Vision-Language Semantic Guidance for Ejection Fraction Assessment in Echocardiography

Ejection fraction (EF) is a key indicator of cardiac function, crucial for diagnosing heart failure and guiding treatment. Its estimation from echocardiography is challenged by morphological changes across cardiac phases and low-quality, noisy boundaries. We propose EFusionNet, a multimodal segmentation framework that integrates echocardiographic images with structured diagnostic text to enhance segmentation and EF assessment. Clinical phrases (e.g., 'irregular boundary') are embedded via a domain-specific language model into both input fusion and UNet skip connections, enabling phase-aware feature calibration. A feature fusion enhancement module (FFEM) refines spatial localization, while a multi-objective loss enforces uncertainty learning and semantic consistency. Evaluated on CAMUS and EchoNet-Dynamic datasets, EFusionNet achieves Dice scores of 91.2%/90.1% and EF_MAE of 4.8/5.0, outperforming baselines and improving reliable, interpretable EF estimation.