# AirwayNet: A Voxel-Connectivity Aware Approach for Accurate Airway Segmentation Using Convolutional Neural Networks

arXiv: https://arxiv.org/abs/1907.06852 · HTML: https://mlsanity.com/paper/1907.06852/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jul 2019.

## Built on by (most cited first, 30 of 32)

- [3D MedDiffusion: A 3D Medical Latent Diffusion Model for Controllable and High-quality Medical Image Generation](https://mlsanity.com/paper/2412.13059/) (2024)
- [Directional Connectivity-based Segmentation of Medical Images](https://mlsanity.com/paper/2304.00145/) (2023)
- [Automatic airway segmentation from Computed Tomography using robust and efficient 3-D convolutional neural networks](https://mlsanity.com/paper/2103.16328/) (2021)
- [Fuzzy Attention Neural Network to Tackle Discontinuity in Airway Segmentation](https://mlsanity.com/paper/2209.02048/) (2022)
- [NaviAirway: a Bronchiole-sensitive Deep Learning-based Airway Segmentation Pipeline](https://mlsanity.com/paper/2203.04294/) (2022)
- [BREAK: Bronchi Reconstruction by gEodesic transformation And sKeleton embedding](https://mlsanity.com/paper/2202.00002/) (2022)
- [Human Treelike Tubular Structure Segmentation: A Comprehensive Review and Future Perspectives](https://mlsanity.com/paper/2207.11203/) (2022)
- [FDA: Feature Decomposition and Aggregation for Robust Airway Segmentation](https://mlsanity.com/paper/2109.02920/) (2021)
- [Hunting imaging biomarkers in pulmonary fibrosis: Benchmarks of the AIIB23 challenge](https://mlsanity.com/paper/2312.13752/) (2023)
- [Two-stage Contextual Transformer-based Convolutional Neural Network for Airway Extraction from CT Images](https://mlsanity.com/paper/2212.07651/) (2022)
- [Multi-site, Multi-domain Airway Tree Modeling (ATM'22): A Public Benchmark for Pulmonary Airway Segmentation](https://mlsanity.com/paper/2303.05745/) (2023)
- [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)
- [LTSP: Long-Term Slice Propagation for Accurate Airway Segmentation](https://mlsanity.com/paper/2202.06260/) (2022)
- [Efficient Anatomical Labeling of Pulmonary Tree Structures via Deep Point-Graph Representation-based Implicit Fields](https://mlsanity.com/paper/2309.17329/) (2023)
- [Intensity Confusion Matters: An Intensity-Distance Guided Loss for Bronchus Segmentation](https://mlsanity.com/paper/2406.16150/) (2024)
- [BCNet: Bronchus Classification via Structure Guided Representation Learning](https://mlsanity.com/paper/2205.06947/) (2022)
- [Coarse-to-fine Airway Segmentation Using Multi information Fusion Network and CNN-based Region Growing](https://mlsanity.com/paper/2102.12755/) (2021)
- [Trexplorer Super: Topologically Correct Centerline Tree Tracking of Tubular Objects in CT Volumes](https://mlsanity.com/paper/2507.10881/) (2025)
- [HarmonySeg: Tubular Structure Segmentation with Deep-Shallow Feature Fusion and Growth-Suppression Balanced Loss](https://mlsanity.com/paper/2504.07827/) (2025)
- [Extraction of Pulmonary Airway in CT Scans Using Deep Fully Convolutional Networks](https://mlsanity.com/paper/2208.07202/) (2022)
- [Differentiable Topology-Preserved Distance Transform for Pulmonary Airway Segmentation](https://mlsanity.com/paper/2209.08355/) (2022)
- [Open-source tool for Airway Segmentation in Computed Tomography using 2.5D Modified EfficientDet: Contribution to the ATM22 Challenge](https://mlsanity.com/paper/2209.15094/) (2022)
- [Progressive Curriculum Learning with Scale-Enhanced U-Net for Continuous Airway Segmentation](https://mlsanity.com/paper/2410.18456/) (2024)
- [TopoSculpt: Betti-Steered Topological Sculpting of 3D Fine-grained Tubular Shapes](https://mlsanity.com/paper/2509.03938/) (2025)
- [Quantifying Topology In Pancreatic Tubular Networks From Live Imaging 3D Microscopy](https://mlsanity.com/paper/2105.09737/) (2021)
- [Airway Tree Modeling Using Dual-channel 3D UNet 3+ with Vesselness Prior](https://mlsanity.com/paper/2208.13969/) (2022)
- [A CT-Based Airway Segmentation Using U2-net Trained by the Dice Loss Function](https://mlsanity.com/paper/2209.10796/) (2022)
- [Boundary-Emphasized Weight Maps for Distal Airway Segmentation](https://mlsanity.com/paper/2503.00242/) (2025)
- [Airway Segmentation Network for Enhanced Tubular Feature Extraction](https://mlsanity.com/paper/2507.06581/) (2025)
- [DCFFSNet: Deep Connectivity Feature Fusion Separation Network for Medical Image Segmentation](https://mlsanity.com/paper/2507.18407/) (2025)

## Abstract (arXiv)

Airway segmentation on CT scans is critical for pulmonary disease diagnosis and endobronchial navigation. Manual extraction of airway requires strenuous efforts due to the complicated structure and various appearance of airway. For automatic airway extraction, convolutional neural networks (CNNs) based methods have recently become the state-of-the-art approach. However, there still remains a challenge for CNNs to perceive the tree-like pattern and comprehend the connectivity of airway. To address this, we propose a voxel-connectivity aware approach named AirwayNet for accurate airway segmentation. By connectivity modeling, conventional binary segmentation task is transformed into 26 tasks of connectivity prediction. Thus, our AirwayNet learns both airway structure and relationship between neighboring voxels. To take advantage of context knowledge, lung distance map and voxel coordinates are fed into AirwayNet as additional semantic information. Compared to existing approaches, AirwayNet achieved superior performance, demonstrating the effectiveness of the network's awareness of voxel connectivity.