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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 | ||
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Statistical Atlases and Computational Models of the Heart. Multi-Sequence CMR Segmentation, CRT-EPiggy and LV Full Quantification Challenges : 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 : 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. Regular and CMRxRecon Challenge Papers [[electronic resource] ] : 14th International Workshop, STACOM 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 12, 2023, Revised Selected Papers / / edited by Oscar Camara, Esther Puyol-Antón, Maxime Sermesant, Avan Suinesiaputra, Qian Tao, Chengyan Wang, Alistair Young
| Statistical Atlases and Computational Models of the Heart. Regular and CMRxRecon Challenge Papers [[electronic resource] ] : 14th International Workshop, STACOM 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 12, 2023, Revised Selected Papers / / edited by Oscar Camara, Esther Puyol-Antón, Maxime Sermesant, Avan Suinesiaputra, Qian Tao, Chengyan Wang, Alistair Young |
| Autore | Camara Oscar |
| Edizione | [1st ed. 2024.] |
| Pubbl/distr/stampa | Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2024 |
| Descrizione fisica | 1 online resource (507 pages) |
| Disciplina | 006.37 |
| Altri autori (Persone) |
Puyol-AntónEsther
SermesantMaxime SuinesiaputraAvan TaoQian WangChengyan YoungAlistair |
| Collana | Lecture Notes in Computer Science |
| Soggetto topico |
Computer vision
Computer science - Mathematics Mathematical statistics Machine learning Computer engineering Computer networks Social sciences - Data processing Computer Vision Probability and Statistics in Computer Science Machine Learning Computer Engineering and Networks Computer Application in Social and Behavioral Sciences |
| ISBN | 3-031-52448-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | CardiacSeg: Customized Pre-Training Volumetric Transformer with Scaling Pyramid for 3D Cardiac Segmentation -- Voxel2Hemodynamics: An End-to-end Deep Learning Method for Predicting Coronary Artery Hemodynamics -- Deep Learning for Automatic Strain Quantification in Arrhythmogenic Right Ventricular Cardiomyopathy -- Patient Stratification Based on Fast Simulation of Cardiac Electrophysiology on Digital Twins -- Deep Conditional Shape Models for 3D cardiac image segmentation -- Global Sensitivity Analysis of Thrombus Formation in the Left Atrial Appendage of Atrial Fibrillation Patients -- Sparse annotation strategies for segmentation of short axis cardiac MRI -- Contrast-Agnostic Groupwise Registration by Robust PCA for Quantitative Cardiac MRI -- FM-Net: A Fully Automatic Deep Learning Pipeline for Epicardial Adipose Tissue Segmentation -- Automated quality-controlled left heart segmentation from 2D echocardiography -- Impact of hypertension on left ventricular pressure-strain loop characteristics and myocardial work -- Automated segmentation of the right ventricle from 3D echocardiography using labels from cardiac magnetic resonance imaging -- Neural Implicit Functions for 3D Shape Reconstruction from standard Cardiovascular Magnetic Resonance views -- Deep Learning-based Pulmonary Artery Surface Mesh Generation -- Impact of catheter orientation on cardiac radiofrequency ablation -- Generating Virtual Populations of 3D Cardiac Anatomies with Snowflake-Net -- Effects of Fibrotic Border Zone on Drivers for Atrial Fibrillation: An In-Silico Mechanistic Investigation -- Exploring the relationship between pulmonary artery shape and pressure in Pulmonary Hypertension: A statistical shape analysis study. -- Type and Shape Disentangled Generative Modeling for Congenital Heart Defects -- Automated Coronary Vessels Segmentation in X-ray Angiography Using Graph Attention Network -- Inherent Atrial Fibrillation Vulnerability in the Appendages Exacerbated in Heart Failure -- Two-Stage Deep Learning Framework for Quality Assessment of Left Atrial Late Gadolinium Enhanced MRI Images -- Automatic Landing Zone Plane Detection in Contrast-Enhanced Cardiac CT Volumes -- A Benchmarking Study of Deep Learning Approaches for Bi-atrial Segmentation on Late Gadolinium-enhanced MRIs -- Fill the K-Space and Refine the Image: Prompting for Dynamic and Multi-Contrast MRI Reconstruction -- Learnable objective image function for accelerated MRI reconstruction -- Accelerating Cardiac MRI via Deblurring without Sensitivity Estimation -- T1/T2 relaxation temporal modelling from accelerated acquisitions using a Latent Transformer -- T1 and T2 mapping reconstruction based on conditional DDPM -- $k$-$t$ CLAIR: Self-Consistency Guided Multi-Prior Learning for Dynamic Parallel MR Image Reconstruction -- Cardiac MRI reconstruction from undersampled k-space using double-stream IFFT and a denoising GNA-UNET pipeline -- Multi-Scale Inter-Frame Information Fusion Based Network for Cardiac MRI Reconstruction -- Relaxometry Guided Quantitative Cardiac Magnetic Resonance Image Reconstruction -- A Context-Encoders-based Generative Adversarial Networks for Cine Magnetic Resonance Imaging Reconstruction -- Accelerated Cardiac Parametric Mapping using Deep Learning-Refined Subspace Models -- DiffCMR: Fast Cardiac MRI Reconstruction with Diffusion Probabilistic Models -- C3-Net: Complex-Valued Cascading Cross-Domain Convolutional Neural Network for Reconstructing Undersampled CMR Images -- Space-Time Deformable Attention Parallel Imaging Reconstruction for Highly Accelerated Cardiac MRI -- Multi-level Temporal Information Sharing Transformer-based Feature Reuse Network for Cardiac MRI Reconstruction -- Cine cardiac MRI reconstruction using a convolutional recurrent network with refinement -- ReconNext:A Encoder-Decoder Skip Cross Attention based approach to reconstruct Cardiac MRI -- Temporal Super-Resolution for Fast T1 Mapping -- NoSENSE: Learned Unrolled Cardiac MRI Reconstruction Without Explicit Sensitivity Maps -- CineJENSE: Simultaneous Cine MRI Image Reconstruction and Sensitivity Map Estimation using Neural Representations -- Deep Cardiac MRI Reconstruction with ADMM. |
| Record Nr. | UNINA-9910831019803321 |
Camara Oscar
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| Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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