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Cardiac structure segmentation from echocardiogram videos plays a crucialrole in diagnosing heart disease. The combination of multi-view echocardiogramdata is essential to enhance the accuracy and robustness of automated methods.However, due to the visual disparity of the data, deriving cross-view contextinformation remains a challenging task, and unsophisticated fusion strategiescan even lower performance. In this study, we propose a novel Gobal-Localfusion (GL-Fusion) network to jointly utilize multi-view information globallyand locally that improve the accuracy of echocardiogram analysis. Specifically,a Multi-view Global-based Fusion Module (MGFM) is proposed to extract globalcontext information and to explore the cyclic relationship of differentheartbeat cycles in an echocardiogram video. Additionally, a Multi-viewLocal-based Fusion Module (MLFM) is designed to extract correlations of cardiacstructures from different views. Furthermore, we collect a multi-viewechocardiogram video dataset (MvEVD) to evaluate our method. Our methodachieves an 82.29% average dice score, which demonstrates a 7.83% improvementover the baseline method, and outperforms other existing state-of-the-artmethods. To our knowledge, this is the first exploration of a multi-view methodfor echocardiogram video segmentation. Code available at:https://github.com/xmed-lab/GL-Fusion

GL-Fusion: Global-Local Fusion Network for Multi-view Echocardiogram Video Segmentation