MUSE: Multi-View Echocardiography Segmentation via View-Adaptive Experts and Boundary-Aware Multi-Domain Fusion

Echocardiography segmentation (ES) can substantially reduce radiologists' heavy clinical workload in image analysis. Despite recent advances, accurate multi-view segmentation remains challenging because echocardiograms are complex and ambiguous, whereas existing models mainly focus on a single echocardiographic view, limiting the clinical value of current models. To address these issues, we propose Multi-View Echocardiography Segmentation via View-Adaptive Experts and Boundary-Aware Multi-Domain Fusion (MUSE), a novel framework that aims to improve multi-view echocardiogram segmentation through view-adaptive expert paths. Specifically, MUSE adopts an encoder-decoder architecture. To handle ambiguous and complex echocardiographic images, MUSE constructs boundary priors from bottleneck features and performs multi-domain fusion through boundary-aware gating, which selectively enhances the wavelet, spatial-feature, and adaptive multiband Fourier branches. To further improve view-specific segmentation, we design a multi-expert routing mechanism that predicts view types from image features and employs shared and view-specific experts to extract common and distinctive features for segmentation. Additionally, to alleviate the limited view variety of existing datasets, we collect and annotate a multi-view, multi-structure echocardiography dataset, thereby improving clinical relevance through broader view coverage. Experimental results on two public datasets and one private dataset demonstrate the effectiveness of MUSE, which achieves state-of-the-art segmentation performance on both public and private datasets across diverse views and anatomical structures.