Echo-SAM: Zero-Shot Learning of Unseen Structures via Medical Knowledge Graph Grounding

Segmenting unseen anatomical structures in echocardiographic images is challenging because dense annotations are scarce and ultrasound often presents visually similar textures across different cardiac structures. Current foundation models remain limited in this setting because they treat anatomical knowledge as independent text prompts, overlooking the spatial topology that clinicians use to localize ambiguous structures from their visible neighbors. We propose Echo-SAM, a zero-shot ultrasound segmentation framework that grounds a structured cardiac knowledge graph in visual space through relation-conditioned geometric support inference. Echo-SAM localizes seen structures as anatomical anchors, propagates relational constraints through a knowledge-grounded Graph Neural Network, and enhances text prompts with image-grounded anatomical context. We further introduce a topology-to-geometry mapping that converts graph relations into geometry-aware support proposal scores, yielding posterior-guided support estimates for unseen structures. By treating relation-specific scale, thickness, and offset as latent geometric variables, Echo-SAM adapts graph-derived support proposals to image-specific anchors without relying on fixed coordinate templates. Extensive evaluations on zero-shot echocardiography segmentation benchmarks demonstrate that Echo-SAM substantially outperforms state-of-the-art foundation models, achieving up to 50.42\% absolute improvement in Dice score. Code will be publicly available.