Statistical Atlases and Computational Models of the Heart. Regular and CMRxRecon Challenge Papers (Record no. 187098)

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control field 978-3-031-52448-6
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control field DE-He213
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control field 20240423130309.0
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020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9783031524486
-- 978-3-031-52448-6
024 7# - OTHER STANDARD IDENTIFIER
Standard number or code 10.1007/978-3-031-52448-6
Source of number or code doi
050 #4 - LIBRARY OF CONGRESS CALL NUMBER
Classification number TA1634
072 #7 - SUBJECT CATEGORY CODE
Subject category code UYQV
Source bicssc
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Subject category code COM016000
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Subject category code UYQV
Source thema
082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 006.37
Edition number 23
245 10 - TITLE STATEMENT
Title Statistical Atlases and Computational Models of the Heart. Regular and CMRxRecon Challenge Papers
Medium [electronic resource] :
Remainder of title 14th International Workshop, STACOM 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 12, 2023, Revised Selected Papers /
Statement of responsibility, etc edited by Oscar Camara, Esther Puyol-Antón, Maxime Sermesant, Avan Suinesiaputra, Qian Tao, Chengyan Wang, Alistair Young.
250 ## - EDITION STATEMENT
Edition statement 1st ed. 2024.
264 #1 -
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-- Springer Nature Switzerland :
-- Imprint: Springer,
-- 2024.
300 ## - PHYSICAL DESCRIPTION
Extent XIV, 494 p. 169 illus., 149 illus. in color.
Other physical details online resource.
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490 1# - SERIES STATEMENT
Series statement Lecture Notes in Computer Science,
International Standard Serial Number 1611-3349 ;
Volume number/sequential designation 14507
505 0# - FORMATTED CONTENTS NOTE
Formatted contents note 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 LearningFramework 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.
520 ## - SUMMARY, ETC.
Summary, etc This book constitutes the proceedings of the 14th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2023, as well as the Cardiac MRI Reconstruction Challenge, CMRxRecon Challenge. There was a total of 53 submissions to the workshop. The 24 regular workshop papers included in this volume were carefully reviewed and selected from 29 paper submissions. They deal with cardiac segmentation, modelling, strain quantification, registration, statistical shape analysis, and quality control. In addition, 21 papers from the CMRxRecon challenge are included in this volume. They focus on fast CMR image reconstruction and provide a benchmark dataset that enables the broader research community to promote advances in this area of research. .
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Computer vision.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Computer science
General subdivision Mathematics.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Mathematical statistics.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Machine learning.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Computer engineering.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Computer networks .
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Social sciences
General subdivision Data processing.
650 14 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Computer Vision.
650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Probability and Statistics in Computer Science.
650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Machine Learning.
650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Computer Engineering and Networks.
650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Computer Application in Social and Behavioral Sciences.
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Camara, Oscar.
Relator term editor.
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700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Puyol-Antón, Esther.
Relator term editor.
Relator code edt
-- http://id.loc.gov/vocabulary/relators/edt
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Sermesant, Maxime.
Relator term editor.
Relator code edt
-- http://id.loc.gov/vocabulary/relators/edt
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Suinesiaputra, Avan.
Relator term editor.
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700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Tao, Qian.
Relator term editor.
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700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Wang, Chengyan.
Relator term editor.
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700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Young, Alistair.
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Corporate name or jurisdiction name as entry element SpringerLink (Online service)
773 0# - HOST ITEM ENTRY
Title Springer Nature eBook
776 08 - ADDITIONAL PHYSICAL FORM ENTRY
Display text Printed edition:
International Standard Book Number 9783031524479
776 08 - ADDITIONAL PHYSICAL FORM ENTRY
Display text Printed edition:
International Standard Book Number 9783031524493
830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE
Uniform title Lecture Notes in Computer Science,
-- 1611-3349 ;
Volume number/sequential designation 14507
856 40 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier <a href="https://doi.org/10.1007/978-3-031-52448-6">https://doi.org/10.1007/978-3-031-52448-6</a>
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