Abstract: To address challenges in thyroid ultrasound nodule segmentation, including blurred boundaries, low contrast, and highly variable small lesions, this paper proposes an improved model named MAD-UNet. The model improves contour delineation by strengthening cross-layer feature transfer consistency and deformable context modeling. A Multi-Directional Separable Attention Module (MDSAM) is embedded in the skip connections between the encoder and the decoder. MDSAM applies direction-aware channel–spatial joint attention to reweight key edge responses. This design enhances the consistency between shallow spatial details and deep semantic information. It strengthens boundary localization and alleviates gradient attenuation during deep network training. In addition, the Transformer encoder depth is extended to 24 layers to better model long-range dependencies and global context. Furthermore, a Deformable Adaptive Multi-Scale Context Module (DAMCM) is introduced. DAMCM combines deformable modeling with multi-scale context aggregation. It enables adaptive fusion of local structure alignment and global context supplementation. It improves representation of irregular contours and small targets. On the TN3K, DDTI, and Shanghai Sixth People's Hospital THN-L datasets, the Dice scores reach 89.10%、90.53% and 91.17%, respectively. The overall performance exceeds the TransUNet baseline on all datasets. Complexity evaluation shows 215.27M parameters, 65.96G FLOPs, and an inference speed of 111 FPS. Visualization analysis shows stronger robustness for nodule contours under complex ultrasound conditions. The experimental results verify the effectiveness of the model in fine boundary delineation and small lesion recognition. The method provides a basis for subsequent deployment and optimization in clinical application scenarios.

摘要： ：针对甲状腺超声结节分割中边界模糊、对比度低及小体积多变等难题，提出改进模型MAD-UNet，通过强化跨层特征传递一致性与形变上下文建模提升轮廓刻画能力。在编码器与解码器的跳跃连接处嵌入多方向可分离注意力模块（Multi-Directional Separable Attention Module，MDSAM），通过方向感知的通道—空间联合注意力对关键边缘响应进行重加权，增强浅层空间细节与深层语义信息的一致性，从而强化边界定位并缓解深层网络训练中的梯度衰减问题。其次，将Transformer编码器深度扩展至24层，以更充分地建模长程依赖与全局上下文。进一步地，引入形变自适应多尺度上下文模块（Deformable Adaptive Multi-Scale Context Module，DAMCM），结合形变建模与多尺度上下文聚合，实现局部结构对齐与全局语境补充的自适应融合，增强对不规则轮廓与细小目标的表达能力。模型在TN3K、DDTI与上海第六人民医院THN-L数据集上的Dice系数分别达到89.10%、90.53%和91.17%。整体性能均优于TransUNet基线；复杂度评估显示，模型参数量为215.27M、浮点运算量（floating-point operations，FLOPs）为65.96G、推理速度为111帧每秒（frames per second，FPS）。可视化分析显示在复杂超声条件下对结节轮廓具有更强鲁棒性。实验结果验证了该模型在精细边界刻画与小病灶识别方面的有效性，为后续面向临床应用场景的部署与优化提供了方法基础。