MM-MoE: A Disease-oriented Multi-Task and Multi-View Framework for Generalized Cardiac Segmentation

Cardiovascular diseases typically cause specific changes in cardiac structure, but these disease-induced variations are often less pronounced than those introduced by multi-source data acquisition or noise in the images, such as fluctuations in image brightness, contrast, and field of view. This makes it more difficult to extract key details about organs and diseases, posing significant challenges to traditional segmentation and domain generalization methods. To address these challenges, we propose MM-MoE, a disease-oriented multi-task and multi-view framework designed for more accurate generalized cardiac structure segmentation. Specifically, we introduce a Mixture-of-Experts based Multi-Task Joint and Adversarial Learning Strategy (MTMoE-J&A) to enable the model to learn more general and domain-invariant disease-specific features, and employ a Multi-View Feature Fusion Network (MVFusion) to effectively integrate complementary spatial information from different views, leading to improved segmentation accuracy and completeness. Additionally, we develop a parallel Channel-Spatial-Slice Attention Module (CSS-Attn) to mitigate crucial detail loss caused by the anisotropic nature of medical images. Extensive experiments conducted on the M&Ms-2 dataset demonstrate that MM-MoE outperforms state-of-the-art segmentation models and domain generalization methods in terms of segmentation generalization performance.