基于深度图与胶囊推理的视网膜血管分割
Retinal Vessel Segmentation with Deep Graph and Capsule Reasoning
AI总结：
本研究提出融合胶囊卷积与CNN的GCC-UNet网络，通过多种图注意力与融合模块兼顾全局上下文与局部血管连续性，在公开数据集上表现优于现有方法，首次整合三类卷积技术应用于医学图像分割。
摘要：
有效的视网膜血管分割需要复杂整合全局上下文感知与局部血管连续性。为解决这一挑战，我们提出图胶囊卷积网络（Graph Capsule Convolution Network, GCC-UNet），该网络将胶囊卷积与CNN（卷积神经网络）融合，以同时捕获局部和全局特征。图胶囊卷积算子专为增强全局上下文表征而设计，而选择性图注意力融合模块则确保局部与全局信息的无缝整合。为进一步提升血管连续性，我们引入瓶颈图注意力模块，该模块整合了通道与空间图注意力机制。多尺度图融合模块可巧妙结合不同尺度的特征。我们的方法已在广泛使用的公开数据集上通过实验得到严格验证，消融研究证实了各组件的有效性。对比结果凸显了GCC-UNet相较于现有方法的优越性能，为视网膜血管分割设立了新基准。值得注意的是，这项工作首次在医学图像分割领域整合了普通卷积、图卷积和胶囊卷积技术。
英文摘要：
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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