Statistical atlases and computationa...
STACOM (Workshop) (2022 :)

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  • 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 /
  • 紀錄類型: 書目-電子資源 : Monograph/item
    正題名/作者: Statistical atlases and computational models of the heart/ edited by Oscar Camara ... [et al.].
    其他題名: Regular and CMRxMotion Challenge papers : 13th International Workshop, STACOM 2022, held in conjunction with MICCAI 2022, Singapore, September 18, 2022 : revised selected papers /
    其他題名: STACOM 2022
    其他作者: Camara, Oscar.
    團體作者: STACOM (Workshop)
    出版者: Cham :Springer Nature Switzerland : : 2022.,
    面頁冊數: xiv, 515 p. :ill. (some col.), digital ;24 cm.
    附註: "STACOM 2021, was held as a virtual event."-- Preface.
    內容註: 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.
    Contained By: Springer Nature eBook
    標題: Heart - Congresses. - Imaging -
    電子資源: https://doi.org/10.1007/978-3-031-23443-9
    ISBN: 9783031234439
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