Intelligent segmentation of thyroid nodule ultrasound images based on benign-malignant-aware semantic prototype calibration and class-conditional boundary refinement.

INTRODUCTION: Ultrasound-based thyroid nodule segmentation remains challenging because of blurred boundaries, complex echo noise, and subtle morphological differences between benign and malignant lesions. METHODS: We propose a benign-malignant-aware segmentation framework based on SegFormer, incorporating adaptive semantic prototype calibration (ASPC) for class-level semantic discrimination and class-conditional boundary refinement (CCBR) for ambiguous contour correction. The model performs three-class segmentation of background, benign, and malignant regions. RESULTS: Experiments on the public TN3K dataset and an in-house clinical dataset demonstrate consistent performance under both external transfer and supervised evaluation settings. The proposed method achieved PA, mIoU, mDice, and mPrecision of 0.9449, 0.6427, 0.7517, and 0.8147 on TN3K; 0.7793, 0.5016, 0.6374, and 0.7089 under direct external transfer; and 0.8521, 0.6124, 0.7328, and 0.7996 under supervised in-house evaluation. DISCUSSION: Ablation studies and Grad-CAM visualization further confirm that ASPC and CCBR improve semantic discrimination, boundary refinement, and model interpretability, supporting the effectiveness of the proposed framework for thyroid nodule segmentation.