Retinal Vessel Segmentation with Deep Graph and Capsule Reasoning

TL;DR

This document describes elsarticle.cls, a LaTeX document class tailored for Elsevier journal submissions that minimizes package conflicts by building on article.cls and leveraging standard packages such as natbib, geometry, and hyperref. It explains the key design differences from the previous elsart.cls, including improved compatibility, preprint and final formatting modes, and integrated front matter handling. The installation guidance covers obtaining the package from Elsevier resources or CTAN, building the class from the dtx/ins files, and deploying it within the TeXMF tree. The class standardizes formatting for Elsevier journals, streamlines frontmatter and float management, and supports a broad set of options to match submission and publication styles.

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.