SGDC：面向医学图像分割的结构引导动态卷积

摘要

空间可变动态卷积为将空间自适应性融入深度神经网络提供了原理性方法。然而医学分割领域的主流设计通常通过平均池化生成动态卷积核，这种做法会隐式地将高频空间细节压缩为粗糙的空间紧凑表示，导致预测结果过度平滑，从而降低细粒度临床结构的分割保真度。为解决这一局限，我们提出了一种新颖的结构引导动态卷积（SGDC）机制，该机制通过显式监督的结构提取分支来引导动态卷积核和门控信号的生成，实现结构感知的特征调制。具体而言，该辅助分支提取的高保真边界信息将与语义特征融合，以实现空间精确的特征调制。通过用像素级结构引导替代上下文聚合，所提设计有效避免了平均池化引起的信息损失。实验结果表明，SGDC在ISIC 2016、PH2、ISIC 2018和CoNIC数据集上实现了最先进性能：通过将豪斯多夫距离（HD95）降低2.05个点显著提升边界保真度，并在基于池化的基线模型上获得0.99%-1.49%的稳定交并比提升。此外，该机制展现出扩展至其他细粒度、结构敏感视觉任务（如小目标检测）的强大潜力，为医学图像分析中的结构完整性保持提供了原理性解决方案。为促进可复现性并推动后续研究，我们的SGE和SGDC模块实现代码已公开于https://github.com/solstice0621/SGDC。

English

Spatially variant dynamic convolution provides a principled approach of integrating spatial adaptivity into deep neural networks. However, mainstream designs in medical segmentation commonly generate dynamic kernels through average pooling, which implicitly collapses high-frequency spatial details into a coarse, spatially-compressed representation, leading to over-smoothed predictions that degrade the fidelity of fine-grained clinical structures. To address this limitation, we propose a novel Structure-Guided Dynamic Convolution (SGDC) mechanism, which leverages an explicitly supervised structure-extraction branch to guide the generation of dynamic kernels and gating signals for structure-aware feature modulation. Specifically, the high-fidelity boundary information from this auxiliary branch is fused with semantic features to enable spatially-precise feature modulation. By replacing context aggregation with pixel-wise structural guidance, the proposed design effectively prevents the information loss introduced by average pooling. Experimental results show that SGDC achieves state-of-the-art performance on ISIC 2016, PH2, ISIC 2018, and CoNIC datasets, delivering superior boundary fidelity by reducing the Hausdorff Distance (HD95) by 2.05, and providing consistent IoU gains of 0.99\%-1.49\% over pooling-based baselines. Moreover, the mechanism exhibits strong potential for extension to other fine-grained, structure-sensitive vision tasks, such as small-object detection, offering a principled solution for preserving structural integrity in medical image analysis. To facilitate reproducibility and encourage further research, the implementation code for both our SGE and SGDC modules has been is publicly released at https://github.com/solstice0621/SGDC.

SGDC：面向医学图像分割的结构引导动态卷积

SGDC: Structurally-Guided Dynamic Convolution for Medical Image Segmentation

摘要

Support