, Tommaso Mansi, Avan Suinesiaputra
| Statistical Atlases and Computational Models of the Heart. Multi-Sequence CMR Segmentation, CRT-EPiggy and LV Full Quantification Challenges [[electronic resource] ] : 10th International Workshop, STACOM 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13, 2019, Revised Selected Papers / / edited by Mihaela Pop, Maxime Sermesant, Oscar Camara, Xiahai Zhuang, Shuo Li, Alistair Young, Tommaso Mansi, Avan Suinesiaputra |
| Edizione | [1st ed. 2020.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 |
| Descrizione fisica | 1 online resource (XV, 417 p. 200 illus., 168 illus. in color.) |
| Disciplina | 611.12 |
| Collana | Image Processing, Computer Vision, Pattern Recognition, and Graphics |
| Soggetto topico |
Optical data processing
Artificial intelligence Pattern recognition Application software Image Processing and Computer Vision Artificial Intelligence Pattern Recognition Computer Applications |
| ISBN | 3-030-39074-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Regular Papers -- Multi-Sequence CMR Segmentation Challenge -- CRT-EPiggy Challenge -- LV Full Quantification Challenge. |
| Record Nr. | UNINA-9910373928703321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Statistical Atlases and Computational Models of the Heart. Multi-Sequence CMR Segmentation, CRT-EPiggy and LV Full Quantification Challenges [[electronic resource] ] : 10th International Workshop, STACOM 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13, 2019, Revised Selected Papers / / edited by Mihaela Pop, Maxime Sermesant, Oscar Camara, Xiahai Zhuang, Shuo Li, Alistair Young, Tommaso Mansi, Avan Suinesiaputra
| Statistical Atlases and Computational Models of the Heart. Multi-Sequence CMR Segmentation, CRT-EPiggy and LV Full Quantification Challenges [[electronic resource] ] : 10th International Workshop, STACOM 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13, 2019, Revised Selected Papers / / edited by Mihaela Pop, Maxime Sermesant, Oscar Camara, Xiahai Zhuang, Shuo Li, Alistair Young, Tommaso Mansi, Avan Suinesiaputra |
| Edizione | [1st ed. 2020.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 |
| Descrizione fisica | 1 online resource (XV, 417 p. 200 illus., 168 illus. in color.) |
| Disciplina | 611.12 |
| Collana | Image Processing, Computer Vision, Pattern Recognition, and Graphics |
| Soggetto topico |
Optical data processing
Artificial intelligence Pattern recognition Application software Image Processing and Computer Vision Artificial Intelligence Pattern Recognition Computer Applications |
| ISBN | 3-030-39074-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Regular Papers -- Multi-Sequence CMR Segmentation Challenge -- CRT-EPiggy Challenge -- LV Full Quantification Challenge. |
| Record Nr. | UNISA-996418223103316 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||