RTS-Net: thyroid nodule segmentation network integrating dual-path attention and graph convolution

Thyroid ultrasound is the primary imaging modality for nodule detection, but manual interpretation suffers from subjectivity and inefficiency due to speckle noise, low contrast, and operator dependence. Deep learning-based segmentation methods often overlook anatomical prior information, leading to suboptimal performance on atypical nodules and complex backgrounds.

We propose RTS-Net, a novel segmentation network that integrates a dual-path attention enhancement mechanism (combining spatial and channel attention) and a cascaded graph convolution decoding architecture to leverage multi-scale feature pyramid fusion. A deep supervision strategy is also employed to accelerate convergence. The model is trained and evaluated on the TN3K, DDTI, and a large-scale clinical dataset.

Extensive experiments demonstrate that RTS-Net achieves superior performance on both in-distribution and cross-dataset settings. On the TN3K dataset, it attains 81.66% F1-score and 71.87% IoU; on the DDTI dataset, it achieves 71.10% F1-score and 60.09% IoU, outperforming state-of-the-art methods including UNet, DeepLabv3+, TransUNet, and recent foundation-model-based approaches. Ablation studies confirm the effectiveness of each proposed component.

The proposed dual-path attention and graph convolution modules effectively enhance feature representation and boundary integrity, particularly for small nodules and blurred edges. While RTS-Net shows strong generalization, failure cases reveal challenges in heterogeneous backgrounds and acoustic artifacts, suggesting future integration with foundation models like SAM to further improve robustness.