动态拓扑重构与熵衰减机制提升医学图像分割精度
DTEA: Dynamic Topology Weaving and Instability-Driven Entropic Attenuation for Medical Image Segmentation
- 构建动态超图融合多尺度语义特征，增强解剖结构建模
- 在三个数据集上平均Dice达94.3%，显著优于现有方法
- 适合临床复杂场景下需要高精度分割的医学图像任务
在医学图像分割中，跳跃连接用于融合全局上下文并缩小编码器与解码器之间的语义差距。当前方法常受限于结构表征能力不足和上下文建模不充分，影响复杂临床场景下的泛化性能。本文提出DTEA模型，包含新的跳跃连接框架，集成语义拓扑重配置（STR）与熵扰动门控（EPG）模块。STR将多尺度语义特征重新组织为动态超图，以更好建模跨分辨率解剖依赖，增强结构与语义表征；EPG通过扰动后通道稳定性评估，过滤高熵通道，强化临床关键区域的空间注意力。在三个基准数据集上的大量实验表明，该框架实现了更优的分割精度与跨临床场景的更好泛化能力。代码已公开于https://github.com/LWX-Research/DTEA。
原文摘要 · Abstract (English)
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}.
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