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Titlebook: Statistical Atlases and Computational Models of the Heart. Multi-Sequence CMR Segmentation, CRT-EPig; 10th International W Mihaela Pop,Maxi

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书目名称Statistical Atlases and Computational Models of the Heart. Multi-Sequence CMR Segmentation, CRT-EPig
副标题10th International W
编辑Mihaela Pop,Maxime Sermesant,Avan Suinesiaputra
视频video
丛书名称Lecture Notes in Computer Science
图书封面Titlebook: Statistical Atlases and Computational Models of the Heart. Multi-Sequence CMR Segmentation, CRT-EPig; 10th International W Mihaela Pop,Maxi
描述.This book constitutes the thoroughly refereed post-workshop proceedings of the 10th International Workshop on Statistical Atlases and Computational Models of the Heart: Atrial Segmentation and LV Quantification Challenges, STACOM 2019, held in conjunction with MICCAI 2019, in Shenzhen, China, in October 2019...The 42 revised full workshop papers were carefully reviewed and selected from 76 submissions. The topics of the workshop included: cardiac imaging and image processing, machine learning applied to cardiac imaging and image analysis, atlas construction, statistical modelling of cardiac function across different patient populations, cardiac computational physiology, model customization, atlas based functional analysis, ontological schemata for data and results, integrated functional and structural analyses, as well as the pre-clinical and clinical applicability of these methods..
出版日期Conference proceedings 2020
关键词artificial intelligence; automatic segmentations; CFD; color image processing; computer vision; CRT; deep
版次1
doihttps://doi.org/10.1007/978-3-030-39074-7
isbn_softcover978-3-030-39073-0
isbn_ebook978-3-030-39074-7Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer Nature Switzerland AG 2020
The information of publication is updating

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Towards Hyper-Reduction of Cardiac Models Using Poly-affine Transformationsof freedom, allows a lower computational cost while preserving a good accuracy for simple geometries. The method is validated on a cube under simple compression and preliminary results on simplified cardiac geometries are presented, reducing by a factor 100 the number of degrees of freedom.
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Non-invasive Pressure Estimation in Patients with Pulmonary Arterial Hypertension: Data-Driven or Mo model personalisation and tested its prediction power of catheter data. Standard machine learning methods were also investigated for pulmonary artery pressure prediction. Our preliminary results demonstrated the potential prediction power of both data-driven and model-based approaches.
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0302-9743 tational Models of the Heart: Atrial Segmentation and LV Quantification Challenges, STACOM 2019, held in conjunction with MICCAI 2019, in Shenzhen, China, in October 2019...The 42 revised full workshop papers were carefully reviewed and selected from 76 submissions. The topics of the workshop includ
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Co-registered Cardiac , DT Images and Histological Images for Fibrosis Quantificatione processing pipeline developed to quantitatively analyze collagen density and features in a pig model of chronic fibrosis. Specifically, we use . diffusion tensor imaging (DTI) (. mm resolution) to calculate fractional anisotropy maps in: healthy tissue, infarct core (IC) and gray zone (GZ) (i.e.,
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Manufacturing of Ultrasound- and MRI-Compatible Aortic Valves Using 3D Printing for Analysis and Simlead to organ structure change. Aortic stenosis is the most common valve pathology with controversies regarding its optimal management, such as the timing of valve replacement. Therefore, there is emerging demand for analysis and simulation of valves to help researchers and companies to test novel a
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