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With the increasing detection of thyroid nodules in ultrasound screening, accurate nodule segmentation has become important for computer-aided assessment of clinically relevant features, such as contour regularity, aspect ratio, and margin sharpness. Although ultrasound is widely used as a first-line imaging modality in clinical practice, thyroid nodule segmentation remains challenging because of low tissue contrast, speckle-blurred boundaries, and large variations in nodule size and morphology. To address these challenges, we propose TEM-Net, a Tri-Channel Edge-Aware Multi-Scale Network for thyroid nodule segmentation. TEM-Net constructs a tri-channel representation from the raw ultrasound image, including the original grayscale image, a contrast-enhanced image, and a gradient-magnitude map, to highlight weak intensity differences and boundary-related cues in low-contrast ultrasound images. An Edge-Guided Feature Amplification (EGFA) module is introduced before the first down-sampling operation to emphasize boundary responses before spatial resolution is reduced. In addition, a Multi-Focus Cross-Scale Attention Refinement (MF-CAR) module is embedded into skip connections, combining dilated depth-wise convolutions with channel–spatial attention to improve the fusion of local boundary details and broader contextual information. Across three random seeds, TEM-Net achieves mean Dice scores of 0.8822 and 0.9066 and mean IoU scores of 0.7893 and 0.8291 on TN3K and DDTI, respectively, showing competitive performance compared with representative segmentation methods.
Peng et al. (Wed,) studied this question.