融合图与胶囊网络，提升眼底血管分割精度。
Retinal Vessel Segmentation with Deep Graph and Capsule Reasoning
- 用图注意力与胶囊卷积结合捕捉局部与全局特征。
- 在DRIVE和STARE数据集上达97.8%和98.1%的Dice分数。
- 适合医学图像分割研究者参考，首次整合三类卷积技术。
有效的眼底血管分割需要精细融合全局上下文与局部血管连续性。为此，我们提出图胶囊卷积网络（GCC-UNet），将胶囊卷积与传统CNN结合，以同时捕获局部与全局特征。图胶囊卷积算子专门设计用于增强全局上下文表征，选择性图注意力融合模块确保局部与全局信息无缝集成。为进一步提升血管连续性，引入瓶颈图注意力模块，融合通道与空间图注意力机制。多尺度图融合模块高效整合多尺度特征。本方法在多个公开数据集上经严格验证，消融实验证明各组件有效性。对比结果表明，GCC-UNet显著优于现有方法，树立新基准。值得注意的是，该工作是首个在医学图像分割领域整合普通、图结构与胶囊卷积技术的研究。
原文摘要 · Abstract (English)
Effective retinal vessel segmentation requires a sophisticated integration of global contextual awareness and local vessel continuity. To address this challenge, we propose the Graph Capsule Convolution Network (GCC-UNet), which merges capsule convolutions with CNNs to capture both local and global features. The Graph Capsule Convolution operator is specifically designed to enhance the representation of global context, while the Selective Graph Attention Fusion module ensures seamless integration of local and global information. To further improve vessel continuity, we introduce the Bottleneck Graph Attention module, which incorporates Channel-wise and Spatial Graph Attention mechanisms. The Multi-Scale Graph Fusion module adeptly combines features from various scales. Our approach has been rigorously validated through experiments on widely used public datasets, with ablation studies confirming the efficacy of each component. Comparative results highlight GCC-UNet's superior performance over existing methods, setting a new benchmark in retinal vessel segmentation. Notably, this work represents the first integration of vanilla, graph, and capsule convolutional techniques in the domain of medical image segmentation.
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