Semantic-Aware Deformable Feature Alignment for Thyroid Nodule Segmentation in Ultrasound

Abstract

Accurate segmentation of thyroid nodules in ultrasound images is essential for clinical diagnosis yet remains challenging due to boundary ambiguity, speckle noise, and complex geometric variability. Existing methods often fail to reconcile global semantic understanding with precise local boundary delineation, leading to errors in irregular cases. To address this, we propose SDFA-Net, a framework built on semantic-aware deformable feature alignment that explicitly decouples the learning of robust global semantics and detailed local geometry. Our approach integrates three main components: a clustered linear context aggregation module for efficient long-range dependency modeling, an affinity-driven token modulation mechanism using dynamic prototypes to suppress background interference, and a cascaded spatially-aligned feature fusion strategy that achieves sub-pixel geometric alignment across multiple resolution stages. By filtering and aligning multi-scale features, SDFA-Net ensures robust fusion that is both semantically meaningful and geometrically accurate. We validate our method through extensive experiments on three public thyroid ultrasound datasets, TN3K, TUCC, and DDTI, where it demonstrates superior performance over state-of-the-art methods. The proposed framework offers a generalizable solution for precise medical image segmentation in boundary-ambiguous scenarios.