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Statistical atlases and computational models of the heart : first international workshop, STACOM 2010 and cardiac electrophysical simulation challenge, CESC 2010, held in conjunction with MICCAI 2010, Beijing, China, September 20, 2010 : proceedings / / Oscar Camara ... [et al.] (eds.)
| Statistical atlases and computational models of the heart : first international workshop, STACOM 2010 and cardiac electrophysical simulation challenge, CESC 2010, held in conjunction with MICCAI 2010, Beijing, China, September 20, 2010 : proceedings / / Oscar Camara ... [et al.] (eds.) |
| Edizione | [1st ed. 2010.] |
| Pubbl/distr/stampa | Berlin, : Springer, 2010 |
| Descrizione fisica | 1 online resource (XII, 292 p. 140 illus.) |
| Disciplina | 006 |
| Altri autori (Persone) | CamaraOscar (Oscar Camara Rey) |
| Collana |
Lecture notes in computer science
LNCS sublibrary. SL 6, Image processing, computer vision, pattern recognition, and graphics |
| Soggetto topico |
Heart - Imaging - Atlases
Imaging systems in medicine Three-dimensional imaging in medicine Heart - Computer simulation |
| ISBN |
1-280-38911-7
9786613567031 3-642-15835-8 |
| Classificazione | 610 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Keynote Presentations -- Atlas Construction and Image Analysis Using Statistical Cardiac Models -- Patient-Specific Modeling of the Heart: Applications to Cardiovascular Disease Management -- The Generation of Patient-Specific Heart Models for Diagnosis and Interventions -- Methods and Infrastructure for Atlas Construction -- The Cardiac Atlas Project: Rationale, Design and Procedures -- The Cardiac Atlas Project: Preliminary Description of Heart Shape in Patients with Myocardial Infarction -- The Cardiac Atlas Project: Development of a Framework Integrating Cardiac Images and Models -- Atlas-Based Quantification of Myocardial Motion Abnormalities: Added-value for the Understanding of CRT Outcome? -- Towards High-Resolution Cardiac Atlases: Ventricular Anatomy Descriptors for a Standardized Reference Frame -- Structure and Flow -- Robust Atlas-Based Segmentation of Highly Variable Anatomy: Left Atrium Segmentation -- Atlas-Based Reduced Models of Blood Flows for Fast Patient-Specific Simulations -- Image and Physiological Data Fusion for Guidance and Modelling of Cardiac Resynchronization Therapy Procedures -- A Multi-method Approach towards Understanding the Pathophysiology of Aortic Dissections – The Complementary Role of In-Silico, In-Vitro and In-Vivo Information -- Endowing Canonical Geometries to Cardiac Structures -- Automatic Segmentation of Left Atrial Geometry from Contrast-Enhanced Magnetic Resonance Images Using a Probabilistic Atlas -- Interactive Cardiac Image Analysis for Biventricular Function of the Human Heart -- Cardiac Motion Estimation Using a ProActive Deformable Model: Evaluation and Sensitivity Analysis -- Investigating Heart Failure Using Ventricular Imaging and Modelling -- Incorporating Low-Level Constraints for the Retrieval of Personalised Heart Models from Dynamic MRI -- Volumetric Myocardial Mechanics from 3D+t Ultrasound Data with Multi-model Tracking -- Mechanics and Motion -- Cardiac Active Contraction Parameters Estimated from Magnetic Resonance Imaging -- Electrophysiology and Electrical Activation -- Recovering Cardiac Electrical Activity from Medical Image Sequence: A Model-Based Approach -- Non-invasive Activation Times Estimation Using 3D Echocardiography -- Modeling Drug Effects on Personalized 3D Models of the Heart: A Simulation Study -- How Much Geometrical Detail Do We Need in Cardiac Electrophysiological Imaging? A Generic Heart-Torso Representation for Fast Subject-Specific Customization -- Influence of Geometric Variations on LV Activation Times: A Study on an Atlas-Based Virtual Population -- Computational Electrophysiological Simulation Challenge (CESC 2010) -- Generic Conduction Parameters for Predicting Activation Waves in Customised Cardiac Electrophysiology Models -- A Statistical Physiological-Model-Constrained Framework for Computational Imaging of Subject-Specific Volumetric Cardiac Electrophysiology Using Optical Imaging and MRI Data -- Estimation of Reaction, Diffusion and Restitution Parameters for a 3D Myocardial Model Using Optical Mapping and MRI -- Personalization of Fast Conduction Purkinje System in Eikonal-Based Electrophysiological Models with Optical Mapping Data. |
| Altri titoli varianti |
STACOM 2010
CESC 2010 |
| Record Nr. | UNINA-9910484999103321 |
| Berlin, : Springer, 2010 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Statistical atlases and computational models of the heart : Regular and CMRxMotion Challenge papers : 13th International Workshop, STACOM 2022, held in conjunction with MICCAI 2022, Singapore, September 18, 2022, revised selected papers / / edited by Oscar Camara [and six others]
| Statistical atlases and computational models of the heart : Regular and CMRxMotion Challenge papers : 13th International Workshop, STACOM 2022, held in conjunction with MICCAI 2022, Singapore, September 18, 2022, revised selected papers / / edited by Oscar Camara [and six others] |
| Edizione | [1st ed. 2022.] |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (527 pages) |
| Disciplina | 658 |
| Collana | Lecture Notes in Computer Science |
| Soggetto topico | Heart - Imaging |
| ISBN | 3-031-23443-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Generative Modelling of the Ageing Heart with Cross-Sectional Imaging and Clinical Data -- Learning correspondences of cardiac motion using biomechanics-informed modeling -- Multi-modal Latent-space Self-alignment for Super-resolution Cardiac MR Segmentation -- Towards real-time optimization of left atrial appendage occlusion device placement through physics-informed neural networks -- Haemodynamic changes in the fetal circulation after connection to an artificial placenta: a computational modelling study -- Personalized Fast Electrophysiology Simulations to Evaluate Arrhythmogenicity of Ventricular Slow Conduction Channels -- Self-supervised motion descriptor for cardiac phase detection in 4D CMR based on discrete vector field estimations -- Going Off-Grid: Continuous Implicit Neural Representations for 3D Vascular Modeling -- Comparison of Semi- and Un-supervised Domain Adaptation Methods for Whole-Heart Segmentation -- Automated Quality Controlled Analysis of 2D Phase Contrast Cardiovascular Magnetic Resonance Imaging -- An Atlas-Based Analysis of Biventricular Mechanics in Tetralogy of Fallot -- Review of data types and model dimensionality for cardiac DTI SMS-related artefact removal -- Improving Echocardiography Segmentation by Polar Transformation -- Spatiotemporal Cardiac Statistical Shape Modeling: A Data-Driven Approach -- Interpretable Prediction of Post-Infarct Ventricular Arrhythmia using Graph Convolutional Network -- Unsupervised Echocardiography Registration through Patch-based MLPs and Transformers -- Sensitivity analysis of left atrial wall modeling approaches and inlet/outlet boundary conditions in fluid simulations to predict thrombus formation -- APHYN-EP: Physics-based deep learning framework to learn and forecast cardiac electrophysiology dynamics -- Unsupervised machine-learning exploration of morphological and haemodynamic indices to predict thrombus formation at the left atrial appendage -- Geometrical deep learning for the estimation of residence time in the left atria -- Explainable Electrocardiogram Analysis with Wave Decomposition: Application to Myocardial Infarction Detection -- A systematic study of race and sex bias in CNN-based cardiac MR segmentation -- Mesh U-Nets for 3D Cardiac Deformation Modeling -- Skeletal model-based analysis of the tricuspid valve in hypoplastic left heart syndrome -- Simplifying Disease Staging Models into a Single Anatomical Axis – A Case Study of Aortic Coarctation In-utero -- Point2Mesh-Net: Combining Point Cloud and Mesh-Based Deep Learning for Cardiac Shape Reconstruction -- Post-Infarction Risk Prediction with Mesh Classification Networks -- Statistical Shape Modeling of Biventricular Anatomy with Shared Boundaries -- Computerized Analysis of the Human Heart to Guide Targeted Treatment of Atrial Fibrillation -- 3D Mitral Valve Surface Reconstruction from 3D TEE via Graph Neural Networks -- Efficient MRI Reconstruction with Reinforcement Learning for Automatic Acquisition Stopping -- Unsupervised Cardiac Segmentation Utilizing Synthesized Images from Anatomical Labels -- PAT-CNN: Automatic Segmentation and Quantification of Pericardial Adipose Tissue from T2-Weighted Cardiac Magnetic Resonance Images -- Deep Computational Model for the Inference of Ventricular Activation Properties -- Semi-Supervised Domain Generalization for Cardiac Magnetic Resonance Image Segmentation with High Quality Pseudo Labels -- Cardiac Segmentation using Transfer Learning under Respiratory Motion Artifacts -- Deep Learning Based Classification and Segmentation for Cardiac Magnetic Resonance Imaging with Respiratory Motion Artifacts -- Multi-task Swin Transformer for Motion Artifacts Classification and Cardiac Magnetic Resonance Image Segmentation -- Automatic Quality Assessment of Cardiac MR Images with Motion Artefacts using Multi-task Learning and K-Space Motion Artefact Augmentation -- Motion-related Artefact Classification Using Patch-based Ensemble and Transfer Learning in Cardiac MRI -- Automatic Image Quality Assessment and Cardiac Segmentation Based on CMR Images -- Detecting respiratory motion artefacts for cardiovascular MRIs to ensure high-quality segmentation -- 3D MRI cardiac segmentation under respiratory motion artifacts -- Cardiac MR Image Segmentation and Quality Control in the Presence of Respiratory Motion Artifact using Simulated Data -- Combination Special Data Augmentation and Sampling Inspection Network for Cardiac Magnetic Resonance Imaging Quality Classification -- Automatic Cardiac Magnetic Resonance Respiratory Motions Assessment and Segmentation -- Robust Cardiac MRI Segmentation with Data-Centric Models to Improve Performance via Intensive Pre-training and Augmentation -- A deep learning-based fully automatic framework for motion-existing cine image quality control and quantitative analysis. |
| Record Nr. | UNINA-9910647383503321 |
| Cham, Switzerland : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Statistical atlases and computational models of the heart : Regular and CMRxMotion Challenge papers : 13th International Workshop, STACOM 2022, held in conjunction with MICCAI 2022, Singapore, September 18, 2022, revised selected papers / / edited by Oscar Camara [and six others]
| Statistical atlases and computational models of the heart : Regular and CMRxMotion Challenge papers : 13th International Workshop, STACOM 2022, held in conjunction with MICCAI 2022, Singapore, September 18, 2022, revised selected papers / / edited by Oscar Camara [and six others] |
| Edizione | [1st ed. 2022.] |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (527 pages) |
| Disciplina | 658 |
| Collana | Lecture Notes in Computer Science |
| Soggetto topico | Heart - Imaging |
| ISBN | 3-031-23443-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Generative Modelling of the Ageing Heart with Cross-Sectional Imaging and Clinical Data -- Learning correspondences of cardiac motion using biomechanics-informed modeling -- Multi-modal Latent-space Self-alignment for Super-resolution Cardiac MR Segmentation -- Towards real-time optimization of left atrial appendage occlusion device placement through physics-informed neural networks -- Haemodynamic changes in the fetal circulation after connection to an artificial placenta: a computational modelling study -- Personalized Fast Electrophysiology Simulations to Evaluate Arrhythmogenicity of Ventricular Slow Conduction Channels -- Self-supervised motion descriptor for cardiac phase detection in 4D CMR based on discrete vector field estimations -- Going Off-Grid: Continuous Implicit Neural Representations for 3D Vascular Modeling -- Comparison of Semi- and Un-supervised Domain Adaptation Methods for Whole-Heart Segmentation -- Automated Quality Controlled Analysis of 2D Phase Contrast Cardiovascular Magnetic Resonance Imaging -- An Atlas-Based Analysis of Biventricular Mechanics in Tetralogy of Fallot -- Review of data types and model dimensionality for cardiac DTI SMS-related artefact removal -- Improving Echocardiography Segmentation by Polar Transformation -- Spatiotemporal Cardiac Statistical Shape Modeling: A Data-Driven Approach -- Interpretable Prediction of Post-Infarct Ventricular Arrhythmia using Graph Convolutional Network -- Unsupervised Echocardiography Registration through Patch-based MLPs and Transformers -- Sensitivity analysis of left atrial wall modeling approaches and inlet/outlet boundary conditions in fluid simulations to predict thrombus formation -- APHYN-EP: Physics-based deep learning framework to learn and forecast cardiac electrophysiology dynamics -- Unsupervised machine-learning exploration of morphological and haemodynamic indices to predict thrombus formation at the left atrial appendage -- Geometrical deep learning for the estimation of residence time in the left atria -- Explainable Electrocardiogram Analysis with Wave Decomposition: Application to Myocardial Infarction Detection -- A systematic study of race and sex bias in CNN-based cardiac MR segmentation -- Mesh U-Nets for 3D Cardiac Deformation Modeling -- Skeletal model-based analysis of the tricuspid valve in hypoplastic left heart syndrome -- Simplifying Disease Staging Models into a Single Anatomical Axis – A Case Study of Aortic Coarctation In-utero -- Point2Mesh-Net: Combining Point Cloud and Mesh-Based Deep Learning for Cardiac Shape Reconstruction -- Post-Infarction Risk Prediction with Mesh Classification Networks -- Statistical Shape Modeling of Biventricular Anatomy with Shared Boundaries -- Computerized Analysis of the Human Heart to Guide Targeted Treatment of Atrial Fibrillation -- 3D Mitral Valve Surface Reconstruction from 3D TEE via Graph Neural Networks -- Efficient MRI Reconstruction with Reinforcement Learning for Automatic Acquisition Stopping -- Unsupervised Cardiac Segmentation Utilizing Synthesized Images from Anatomical Labels -- PAT-CNN: Automatic Segmentation and Quantification of Pericardial Adipose Tissue from T2-Weighted Cardiac Magnetic Resonance Images -- Deep Computational Model for the Inference of Ventricular Activation Properties -- Semi-Supervised Domain Generalization for Cardiac Magnetic Resonance Image Segmentation with High Quality Pseudo Labels -- Cardiac Segmentation using Transfer Learning under Respiratory Motion Artifacts -- Deep Learning Based Classification and Segmentation for Cardiac Magnetic Resonance Imaging with Respiratory Motion Artifacts -- Multi-task Swin Transformer for Motion Artifacts Classification and Cardiac Magnetic Resonance Image Segmentation -- Automatic Quality Assessment of Cardiac MR Images with Motion Artefacts using Multi-task Learning and K-Space Motion Artefact Augmentation -- Motion-related Artefact Classification Using Patch-based Ensemble and Transfer Learning in Cardiac MRI -- Automatic Image Quality Assessment and Cardiac Segmentation Based on CMR Images -- Detecting respiratory motion artefacts for cardiovascular MRIs to ensure high-quality segmentation -- 3D MRI cardiac segmentation under respiratory motion artifacts -- Cardiac MR Image Segmentation and Quality Control in the Presence of Respiratory Motion Artifact using Simulated Data -- Combination Special Data Augmentation and Sampling Inspection Network for Cardiac Magnetic Resonance Imaging Quality Classification -- Automatic Cardiac Magnetic Resonance Respiratory Motions Assessment and Segmentation -- Robust Cardiac MRI Segmentation with Data-Centric Models to Improve Performance via Intensive Pre-training and Augmentation -- A deep learning-based fully automatic framework for motion-existing cine image quality control and quantitative analysis. |
| Record Nr. | UNISA-996508669803316 |
| Cham, Switzerland : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||