# Automated airway segmentation by learning graphical structure

arXiv: https://arxiv.org/abs/2109.14792 · HTML: https://mlsanity.com/paper/2109.14792/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 23 references that are in the map.
- First posted on arXiv 30 Sep 2021.

## Builds on (most cited first, 9 of 9)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [A guide to convolution arithmetic for deep learning](https://mlsanity.com/paper/1603.07285/) (2016)
- [Deep Vessel Segmentation By Learning Graphical Connectivity](https://mlsanity.com/paper/1806.02279/) (2018)
- [Deep Retinal Image Understanding](https://mlsanity.com/paper/1609.01103/) (2016)
- [Automatic Airway Segmentation in chest CT using Convolutional Neural Networks](https://mlsanity.com/paper/1808.04576/) (2018)
- [Mean Field Network based Graph Refinement with application to Airway Tree Extraction](https://mlsanity.com/paper/1804.03348/) (2018)

## Abstract (arXiv)

In this research project, we put forward an advanced method for airway segmentation based on the existent convolutional neural network (CNN) and graph neural network (GNN). The method is originated from the vessel segmentation, but we ameliorate it and enable the novel model to perform better for datasets from computed tomography (CT) scans. Current methods for airway segmentation are considering the regular grid only. No matter what the detailed model is, including the 3-dimensional CNN or 2-dimensional CNN in three directions, the overall graph structures are not taken into consideration. In our model, with the neighbourhoods of airway taken into account, the graph structure is incorporated and the segmentation of airways are improved compared with the traditional CNN methods. We perform experiments on the chest CT scans, where the ground truth segmentation labels are produced manually. The proposed model shows that compared with the CNN-only method, the combination of CNN and GNN has a better performance in that the bronchi in the chest CT scans can be detected in most cases. In addition, the model we propose has a wide extension since the architecture is also utilitarian in fulfilling similar aims in other datasets. Hence, the state-of-the-art model is of great significance and highly applicable in our daily lives. Keywords: Airway segmentation, Convolutional neural network, Graph neural network