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Medical image understanding and analysis : 26th annual conference, MIUA 2022, Cambridge, UK, July 27-29, 2022, proceedings / / Guang Yang [and three others] editors
| Medical image understanding and analysis : 26th annual conference, MIUA 2022, Cambridge, UK, July 27-29, 2022, proceedings / / Guang Yang [and three others] editors |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (913 pages) |
| Disciplina | 616.0754 |
| Collana | Lecture notes in computer science |
| Soggetto topico |
Diagnostic imaging
Diagnostic imaging - Data processing |
| ISBN | 3-031-12053-1 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISA-996483159903316 |
| Cham, Switzerland : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Medical image understanding and analysis : 26th annual conference, MIUA 2022, Cambridge, UK, July 27-29, 2022, proceedings / / Guang Yang [and three others] editors
| Medical image understanding and analysis : 26th annual conference, MIUA 2022, Cambridge, UK, July 27-29, 2022, proceedings / / Guang Yang [and three others] editors |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (913 pages) |
| Disciplina | 616.0754 |
| Collana | Lecture notes in computer science |
| Soggetto topico |
Diagnostic imaging
Diagnostic imaging - Data processing |
| ISBN | 3-031-12053-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910585782803321 |
| Cham, Switzerland : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Paths in Heidegger's later thought / edited by Günter Figal, Diego D'Angelo, Tobias Keiling, and Guang Yang
| Paths in Heidegger's later thought / edited by Günter Figal, Diego D'Angelo, Tobias Keiling, and Guang Yang |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Bloomington, Indiana : , : Indiana University Press, , [2020] |
| Descrizione fisica | 1 online resource (314 pages) |
| Disciplina | 193 |
| Collana | Studies in continental thought |
| ISBN | 0-253-04721-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | ; I: Language, logos, and rhythm. "The House of Being" : poetry, language, place / Jeff Malpas -- Heidegger and Trakl : language speaks in the poet's poem / Markus Wild -- Towards a hermeneutic interpretation of greeting and destiny in Heidegger's thinking / Diego D'Angelo -- Later Heidegger's naturalism / Tristan Moyle -- ; II: Heidegger's physics. Why is Heidegger interested in physis? / Thomas Buchheim -- Being as physis : the belonging together of movement and rest in the Greek experience of physis / Guang Yang -- The end of philosophy and the experience of unending physis / Claudia Baracchi -- Thinking at the first beginning : Heidegger's interpretation of the early Greek physis / Damir Barbarić -- ; III: Phenomenology, the thing, and the fourfold. Tautóphasis : Heidegger and Parmenides / Günter Figal -- Radical contextuality in Heidegger's postmetaphysics : the singularity of being and the fourfold / Jussi Backman -- The phenomenon of shining / Nikola Mirković -- A brief history of things : Heidegger and the tradition / Andrew J. Mitchell -- ; IV: Ground, non-ground, and abyss. Heidegger, Leibniz, and the abyss of reason / Hans Ruin -- Ground, abyss, and the primordial ground : Heidegger in the wake of Schelling / Sylvaine Gourdain -- Erklüftung : Heidegger's thinking of projection in Contributions to Philosophy / Tobias Keiling. |
| Record Nr. | UNINA-9910793913603321 |
| Bloomington, Indiana : , : Indiana University Press, , [2020] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Paths in Heidegger's later thought / edited by Günter Figal, Diego D'Angelo, Tobias Keiling, and Guang Yang
| Paths in Heidegger's later thought / edited by Günter Figal, Diego D'Angelo, Tobias Keiling, and Guang Yang |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Bloomington, Indiana : , : Indiana University Press, , [2020] |
| Descrizione fisica | 1 online resource (314 pages) |
| Disciplina | 193 |
| Collana | Studies in continental thought |
| ISBN | 0-253-04721-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | ; I: Language, logos, and rhythm. "The House of Being" : poetry, language, place / Jeff Malpas -- Heidegger and Trakl : language speaks in the poet's poem / Markus Wild -- Towards a hermeneutic interpretation of greeting and destiny in Heidegger's thinking / Diego D'Angelo -- Later Heidegger's naturalism / Tristan Moyle -- ; II: Heidegger's physics. Why is Heidegger interested in physis? / Thomas Buchheim -- Being as physis : the belonging together of movement and rest in the Greek experience of physis / Guang Yang -- The end of philosophy and the experience of unending physis / Claudia Baracchi -- Thinking at the first beginning : Heidegger's interpretation of the early Greek physis / Damir Barbarić -- ; III: Phenomenology, the thing, and the fourfold. Tautóphasis : Heidegger and Parmenides / Günter Figal -- Radical contextuality in Heidegger's postmetaphysics : the singularity of being and the fourfold / Jussi Backman -- The phenomenon of shining / Nikola Mirković -- A brief history of things : Heidegger and the tradition / Andrew J. Mitchell -- ; IV: Ground, non-ground, and abyss. Heidegger, Leibniz, and the abyss of reason / Hans Ruin -- Ground, abyss, and the primordial ground : Heidegger in the wake of Schelling / Sylvaine Gourdain -- Erklüftung : Heidegger's thinking of projection in Contributions to Philosophy / Tobias Keiling. |
| Record Nr. | UNINA-9910811425803321 |
| Bloomington, Indiana : , : Indiana University Press, , [2020] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
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 | ||
| ||