Puyol Anton Esther, Pop Mihaela, Sermesant Maxime Statistical Atlases and Computational Models of the Heart. M&ms and Emidec Challenges: 11th International Workshop, Stacom 2020, Held in Conjunction w 9783030681067

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Автор: Puyol Anton Esther, Pop Mihaela, Sermesant Maxime
Название: Statistical Atlases and Computational Models of the Heart. M&ms and Emidec Challenges: 11th International Workshop, Stacom 2020, Held in Conjunction w
ISBN: 9783030681067
Издательство: Springer
Классификация:

Общество и общественные науки

Учебное оборудование и технологии, компьютерное обучение (CAL)

Машинное обучение

Распознавание образов

Обработка изображений

ISBN-10: 3030681068
Обложка/Формат: Paperback
Страницы: 417
Вес: 0.61 кг.
Дата издания: 05.04.2021
Язык: English
Размер: 23.39 x 15.60 x 2.26 cm
Ссылка на Издательство: Link
Поставляется из: Германии
Описание: 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 using cross-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 wi

Statistical Atlases and Computational Models of the Heart. M&ms and Emidec Challenges: 11th International Workshop, Stacom 2020, Held in Conjunction w, Puyol Anton Esther, Pop Mihaela, Sermesant Maxime