1. 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 
- STACOM (Workshop) (13th : 2022 : Singapore)
- Cham : Springer, 2023.
- Book — 1 online resource (52 pages) : illustrations (black and white).
- 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 in the 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.
- (source: Nielsen Book Data)
(source: Nielsen Book Data)