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, Esther Puyol-Antón, Chen Qin, Maxime Sermesant, Avan Suinesiaputra, Shuo Wang, Alistair Young.

Format:

Book

Contributor:

Camara, Oscar (Oscar Camara Rey), editor.

Series:

Lecture Notes in Computer Science, 1611-3349 ; 13593

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 (527 pages)

Edition:

1st ed. 2022.

Place of Publication:

Cham : Springer Nature Switzerland : Imprint: Springer, 2022.

Summary:

This book constitutes the proceedings of the 13th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2022, held in conjunction with the 25th MICCAI conference. The 34 regular workshop papers included in this volume were carefully reviewed and selected after being revised and deal with topics such as: common cardiac segmentation and modelling problems to more advanced generative modelling for ageing hearts, learning cardiac motion using biomechanical networks, physics-informed neural networks for left atrial appendage occlusion, biventricular mechanics for Tetralogy of Fallot, ventricular arrhythmia prediction by using graph convolutional network, and deeper analysis of racial and sex biases from machine learning-based cardiac segmentation. In addition, 14 papers from the CMRxMotion challenge are included in the proceedings which aim to assess the effects of respiratory motion on cardiac MRI (CMR) imaging quality and examine the robustness of segmentation models in face of respiratory motion artefacts. A total of 48 submissions to the workshop was received.

Contents:

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 inthe 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.

Notes:

Includes bibliographical references and index.

Other Format:

Print version: Camara, Oscar Statistical Atlases and Computational Models of the Heart. Regular and CMRxMotion Challenge Papers

ISBN:

9783031234439

303123443X