# Retinal Vessel Segmentation with Deep Graph and Capsule Reasoning

arXiv: https://arxiv.org/abs/2409.11508 · HTML: https://mlsanity.com/paper/2409.11508/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 34 of its 66 references that are in the map.
- First posted on arXiv 17 Sep 2024.

## Builds on (most cited first, 30 of 34)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Non-local Neural Networks](https://mlsanity.com/paper/1711.07971/) (2017)
- [Attention U-Net: Learning Where to Look for the Pancreas](https://mlsanity.com/paper/1804.03999/) (2018)
- [Multi-Scale Context Aggregation by Dilated Convolutions](https://mlsanity.com/paper/1511.07122/) (2015)
- [Dynamic Routing Between Capsules](https://mlsanity.com/paper/1710.09829/) (2017)
- [Holistically-Nested Edge Detection](https://mlsanity.com/paper/1504.06375/) (2015)
- [Selective Kernel Networks](https://mlsanity.com/paper/1903.06586/) (2019)
- [Position-aware Graph Neural Networks](https://mlsanity.com/paper/1906.04817/) (2019)
- [DUNet: A deformable network for retinal vessel segmentation](https://mlsanity.com/paper/1811.01206/) (2018)
- [Attention-Driven Dynamic Graph Convolutional Network for Multi-Label Image Recognition](https://mlsanity.com/paper/2012.02994/) (2020)
- [Directed Graph Convolutional Network](https://mlsanity.com/paper/2004.13970/) (2020)
- [SA-UNet: Spatial Attention U-Net for Retinal Vessel Segmentation](https://mlsanity.com/paper/2004.03696/) (2020)
- [Dual Graph Convolutional Network for Semantic Segmentation](https://mlsanity.com/paper/1909.06121/) (2019)
- [Applications of Deep Learning in Fundus Images: A Review](https://mlsanity.com/paper/2101.09864/) (2021)
- [Deep Vessel Segmentation By Learning Graphical Connectivity](https://mlsanity.com/paper/1806.02279/) (2018)
- [Efficient-CapsNet: Capsule Network with Self-Attention Routing](https://mlsanity.com/paper/2101.12491/) (2021)
- [Deep Retinal Image Understanding](https://mlsanity.com/paper/1609.01103/) (2016)
- [Graph Capsule Convolutional Neural Networks](https://mlsanity.com/paper/1805.08090/) (2018)
- [Retinal vessel segmentation based on Fully Convolutional Neural Networks](https://mlsanity.com/paper/1812.07110/) (2018)
- [Capsules for Biomedical Image Segmentation](https://mlsanity.com/paper/2004.04736/) (2020)
- [Retinal Vessel Segmentation in Fundoscopic Images with Generative Adversarial Networks](https://mlsanity.com/paper/1706.09318/) (2017)
- [Attention routing between capsules](https://mlsanity.com/paper/1907.01750/) (2019)
- [Deep Retinal Image Segmentation with Regularization Under Geometric Priors](https://mlsanity.com/paper/1909.09175/) (2019)
- [Robust Retinal Vessel Segmentation from a Data Augmentation Perspective](https://mlsanity.com/paper/2007.15883/) (2020)
- [Dynamic Deep Networks for Retinal Vessel Segmentation](https://mlsanity.com/paper/1903.07803/) (2019)

## Built on by (most cited first, 2 of 2)

- [VPBSD:Vessel-Pattern-Based Semi-Supervised Distillation for Efficient 3D Microscopic Cerebrovascular Segmentation](https://mlsanity.com/paper/2411.09567/) (2024)
- [From Reconstruction to Decision: A Post-Encoder Plug-in Adapter for Curvilinear Segmentation](https://mlsanity.com/paper/2606.23486/) (2026)

## Abstract (arXiv)

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.