SGFA: Spatially Guided Feature Adaptation for Multi-Disease Ultrasound

Ultrasound lesions from different diseases can have similar intensity and geometry but differ in local texture, making ambiguous boundaries difficult to resolve from spatial cues alone. We propose SGFA (Spatially Guided Feature Adaptation), a multi-disease ultrasound lesion segmentation framework combining Shared Texture Projection (STP) with Quadrant-wise Boundary Box Prompting (QBBP). STP generates image-dependent residual feature updates through projection parameters shared across disease categories. QBBP encodes quadrant-wise boundary boxes as sparse and dense prompts and constrains these updates to prompt-supported regions through a spatial gate. We evaluate SGFA using ground-truth-derived (oracle) prompts on a five-disease pancreatic endoscopic ultrasound (EUS) test set comprising 8,531 frames from 164 patients. We additionally evaluate the framework on the MMOTU ovarian ultrasound dataset with dataset-specific training. SGFA achieves a patient-mean Dice of 0.9548 (frame-mean: 0.9563) on EUS and an image-mean Dice of 0.9642 on MMOTU. Patient-level EUS ablations show gains from STP adaptation under global-box prompting and from jointly adapting STP and the spatial gate under QBBP.