DPF-EHDNet: a differential-path and structurally enhanced network for thyroid ultrasound segmentation

Accurate segmentation of thyroid nodules in ultrasound remains challenging due to speckle noise, low-contrast lesion margins, heterogeneous echogenicity, and substantial inter-case variability. Reliable delineation is essential for supporting clinical assessment and downstream computer-assisted diagnosis. We propose DPF-EHDNet, a differential-path and structurally enhanced segmentation framework tailored to challenging ultrasound conditions. The network integrates differential-path feature enhancement to suppress speckle-induced ambiguity, edge-aware multi-scale context encoding to preserve anatomical structure, and confidence-guided shallow feature fusion to improve localization consistency. Our model is trained under a fixed-iteration protocol without validation-based checkpoint selection to ensure fair comparison across methods, and its performance is reported as the average over three random seeds. Experiments on a thyroid ultrasound dataset constructed from three publicly available benchmarks (DDTI, TN-SCUI2020, and TN3K) show that DPF-EHDNet achieves 93.09% mIoU, 93.35% Dice, 93.98% precision, and 92.73% recall, consistently outperforming representative baseline segmentation approaches. DPF-EHDNet provides a robust and structurally consistent solution for thyroid ultrasound segmentation under speckle degradation and low-contrast conditions, demonstrating its potential to support clinical workflows and computer-assisted ultrasound analysis.