Data Sheet 1_RTS-Net: thyroid nodule segmentation network integrating dual-path attention and graph convolution.zip

IntroductionThyroid 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.
MethodsWe 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.
ResultsExtensive 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.
DiscussionThe 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.

引言：甲状腺超声是结节检测的首选成像模态，但由于斑点噪声、对比度偏低以及操作者依赖性问题，人工阅片存在主观性强、效率低下的缺陷。基于深度学习的分割方法往往忽略解剖学先验信息，导致在非典型结节与复杂背景下的分割表现欠佳。
方法：本文提出RTS-Net，一种新型分割网络，其整合了双路径注意力增强机制（结合空间注意力与通道注意力）与级联图卷积解码架构，以实现多尺度特征金字塔融合；同时采用深度监督策略以加速模型收敛。本模型在TN3K、DDTI以及一个大规模临床数据集上开展训练与评估。
结果：大量实验表明，RTS-Net在分布内与跨数据集场景下均取得了优异性能。在TN3K数据集上，其F1分数达81.66%，交并比（IoU，Intersection over Union）为71.87%；在DDTI数据集上，F1分数达71.10%，交并比为60.09%，性能优于当前最优方法，包括UNet、DeepLabv3+、TransUNet以及近期基于基础模型的相关方法。消融实验验证了所提出的各组件的有效性。
讨论：所提出的双路径注意力模块与图卷积模块可有效增强特征表征能力与边界完整性，尤其针对小结节与边缘模糊的结节。尽管RTS-Net展现出较强的泛化能力，但失败案例表明，其在异质性背景与声学伪影场景下仍存在挑战，未来可考虑与SAM（Segment Anything Model）等基础模型进行集成，以进一步提升模型鲁棒性。