Statistical Atlases and Computational Models of the Heart. Multi-Disease, Multi-View, and Multi-Center Right Ventricular Segmentation in Cardiac MRI Challenge : 12th International Workshop, STACOM 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, September 27, 2021, Revised Selected Papers / edited by Esther Puyol Antón, Mihaela Pop, Carlos Martín-Isla, Maxime Sermesant, Avan Suinesiaputra, Oscar Camara, Karim Lekadir, Alistair Young.

Format:

Book

Contributor:

Puyol Anton, Esther, Editor.

Pop, Mihaela, Editor.

Martín-Isla, Carlos., Editor.

Sermesant, Maxime, Editor.

Suinesiaputra, Avan, Editor.

Camara, Aboubacar, Editor.

Lekadir, Karim, Editor.

Young, Alistair, Editor.

SpringerLink (Online service)

Series:

Computer Science (SpringerNature-11645)

LNCS sublibrary. Image processing, computer vision, pattern recognition, and graphics ; SL 6, 13131

Image Processing, Computer Vision, Pattern Recognition, and Graphics ; 13131

Language:

English

Subjects (All):

Computer vision.

Machine learning.

Pattern recognition systems.

Application software.

Computers.

Computer Vision.

Machine Learning.

Automated Pattern Recognition.

Computer and Information Systems Applications.

Computing Milieux.

Local Subjects:

Computer Vision.

Machine Learning.

Automated Pattern Recognition.

Computer and Information Systems Applications.

Computing Milieux.

Physical Description:

1 online resource (XIII, 385 pages) : 149 illustrations, 139 illustrations in color.

Edition:

1st ed. 2022.

Contained In:

Springer Nature eBook

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2022.

System Details:

text file PDF

Summary:

This book constitutes the proceedings of the 12th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2021, as well as the M&Ms-2 Challenge: Multi-Disease, Multi-View and Multi-Center Right Ventricular Segmentation in Cardiac MRI Challenge. The 25 regular workshop papers included in this volume were carefully reviewed and selected after being revised. They deal with cardiac imaging and image processing, machine learning applied to cardiac imaging and image analysis, atlas construction, artificial intelligence, statistical modelling of cardiac function across different patient populations, cardiac computational physiology, model customization, atlas based functional analysis, ontological schemata for data and results, integrated functional and structural analyses, as well as the pre-clinical and clinical applicability of these methods. In addition, 15 papers from the M&MS-2 challenge are included in this volume. The Multi-Disease, Multi-View and Multi-Center Right Ventricular Segmentation in Cardiac MRI Challenge (M&Ms-2) is focusing on the development of generalizable deep learning models for the Right Ventricle that can maintain good segmentation accuracy on different centers, pathologies and cardiac MRI views. There was a total of 48 submissions to the workshop.

Contents:

Multi-atlas segmentation of the aorta from 4D flow MRI: comparison of several fusion strategie

Quality-aware Cine Cardiac MRI Reconstruction and Analysis from Undersampled k-space Data

Coronary Artery Centerline Refinement using GCN Trained with Synthetic Data

Novel imaging biomarkers to evaluate heart dysfunction post-chemotherapy: a preclinical MRI feasibility study

A bi-atrial statistical shape model as a basis to classify left atrial enlargement from simulated and clinical 12-lead ECGs

Vessel Extraction and Analysis of Aortic Dissection

The Impact of Domain Shift on Left and Right Ventricle Segmentation in Short Axis Cardiac MR Images

Characterizing myocardial ischemia and reperfusion patterns with hierarchical manifold learning

Generating Subpopulation-Specific Biventricular Anatomy Models Using Conditional Point Cloud Variational Autoencoders

Improved AI-based Segmentation of Apical and Basal Slices from Clinical Cine CMR

Mesh Convolutional Neural Networks for Wall Shear Stress Estimation in 3D Artery Models

Hierarchical multi-modality prediction model to assess obesity-related remodelling

Neural Angular Plaque Characterization:Automated Quantification of Polar Distributionfor Plaque Composition

Simultaneous Segmentation and Motion Estimation of Left Ventricular Myocardium in 3D Echocardiography using Multi-task Learning

Statistical shape analysis of the tricuspid valve in hypoplastic left heart syndrome

An Unsupervised 3D Recurrent Neural Networkfor Slice Misalignment Correction in CardiacMR Imaging

Unsupervised Multi-Modality RegistrationNetwork based on Spatially Encoded Gradient Information

In-silico analysis of device-related thrombosis for different left atrial appendage occluder settings

Valve flattening with functional biomarkers for the assessment of mitral valve repair

Multi-modality cardiac segmentation via mixing domains for unsupervised adaptation

Uncertainty-Aware Training for Cardiac Resynchronisation Therapy Response Prediction

Cross-domain Artefact Correction of Cardiac MRI

Detection and Classification of Coronary Artery Plaques in Coronary Computed Tomography Angiography Using 3D CNN

Predicting 3D Cardiac Deformations With Point Cloud Autoencoders

Influence of morphometric and mechanical factors in thoracic aorta finite element modeling

Right Ventricle Segmentation via Registration and Multi-input Modalities in Cardiac Magnetic Resonance Imaging from Multi-Disease, Multi-View and Multi-Center

Using MRI-specific Data Augmentation to Enhance the Segmentation of Right Ventricle in Multi-disease, Multi-center and Multi-view Cardiac MRI

Right Ventricular Segmentation from Short- and Long-Axis MRIs via Information Transition

Tempera: Spatial Transformer Feature Pyramid Network for Cardiac MRI Segmentation

Multi-view SA-LA Net: A framework for simultaneous segmentation of RV on multi-view cardiac MR Images

Right ventricular segmentation in multi-view cardiac MRI using a unified U-net model

Deformable Bayesian Convolutional Networks for Disease-Robust Cardiac MRI Segmentation

Consistency based Co-Segmentation for Multi-View Cardiac MRI using Vision Transformer

Refined Deep Layer Aggregation for Multi-Disease, Multi-View and Multi-Center Cardiac MR Segmentation

A Multi-View Cross-Over Attention U-Net Cascade With Fourier Domain Adaptation For Multi-Domain Cardiac MRI Segmentation

Multi-Disease, Multi-View and Multi-Center Right Ventricular Segmentation in Cardiac MRI using Efficient Late-Ensemble Deep Learning Approach

Automated Segmentation of the Right Ventricle from Magnetic Resonance Imaging Using Deep Convolutional Neural Networks

3D right ventricle reconstruction from 2D U-Net segmentation of sparse short-axis and 4-chamber cardiac cine MRI views

Late Fusion U-Net with GAN-based Augmentation for Generalizable Cardiac MRI Segmentation

Using Out-of-Distribution Detection for Model Refinement in Cardiac Image Segmentation.

Other Format:

Printed edition:

ISBN:

978-3-030-93722-5

9783030937225

Access Restriction:

Restricted for use by site license.