# Graph Convolutional Networks for Coronary Artery Segmentation in Cardiac CT Angiography

arXiv: https://arxiv.org/abs/1908.05343 · HTML: https://mlsanity.com/paper/1908.05343/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Aug 2019.

## Built on by (most cited first, 18 of 18)

- [Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future](https://mlsanity.com/paper/2105.13137/) (2021)
- [A Survey on Graph Neural Networks and Graph Transformers in Computer Vision: A Task-Oriented Perspective](https://mlsanity.com/paper/2209.13232/) (2022)
- [ImageCAS: A Large-Scale Dataset and Benchmark for Coronary Artery Segmentation based on Computed Tomography Angiography Images](https://mlsanity.com/paper/2211.01607/) (2022)
- [Mesh Neural Networks for SE(3)-Equivariant Hemodynamics Estimation on the Artery Wall](https://mlsanity.com/paper/2212.05023/) (2022)
- [Segmentation and Vascular Vectorization for Coronary Artery by Geometry-based Cascaded Neural Network](https://mlsanity.com/paper/2305.04208/) (2023)
- [Deep Learning-Based Carotid Artery Vessel Wall Segmentation in Black-Blood MRI Using Anatomical Priors](https://mlsanity.com/paper/2112.01137/) (2021)
- [Mesh convolutional neural networks for wall shear stress estimation in 3D artery models](https://mlsanity.com/paper/2109.04797/) (2021)
- [U-GAT: Multimodal Graph Attention Network for COVID-19 Outcome Prediction](https://mlsanity.com/paper/2108.00860/) (2021)
- [A topology-preserving three-stage framework for fully-connected coronary artery extraction](https://mlsanity.com/paper/2504.01597/) (2025)
- [HarmonySeg: Tubular Structure Segmentation with Deep-Shallow Feature Fusion and Growth-Suppression Balanced Loss](https://mlsanity.com/paper/2504.07827/) (2025)
- [ConvNeXtv2 Fusion with Mask R-CNN for Automatic Region Based Coronary Artery Stenosis Detection for Disease Diagnosis](https://mlsanity.com/paper/2310.04749/) (2023)
- [Deep Learning for Vascular Segmentation and Applications in Phase Contrast Tomography Imaging](https://mlsanity.com/paper/2311.13319/) (2023)
- [SIRE: scale-invariant, rotation-equivariant estimation of artery orientations using graph neural networks](https://mlsanity.com/paper/2311.05400/) (2023)
- [Global Control for Local SO(3)-Equivariant Scale-Invariant Vessel Segmentation](https://mlsanity.com/paper/2403.15314/) (2024)
- [Automated Coronary Arteries Labeling Via Geometric Deep Learning](https://mlsanity.com/paper/2212.00386/) (2022)
- [Deep Learning-Based Prediction of Fractional Flow Reserve along the Coronary Artery](https://mlsanity.com/paper/2308.04923/) (2023)
- [Leveraging Frequency Domain Learning in 3D Vessel Segmentation](https://mlsanity.com/paper/2401.06224/) (2024)
- [Multi-View Deformable Convolution Meets Visual Mamba for Coronary Artery Segmentation](https://mlsanity.com/paper/2603.21829/) (2026)

## Abstract (arXiv)

Detection of coronary artery stenosis in coronary CT angiography (CCTA) requires highly personalized surface meshes enclosing the coronary lumen. In this work, we propose to use graph convolutional networks (GCNs) to predict the spatial location of vertices in a tubular surface mesh that segments the coronary artery lumen. Predictions for individual vertex locations are based on local image features as well as on features of neighboring vertices in the mesh graph. The method was trained and evaluated using the publicly available Coronary Artery Stenoses Detection and Quantification Evaluation Framework. Surface meshes enclosing the full coronary artery tree were automatically extracted. A quantitative evaluation on 78 coronary artery segments showed that these meshes corresponded closely to reference annotations, with a Dice similarity coefficient of 0.75/0.73, a mean surface distance of 0.25/0.28 mm, and a Hausdorff distance of 1.53/1.86 mm in healthy/diseased vessel segments. The results showed that inclusion of mesh information in a GCN improves segmentation overlap and accuracy over a baseline model without interaction on the mesh. The results indicate that GCNs allow efficient extraction of coronary artery surface meshes and that the use of GCNs leads to regular and more accurate meshes.