In medical image segmentation, skip connections are used to merge global context and reduce the semantic gap between encoder and decoder. Current methods often struggle with limited structural representation and insufficient contextual modeling, affecting generalization in complex clinical scenarios. We propose the DTEA model, featuring a new skip connection framework with the Semantic Topology Reconfiguration (STR) and Entropic Perturbation Gating (EPG) modules. STR reorganizes multi-scale semantic features into a dynamic hypergraph to better model cross-resolution anatomical dependencies, enhancing structural and semantic representation. EPG assesses channel stability after perturbation and filters high-entropy channels to emphasize clinically important regions and improve spatial attention. Extensive experiments on three benchmark datasets show our framework achieves superior segmentation accuracy and better generalization across various clinical settings. The code is available at \href{https://github.com/LWX-Research/DTEA}{https://github.com/LWX-Research/DTEA}.

翻译：在医学图像分割中，跳跃连接用于融合全局上下文并减少编码器与解码器之间的语义鸿沟。现有方法常受限于结构表征能力不足和上下文建模不充分，影响了在复杂临床场景中的泛化性能。我们提出了DTEA模型，其采用一种新颖的跳跃连接框架，包含语义拓扑重构（STR）模块和熵扰动门控（EPG）模块。STR将多尺度语义特征重组为动态超图，以更好地建模跨分辨率解剖结构依赖关系，从而增强结构和语义表征能力。EPG通过评估扰动后通道的稳定性，过滤高熵通道，以强调临床重要区域并改善空间注意力机制。在三个基准数据集上的大量实验表明，我们的框架实现了卓越的分割精度，并在多种临床场景中表现出更好的泛化能力。代码发布于 \href{https://github.com/LWX-Research/DTEA}{https://github.com/LWX-Research/DTEA}。