# A joint 3D UNet-Graph Neural Network-based method for Airway Segmentation from chest CTs

arXiv: https://arxiv.org/abs/1908.08588 · HTML: https://mlsanity.com/paper/1908.08588/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Aug 2019.

## Built on by (most cited first, 27 of 27)

- [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)
- [Multimodal Data Integration for Oncology in the Era of Deep Neural Networks: A Review](https://mlsanity.com/paper/2303.06471/) (2023)
- [Automatic airway segmentation from Computed Tomography using robust and efficient 3-D convolutional neural networks](https://mlsanity.com/paper/2103.16328/) (2021)
- [NaviAirway: a Bronchiole-sensitive Deep Learning-based Airway Segmentation Pipeline](https://mlsanity.com/paper/2203.04294/) (2022)
- [Human Treelike Tubular Structure Segmentation: A Comprehensive Review and Future Perspectives](https://mlsanity.com/paper/2207.11203/) (2022)
- [Two-stage Contextual Transformer-based Convolutional Neural Network for Airway Extraction from CT Images](https://mlsanity.com/paper/2212.07651/) (2022)
- [AeroPath: An airway segmentation benchmark dataset with challenging pathology](https://mlsanity.com/paper/2311.01138/) (2023)
- [Multi-site, Multi-domain Airway Tree Modeling (ATM'22): A Public Benchmark for Pulmonary Airway Segmentation](https://mlsanity.com/paper/2303.05745/) (2023)
- [Single Neuron Segmentation using Graph-based Global Reasoning with Auxiliary Skeleton Loss from 3D Optical Microscope Images](https://mlsanity.com/paper/2101.08910/) (2021)
- [Accurate Airway Tree Segmentation in CT Scans via Anatomy-aware Multi-class Segmentation and Topology-guided Iterative Learning](https://mlsanity.com/paper/2306.09116/) (2023)
- [Structure and position-aware graph neural network for airway labeling](https://mlsanity.com/paper/2201.04532/) (2022)
- [LTSP: Long-Term Slice Propagation for Accurate Airway Segmentation](https://mlsanity.com/paper/2202.06260/) (2022)
- [BCNet: Bronchus Classification via Structure Guided Representation Learning](https://mlsanity.com/paper/2205.06947/) (2022)
- [GDDS: Pulmonary Bronchioles Segmentation with Group Deep Dense Supervision](https://mlsanity.com/paper/2303.09212/) (2023)
- [HarmonySeg: Tubular Structure Segmentation with Deep-Shallow Feature Fusion and Growth-Suppression Balanced Loss](https://mlsanity.com/paper/2504.07827/) (2025)
- [Differentiable Topology-Preserved Distance Transform for Pulmonary Airway Segmentation](https://mlsanity.com/paper/2209.08355/) (2022)
- [A Review of Modern Approaches for Coronary Angiography Imaging Analysis](https://mlsanity.com/paper/2209.13997/) (2022)
- [Robust Tumor Segmentation with Hyperspectral Imaging and Graph Neural Networks](https://mlsanity.com/paper/2311.11782/) (2023)
- [A Graphical Approach For Brain Haemorrhage Segmentation](https://mlsanity.com/paper/2202.06876/) (2022)
- [3D PETCT Tumor Lesion Segmentation via GCN Refinement](https://mlsanity.com/paper/2302.12571/) (2023)
- [Validated respiratory drug deposition predictions from 2D and 3D medical images with statistical shape models and convolutional neural networks](https://mlsanity.com/paper/2303.01036/) (2023)
- [Interpolation-Split: a data-centric deep learning approach with big interpolated data to boost airway segmentation performance](https://mlsanity.com/paper/2308.00008/) (2023)
- [ViG3D-UNet: Volumetric Vascular Connectivity-Aware Segmentation via 3D Vision Graph Representation](https://mlsanity.com/paper/2504.13599/) (2025)
- [iPac: Incorporating Intra-image Patch Context into Graph Neural Networks for Medical Image Classification](https://mlsanity.com/paper/2510.23504/) (2025)
- [Learning Topology-Aware Implicit Field for Unified Pulmonary Tree Modeling with Incomplete Topological Supervision](https://mlsanity.com/paper/2602.02186/) (2026)
- [Virtual Nodes Guided Dynamic Graph Neural Network for Brain Tumor Segmentation with Missing Modalities](https://mlsanity.com/paper/2605.16880/) (2026)

## Abstract (arXiv)

We present an end-to-end deep learning segmentation method by combining a 3D UNet architecture with a graph neural network (GNN) model. In this approach, the convolutional layers at the deepest level of the UNet are replaced by a GNN-based module with a series of graph convolutions. The dense feature maps at this level are transformed into a graph input to the GNN module. The incorporation of graph convolutions in the UNet provides nodes in the graph with information that is based on node connectivity, in addition to the local features learnt through the downsampled paths. This information can help improve segmentation decisions. By stacking several graph convolution layers, the nodes can access higher order neighbourhood information without substantial increase in computational expense. We propose two types of node connectivity in the graph adjacency: i) one predefined and based on a regular node neighbourhood, and ii) one dynamically computed during training and using the nearest neighbour nodes in the feature space. We have applied this method to the task of segmenting the airway tree from chest CT scans. Experiments have been performed on 32 CTs from the Danish Lung Cancer Screening Trial dataset. We evaluate the performance of the UNet-GNN models with two types of graph adjacency and compare it with the baseline UNet.