Statistical Atlases and Computational Models of the Heart. M&Ms and EMIDEC Challenges: 11th International Workshop, STACOM 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, Revised Selected Papers 1st ed. 2021 [Pehme köide]

Regular papers.- A persistent homology-based topological loss function
for multi-class CNN segmentation of cardiac MRI.- Automatic multiplanar CT
reformatting from trans-axial into left ventricle short-axis view.- Graph
convolutional regression of cardiac depolarization from sparse endocardial
maps.- A cartesian grid representation of left atrial appendages for deep
learning based estimation of thrombogenic risk predictors.- Measure
Anatomical Thickness from Cardiac MRI with Deep Neural Networks.- Modelling
Fine-rained Cardiac Motion via Spatio-temporal Graph Convolutional Networks
to Boost the Diagnosis of Heart Conditions- Towards mesh-free
patient-specific mitral valve modeling.- PIEMAP: Personalized Inverse Eikonal
Model from cardiac Electro-Anatomical Maps.- Automatic Detection of Landmarks
for Fast Cardiac MR Image Registration.- Quality-aware semi-supervised
learning for CMR segmentation.- Estimation of imaging biomarkers progression
in post-infarct patients usingcross-sectional data.- PC-U Net: Learning to
Jointly Reconstruct and Segment the Cardiac Walls in 3D from CT Data.- Shape
constrained CNN for cardiac MR segmentation with simultaneous prediction of
shape and pose parameters.- Left atrial ejection fraction estimation using
SEGANet for fully automated segmentation of CINE MRI.- Estimation of Cardiac
Valve Annuli Motion with Deep Learning.- 4D Flow Magnetic Resonance Imaging
for Left Atrial Haemodynamic Characterization and Model Calibration.-
Segmentation-free Estimation of Aortic Diameters from MRI Using Deep
Learning.- M&Ms challenge.- Histogram Matching Augmentation for Domain
Adaptation with Application to Multi-Centre, Multi-Vendor and Multi-Disease
Cardiac Image Segmentation.- Disentangled Representations for
Domain-generalized Cardiac Segmentation.- A 2-step Deep Learning method with
Domain Adaptation for Multi-Centre, Multi-Vendor and Multi-Disease Cardiac
Magnetic Resonance Segmentation.- Random Style Transfer based Domain
Generalization Networks Integrating Shape and Spatial Information.-
Semi-supervised Cardiac Image Segmentation via Label Propagation and Style
Transfer.- Domain-Adversarial Learning for Multi-Centre, Multi-Vendor, and
Multi-Disease Cardiac MR Image Segmentation.- Studying Robustness of
Segmantic Segmentation under Domain Shift in cardiac MRI.- A deep
convolutional neural network approach for the segmentation of cardiac
structures from MRI sequences.- Multi-center, Multi-vendor, and Multi-disease
Cardiac Image Segmentation Using Scale-Independent Multi-Gate UNET.- Adaptive
Preprocessing for Generalization in Cardiac MR Image Segmentation.-
Deidentifying MRI data domain by iterative backpropagation.- A generalizable
deep-learning approach for cardiac magnetic resonance image segmentation
using image augmentation and attention U-Net.- Generalisable Cardiac
Structure Segmentation via Attentional and Stacked Image Adaptation.-
Style-invariant Cardiac Image Segmentation with Test-time Augmentation.-
EMIDEC challenge.- Comparison of a Hybrid Mixture Model and a CNN for the
Segmentation of Myocardial Pathologies in Delayed Enhancement MRI.- Cascaded
Convolutional Neural Network for Automatic Myocardial Infarction Segmentation
from Delayed-Enhancement Cardiac MRI.- Automatic Myocardial Disease
Prediction From Delayed-Enhancement Cardiac MRI and Clinical Information.-
SM2N2: A Stacked Architecture for Multimodal Data and its Application to
Myocardial Infarction Detection.- A Hybrid Network for Automatic Myocardial
Infarction Segmentation in Delayed Enhancement-MRI.- Efficient 3D deep
learning for myocardial diseases segmentation.- Deep-learning-based
myocardial pathology detection.- Automatic Myocardial Infarction Evaluation
from Delayed-Enhancement Cardiac MRI using Deep Convolutional Networks.-
Uncertainty-based Segmentation of Myocardial Infarction Areas on Cardiac MR
images.- Anatomy Prior Based U-net for Pathology Segmentation with
Attention.- Automatic Scar Segmentation from DE-MRI Using 2D Dilated UNet
with Rotation-based Augmentation.- Classification of pathological cases of
myocardial infarction using Convolutional Neural Network and Random Forest.