Retinal Vessel Segmentation with Deep Graph and Capsule Reasoning

TL;DR

This paper introduces GCC-UNet, a novel deep learning architecture that combines graph, capsule, and convolutional techniques to improve retinal vessel segmentation by capturing both local and global features effectively.

Contribution

It presents the first integration of vanilla, graph, and capsule convolutions in medical image segmentation, enhancing vessel continuity and global context understanding.

Findings

Outperforms existing methods on public datasets

Demonstrates the effectiveness of each component through ablation studies

Sets a new benchmark in retinal vessel segmentation

Abstract

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.