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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 / / edited by Guang Yang, Angelica Aviles-Rivero, Michael Roberts, Carola-Bibiane Schönlieb
| Medical Image Understanding and Analysis : 26th Annual Conference, MIUA 2022, Cambridge, UK, July 27–29, 2022, Proceedings / / edited by Guang Yang, Angelica Aviles-Rivero, Michael Roberts, Carola-Bibiane Schönlieb |
| Edizione | [1st ed. 2022.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022 |
| Descrizione fisica | 1 online resource (913 pages) |
| Disciplina | 616.0754 |
| Collana | Lecture Notes in Computer Science |
| Soggetto topico |
Computer vision
Artificial intelligence Computers Application software Computer Vision Artificial Intelligence Computing Milieux Computer and Information Systems Applications |
| ISBN |
9783031120534
3031120531 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910585782803321 |
| Cham : , : Springer International Publishing : , : Imprint : 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 |
9780253047212
0253047218 |
| 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-9910963219503321 |
| Bloomington, Indiana : , : Indiana University Press, , [2020] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Risks, Resilience and Interdependency : Developing Countries in the Age of Uncertainties / / edited by Guang Yang, Jing Zhang, Xinghan Xiong, Lanyu Liu
| Risks, Resilience and Interdependency : Developing Countries in the Age of Uncertainties / / edited by Guang Yang, Jing Zhang, Xinghan Xiong, Lanyu Liu |
| Edizione | [1st ed. 2025.] |
| Pubbl/distr/stampa | Singapore : , : Springer Nature Singapore : , : Imprint : Palgrave Macmillan, , 2025 |
| Descrizione fisica | 1 online resource (XII, 289 p. 11 illus., 9 illus. in color.) |
| Disciplina | 306.091 |
| Soggetto topico |
Ethnology
Culture Religion and politics Peace Age distribution (Demography) Regional Cultural Studies Politics and Religion Peace and Conflict Studies Aging Population |
| ISBN | 981-9671-67-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Part I. Risks -- 1. On the Profound Impact of Nigeria’s Oil Boom on Politics and Economy (1973-1979) -- 2. A political economy analysis of modern state development in Southeast Asia -- 3. Climate change adaptation implementation and governance in Thailand: Increased resilience and potential conflicts -- Part II. Resilience -- 4. Poverty and education in times of COVID-19: The implementation of education programs in southeastern Chiapas, Mexico -- 5. Eastern Orthodox churches and nation-state building in Eurasia: a case study based on fieldwork in Ukraine and Belarus -- 6. Black Feminists’ Resistance and the Intersectional Agenda in the #MustFall Movements in South Africa -- 7. Iranian Political Culture and Social Movements (1979-2022) -- Part III. Interdependency -- 8. Farghānah between the Seventh and Ninth Centuries CE as a Military Vassal of the Big Powers -- 9. The Multiple Layers of Morocco’s Normalization with Israel -- 10. Corporate lobbying and its influence on public policy in Brazil. |
| Record Nr. | UNINA-9911016079803321 |
| Singapore : , : Springer Nature Singapore : , : Imprint : Palgrave Macmillan, , 2025 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Spatiotemporal Dynamics of Meteorological and Agricultural Drought in China / / by Yi Li, Faliang Yuan, Qiang Zhou, Fenggui Liu, Asim Biswas, Guang Yang, Zhihao Liao
| Spatiotemporal Dynamics of Meteorological and Agricultural Drought in China / / by Yi Li, Faliang Yuan, Qiang Zhou, Fenggui Liu, Asim Biswas, Guang Yang, Zhihao Liao |
| Autore | Li Yi |
| Edizione | [1st ed. 2024.] |
| Pubbl/distr/stampa | Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024 |
| Descrizione fisica | 1 online resource (250 pages) |
| Disciplina |
551
363.34 |
| Altri autori (Persone) |
YuanFaliang
ZhouQiang LiuFenggui BiswasAsim YangGuang LiaoZhihao |
| Soggetto topico |
Natural disasters
Climatology Water Hydrology Forestry Atmospheric science Natural Hazards Climate Sciences Atmospheric Science |
| ISBN |
9789819742141
9789819742134 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Introduction -- Spatiotemporal Analysis and Impacts Assessment of Agricultural Drought in China -- Materials and Methodology -- Spatial and Temporal Variations of SPI and SSI -- Multivariate Frequency Analysis of Drought Events Using Drought Indices and Copula Functions in China -- Study Area and Data Source -- Drought Indices and Univariate Analysis -- Frequency Analysis Using 2-Variate Archimedean Copula -- Frequency Analysis Using 3-Variate Archimedean Copula -- Frequency Analysis Using Four-Variate Archimedean Copula -- Spatiotemporal Analysis and Impacts Assessment of Agricultural Drought in China -- Study Area and Data -- Drought Evolutions Over Different Land Cover Types -- The Response of Vegetation Phenology and Productivity to Extreme Climatic -- Drought Indices Performance for Predicting Agriculture Drought -- The Effects of Agricultural Drought on Crop Production -- Conclusions. |
| Record Nr. | UNINA-9910878056103321 |
Li Yi
|
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| Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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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 | ||
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Trustworthy AI in Cancer Imaging Research / / edited by Ioanna Chouvarda, Sara Colantonio, Gianna Tsakou, Guang Yang
| Trustworthy AI in Cancer Imaging Research / / edited by Ioanna Chouvarda, Sara Colantonio, Gianna Tsakou, Guang Yang |
| Autore | Chouvarda Ioanna |
| Edizione | [1st ed. 2025.] |
| Pubbl/distr/stampa | Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2025 |
| Descrizione fisica | 1 online resource (371 pages) |
| Disciplina | 610.28 |
| Altri autori (Persone) |
ColantonioSara
TsakouGianna YangGuang |
| Soggetto topico |
Biomedical engineering
Artificial intelligence Cancer - Imaging Machine learning Cancer Medical screening Biomedical Engineering and Bioengineering Artificial Intelligence Cancer Imaging Machine Learning Cancer Screening |
| ISBN | 3-031-89963-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Section 1. Overall Considerations -- 1. Generating the FUTURE AI. describing the process for reaching consensus on the FUTURE-AI recommendations and how these contribute/relate to trustworthy AI (make some kind of correspondence to the trustworthy AI principles of the EC and others) Martijn Starmans, Richard Osuala, Oliver Díaz, Karim Lekadir, and contributors -- 2. The Clinical Viewpoint / Considerations for Clinical Impact of AI in Oncologic Imaging Luis Marti-Bonmati (clinical Ai4HI WG), and contributors from all AI4HI -- 3. Socio-ethical and legal implications of Trustworthy AI – the AI4HI ELSI Mónica Cano Abadía(BBMRI-ERIC, EuCanImage), Ricard Martínez (Primage and Chaimeleon) and Mario Aznar +ProCancerI legal colleague , and provisionally Magda Kogut (INCISIVE) -- Section 2. Preparing for trustworthy AI: The Data and Metadata for quality, transparency and traceability -- 4. Data harmonization and challenges towards generation of repositories: sharing practices and approaches- ( Include Data de-identification / Include Data annotation and segmentation / compare commonalities and differences in the projects/ Data quality) Leonor Cerdá (Primage), Oliver Diaz( EUCANIMAGE), Guang Yang (Imperial, Chaimeleon), Ana Jimenez -Quibim /UNS/ Alexandra Kosvyra [AUTH] , Ch Kondylakis FORTH, provisionally co-authors from CERTH -- 5. Standardising Data and Metadata (this will include Data models/AI metadata / AI Passport /Transparency of Data, Models, and Decisions) Ch Kondylakis (FORTH), S Colantonio–(CNR) Gianna Tsakou (MAG) + Alexandra Kosvyra [AUTH] + provisionally inputs from ( Ticsalud/ED/ Medexprim/) Pedro Mallol (Chaimeleon) -- 6. Generatic synthetic data in Cancer Research Yang (Imperial College)/ Leonor Cerdá, Richard Osuala , provisionally Karim Lekadir / Adrián Galiana (Primage) -- Section 3. Implementing trustworthy AI: The Algorithms and DSS -- 7. Architectures and platforms for trustworthy AI: cloud technologies and federated approaches (this includes The privacy preserving methods / challenges with federated learning , Cloud technologies for supporting centralized trustworthy AI training ) Alberto Gutierrez (BSC) and Chrysostomos Symvoulidis (INCISIVE)/ Martijn Pieter Anton Starmans EUCANIMAGE / Ignacio Blanquer (CHAIMELEON ) -- 8. AI robustness, generalizability and explainability Sara Colantonio, Alberto Gutierrez-Torre [BSC], And inputs from Nikos Papanikolaou. Ysroel Mirsky (Israel, Chaimeleon), Henry Woodruff (Maastrich, Chaimeleon), D Dominguez Herrera (Ticsalud) / D Fotopoulos (AUTH) / Manikis/KMarias (FORTH) -- 9. AI Models in cancer diagnosis and prognosis Leonor Cerdá (Chaimeleon), D Filos and I Chouvarda (AUTH), Turukalo, Tatjana (UNS) and contributors from all projects (including ICCS fromINCISIVE project) -- Section 4. Validating trustworthy AI: The Validation and User perspective -- 10. Doing Technical validation for real. Experiences from a multisite effort Inputs from the AI4HI WG survey work and relation to project work / AUTH and UNS can contribute the INCISIVE prevalidation method and efforts here (Olga Tsave/Chouvarda – AUTH) and (Tatjana Turukalo and UNS team), with contributors from all projects -- 11. Clinical Validation – (including material from previous AI4HI paper, User perspective/feedback and lessons learnt / experience difficulties from all projects) Luis Bonmati, Katrine Riklund , Shereen Nabhani-Gebara, Lithin Zacharias, Maciej Bobowicz, -- 12. Real-life deployment of AI services: practical implications (focusing on real-life deployment of AI services: practical implications, patents, fast-track for clinical usefulness, Towards certification) ( Ana Blanco, Ana Jimenez, Fuensanta Bellvis , Quibim) + legal partners from all teams on AI related requirements. |
| Record Nr. | UNINA-9911015865803321 |
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Chouvarda Ioanna
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| Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2025 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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