Statistical Atlases and Computational Models of the Heart. Workshop, CMRxRecon and MBAS Challenge Papers.

15th International Workshop, STACOM 2024, Held in Conjunction with MICCAI 2024, Marrakesh, Morocco, October 10, 2024, Revised Selected Papers

AvOscar Camara,Esther Puyol-Antón

Häftad, Engelska, 2025

Del 15448 i serien Lecture Notes in Computer Science

1 759 kr

Beställningsvara. Skickas inom 10-15 vardagar. Fri frakt över 249 kr.

Beskrivning

This book constitutes the proceedings of the 15th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2024, held in conjunction with the 27th International conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2024, in Marrakesh, Morocco in October 2024.The 48 regular workshop papers included in this volume were carefully reviewed and selected from 64 paper submissions. They focus on Regular Papers; Multiclass Bi-Atrial Segmentation Challenge and Cardiac MRI Reconstruction Challenge.

Produktinformation

Utgivningsdatum:2025-04-29
Mått:155 x 235 x 28 mm
Vikt:756 g
Format:Häftad
Språk:Engelska
Serie:Lecture Notes in Computer Science
Antal sidor:490
Förlag:Springer International Publishing AG
ISBN:9783031877551

Utforska kategorier

Innehållsförteckning

.- Single-source Domain Generalization for Coronary Vessels Segmentation in X-ray Angiography. .- Constraint-Based Model in Multimodal Learning to Improve Ventricular Arrhythmia Prediction. .- Automated estimation of cardiac stroke volumes from computed tomography. .- Peridevice leaks following left atrial appendage occlusion - analysis with morphology descriptive centerlines and explainable graph attention network. .- Improved 3D Whole Heart Geometry from Sparse CMR Slices. .- CavityBASNet: Cavity-focused Biatrial Automatic Segmentation on LGE MRI with augmented input channel and left-right myocardium splitting. .- A novel MRI-based electrophysiological computational model of progressive doxorubicin-induced fibrosis in the left ventricle. .- Quantitative comparison of blood flow patterns from in silico simulations and 4D flow data before and after left atrial occlusion. .- Panoramic anatomical context in 3D intracardiac echocardiography (ICE) with 3D registration and geometry-based image fusion. .- Physics-Informed Neural Networks can accurately model cardiac electrophysiology in 3D geometries and fibrillatory conditions. .- Beyond the standards: Fully-Automated Aortic Annulus Segmentation on Contrast-free Magnetic Resonance Imaging using a Computational Aorta Unwrapping Method. .- Coronary Artery Calcium Scoring from Non-Contrast Cardiac CT Using Deep Learning With External Validation. .- Effective approach based on student-teacher self-supervised deep learning for Multi-class Bi-Atrial Segmentation Challenge. .- Sampling-Pattern-Agnostic MRI Reconstruction through Adaptive Consistency Enforcement with Diffusion Model. .- HyperCMR: Enhanced Multi-Contrast CMR Reconstruction with Eagle Loss. .- A Multi-Contrast Cardiac MRI Reconstruction Method Using an Advanced Unrolled Network Architecture. .- Implicit Neural Representations for Registration of Left Ventricle Myocardium During a Cardiac Cycle. .- Deep Multi-contrast Cardiac MRI Reconstruction via vSHARP with Auxil iary Refinement Network. .- Multi-Model Ensemble Approach for Accurate Bi-Atrial Segmentation in LGE-MRI of Atrial Fibrillation Patients. .- Two-Stage nnU-Net for Automatic Multi-class Bi-Atrial Segmentation from LGE-MRIs. .- An Ensemble of 3D Residual Encoder UNet Models for Solving Multi-Class Bi-Atrial Segmentation Challenge. .- Evaluating Convolution, Attention, and Mamba Based U-Net Models for Multi-Class Bi-Atrial Segmentation from LGE-MRI. .- On the Foundation Model for Cardiac MRI Reconstruction. .- Multi-Loss 3D Segmentation for Enhanced Bi-Atrial Segmentation. .- Classification of Mitral Regurgitation from Cardiac Cine MRI using Clinically-Interpretable Morphological Features. .- Gaussian Process Emulators for Few-Shot Segmentation in Cardiac MRI. .- Global Control for Local SO(3)-Equivariant Scale-Invariant Vessel Segmentation. .- A self-distillation bi-atrial segmentation network for Cardiac MRI. .- Adaptive Unrolling Applied to the CMRxRecon2024 Callenge. .- Reducing the number of leads for ECG Imaging with Graph Neural Networks and meaningful latent space. .- Rotor Core Projection Ablation (RCPA): Novel Computational Approach to Catheter Ablation Therapy for Atrial Fibrillation. .- Automated pipeline for regional epicardial adipose tissue distribution analysis in the left atrium. .- Low-Rank Conjugate Gradient-Net for Accelerated Cardiac MR Imaging. .- SBAW-Net: Segmentation of Bi-Atria and Wall Network - Offering Valuable Insights into Challenge Data. .- ResNet-based Convolutional Framework for Segmenting Left Atrial Scars and Cavities. .- EAT-Mamba: Epicardial Adipose Tissue Segmentation from Multi-modal Dixon MRI. .- Neural Fields for Continuous Periodic Motion Estimation in 4D Cardiovascular Imaging. .- Exploring CNN and Transformer Architectures for Multi-class Bi-Atrial Segmentation from Late Gadolinium-Enhanced MRI. .- EigenBoundaries for the temporally regularized segmentation of echocardiographic images. .- Dynamic Cardiac MRI Reconstruction via Separate Optimization of K-space and Hybrid-domian Spatial-temporal Feature Fusion. .- an Interpretable Learning of Risk Explain Ventricular Arrhythmia Mechanism. .- 3D Left Ventricular Reconstruction from 2D Echocardiograms for Reliable Volume Estimation. .- Comparing Left Atrial Spontaneous Echo Contrast Intensity with Gaussian Process Emulator Predictions. .- UPCMR: A Universal Prompt-guided Model for Random Sampling Cardiac MRI Reconstruction. .- An All-in-one Approach for Accelerated Cardiac MRI Reconstruction. .- Improving the Scan-rescan Precision of AI-based CMR Biomarker Estimation.

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Häftad, Engelska, 2025

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Beställningsvara. Skickas inom 10-15 vardagar. Fri frakt över 249 kr.

Beskrivning

Produktinformation

Utgivningsdatum:2025-04-29
Mått:155 x 235 x 28 mm
Vikt:756 g
Format:Häftad
Språk:Engelska
Serie:Lecture Notes in Computer Science
Antal sidor:490
Förlag:Springer International Publishing AG
ISBN:9783031877551

Utforska kategorier

Innehållsförteckning

.- Single-source Domain Generalization for Coronary Vessels Segmentation in X-ray Angiography. .- Constraint-Based Model in Multimodal Learning to Improve Ventricular Arrhythmia Prediction. .- Automated estimation of cardiac stroke volumes from computed tomography. .- Peridevice leaks following left atrial appendage occlusion - analysis with morphology descriptive centerlines and explainable graph attention network. .- Improved 3D Whole Heart Geometry from Sparse CMR Slices. .- CavityBASNet: Cavity-focused Biatrial Automatic Segmentation on LGE MRI with augmented input channel and left-right myocardium splitting. .- A novel MRI-based electrophysiological computational model of progressive doxorubicin-induced fibrosis in the left ventricle. .- Quantitative comparison of blood flow patterns from in silico simulations and 4D flow data before and after left atrial occlusion. .- Panoramic anatomical context in 3D intracardiac echocardiography (ICE) with 3D registration and geometry-based image fusion. .- Physics-Informed Neural Networks can accurately model cardiac electrophysiology in 3D geometries and fibrillatory conditions. .- Beyond the standards: Fully-Automated Aortic Annulus Segmentation on Contrast-free Magnetic Resonance Imaging using a Computational Aorta Unwrapping Method. .- Coronary Artery Calcium Scoring from Non-Contrast Cardiac CT Using Deep Learning With External Validation. .- Effective approach based on student-teacher self-supervised deep learning for Multi-class Bi-Atrial Segmentation Challenge. .- Sampling-Pattern-Agnostic MRI Reconstruction through Adaptive Consistency Enforcement with Diffusion Model. .- HyperCMR: Enhanced Multi-Contrast CMR Reconstruction with Eagle Loss. .- A Multi-Contrast Cardiac MRI Reconstruction Method Using an Advanced Unrolled Network Architecture. .- Implicit Neural Representations for Registration of Left Ventricle Myocardium During a Cardiac Cycle. .- Deep Multi-contrast Cardiac MRI Reconstruction via vSHARP with Auxil iary Refinement Network. .- Multi-Model Ensemble Approach for Accurate Bi-Atrial Segmentation in LGE-MRI of Atrial Fibrillation Patients. .- Two-Stage nnU-Net for Automatic Multi-class Bi-Atrial Segmentation from LGE-MRIs. .- An Ensemble of 3D Residual Encoder UNet Models for Solving Multi-Class Bi-Atrial Segmentation Challenge. .- Evaluating Convolution, Attention, and Mamba Based U-Net Models for Multi-Class Bi-Atrial Segmentation from LGE-MRI. .- On the Foundation Model for Cardiac MRI Reconstruction. .- Multi-Loss 3D Segmentation for Enhanced Bi-Atrial Segmentation. .- Classification of Mitral Regurgitation from Cardiac Cine MRI using Clinically-Interpretable Morphological Features. .- Gaussian Process Emulators for Few-Shot Segmentation in Cardiac MRI. .- Global Control for Local SO(3)-Equivariant Scale-Invariant Vessel Segmentation. .- A self-distillation bi-atrial segmentation network for Cardiac MRI. .- Adaptive Unrolling Applied to the CMRxRecon2024 Callenge. .- Reducing the number of leads for ECG Imaging with Graph Neural Networks and meaningful latent space. .- Rotor Core Projection Ablation (RCPA): Novel Computational Approach to Catheter Ablation Therapy for Atrial Fibrillation. .- Automated pipeline for regional epicardial adipose tissue distribution analysis in the left atrium. .- Low-Rank Conjugate Gradient-Net for Accelerated Cardiac MR Imaging. .- SBAW-Net: Segmentation of Bi-Atria and Wall Network - Offering Valuable Insights into Challenge Data. .- ResNet-based Convolutional Framework for Segmenting Left Atrial Scars and Cavities. .- EAT-Mamba: Epicardial Adipose Tissue Segmentation from Multi-modal Dixon MRI. .- Neural Fields for Continuous Periodic Motion Estimation in 4D Cardiovascular Imaging. .- Exploring CNN and Transformer Architectures for Multi-class Bi-Atrial Segmentation from Late Gadolinium-Enhanced MRI. .- EigenBoundaries for the temporally regularized segmentation of echocardiographic images. .- Dynamic Cardiac MRI Reconstruction via Separate Optimization of K-space and Hybrid-domian Spatial-temporal Feature Fusion. .- an Interpretable Learning of Risk Explain Ventricular Arrhythmia Mechanism. .- 3D Left Ventricular Reconstruction from 2D Echocardiograms for Reliable Volume Estimation. .- Comparing Left Atrial Spontaneous Echo Contrast Intensity with Gaussian Process Emulator Predictions. .- UPCMR: A Universal Prompt-guided Model for Random Sampling Cardiac MRI Reconstruction. .- An All-in-one Approach for Accelerated Cardiac MRI Reconstruction. .- Improving the Scan-rescan Precision of AI-based CMR Biomarker Estimation.

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