Statistical Atlases and Computational Models of the Heart. Regular and CMRxRecon Challenge Papers : 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.

Format:

Book

Contributor:

Camara, Oscar (Oscar Camara Rey), editor.

Series:

Lecture Notes in Computer Science, 1611-3349 ; 14507

Language:

English

Subjects (All):

Computer vision.

Computer science--Mathematics.

Computer science.

Mathematical statistics.

Machine learning.

Computer engineering.

Computer networks.

Social sciences--Data processing.

Social sciences.

Computer Vision.

Probability and Statistics in Computer Science.

Machine Learning.

Computer Engineering and Networks.

Computer Application in Social and Behavioral Sciences.

Local Subjects:

Computer Vision.

Probability and Statistics in Computer Science.

Machine Learning.

Computer Engineering and Networks.

Computer Application in Social and Behavioral Sciences.

Physical Description:

1 online resource (507 pages)

Edition:

1st ed. 2024.

Place of Publication:

Cham : Springer Nature Switzerland : Imprint: Springer, 2024.

Summary:

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. .

Contents:

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.

Notes:

Includes bibliographical references and index.

ISBN:

3-031-52448-9