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Synthetic Echocardiographic Image Sequences for Cardiac Inverse Electro-Kinematic Learning

Abstract : In this paper, we propose to create a rich database of syn- thetic time series of 3D echocardiography (US) images using simulations of a cardiac electromechanical model, in order to study the relationship between electrical disorders and kinematic patterns visible in medical images. From a real 4D sequence, a software pipeline is applied to create several synthetic sequences by combining various steps including motion tracking and segmentation. We use here this synthetic database to train a machine learning algorithm which estimates the depolarization times of each cardiac segment from invariant kinematic descriptors such as local displacements or strains. First experiments on the inverse electro- kinematic learning are demonstrated on the synthetic 3D US database and are evaluated on clinical 3D US sequences from two patients with Left Bundle Branch Block.
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Submitted on : Friday, August 19, 2011 - 7:57:18 PM
Last modification on : Tuesday, October 25, 2022 - 4:21:29 PM

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Adityo Prakosa, Maxime Sermesant, Hervé Delingette, Eric Saloux, Pascal Allain, et al.. Synthetic Echocardiographic Image Sequences for Cardiac Inverse Electro-Kinematic Learning. Proceedings of Medical Image Computing and Computer Assisted Intervention (MICCAI), 2011, Toronto, Canada, Canada. pp.500-507, ⟨10.1007/978-3-642-23623-5_63⟩. ⟨inria-00616214⟩



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