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Titlebook: Statistical Atlases and Computational Models of the Heart. Regular and CMRxMotion Challenge Papers; 13th International W Oscar Camara,Esthe

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31#
發(fā)表于 2025-3-27 00:41:35 | 只看該作者
32#
發(fā)表于 2025-3-27 02:45:10 | 只看該作者
Comparison of?Semi- and Un-Supervised Domain Adaptation Methods for?Whole-Heart Segmentationprocesses as the heart tissue adapts to disease. Coronary Computed Tomography Angiography (CCTA) is considered a first line tool for patients at low or intermediate risk of coronary artery disease, while Coronary Magnetic Resonance Angiography (CMRA) is a promising alternative due to the absence of
33#
發(fā)表于 2025-3-27 05:59:57 | 只看該作者
34#
發(fā)表于 2025-3-27 12:02:53 | 只看該作者
An Atlas-Based Analysis of Biventricular Mechanics in Tetralogy of Fallot variations in ventricular shape are sufficient to explain corresponding differences in ventricular wall motion directly, or if they are indirect markers of altered myocardial mechanical properties. This study was conducted in a cohort of patients with repaired tetralogy of Fallot (rTOF) that face l
35#
發(fā)表于 2025-3-27 14:41:59 | 只看該作者
Review of?Data Types and?Model Dimensionality for?Cardiac DTI SMS-Related Artefact Removaleep learning-based Artificial Intelligence is becoming a crucial tool in mitigating some of its drawbacks, such as the long scan times. As it often happens in fast-paced research environments, a lot of emphasis has been put on showing the capability of deep learning while often not enough time has b
36#
發(fā)表于 2025-3-27 21:15:29 | 只看該作者
Improving Echocardiography Segmentation by?Polar Transformationecade, deep learning-based approaches have significantly improved the performance of echocardiogram segmentation. Most deep learning-based methods assume that the image to be processed is rectangular in shape. However, typically echocardiogram images are formed within a sector of a circle, with a si
37#
發(fā)表于 2025-3-28 00:57:42 | 只看該作者
38#
發(fā)表于 2025-3-28 04:44:02 | 只看該作者
39#
發(fā)表于 2025-3-28 07:25:28 | 只看該作者
Unsupervised Echocardiography Registration Through Patch-Based MLPs and?Transformersre relatively noisy compared to other imaging modalities. Traditional (non-learning) registration approaches rely on the iterative optimization of a similarity metric which is usually costly in time complexity. In recent years, convolutional neural network (CNN) based image registration methods have
40#
發(fā)表于 2025-3-28 11:17:44 | 只看該作者
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