# Automatic airway segmentation from Computed Tomography using robust and efficient 3-D convolutional neural networks

arXiv: https://arxiv.org/abs/2103.16328 · HTML: https://mlsanity.com/paper/2103.16328/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 55 references that are in the map.
- First posted on arXiv 30 Mar 2021.

## Builds on (most cited first, 15 of 15)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/1606.04797/) (2016)
- [3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation](https://mlsanity.com/paper/1606.06650/) (2016)
- [A Survey on Deep Learning in Medical Image Analysis](https://mlsanity.com/paper/1702.05747/) (2017)
- [Generalised Dice overlap as a deep learning loss function for highly unbalanced segmentations](https://mlsanity.com/paper/1707.03237/) (2017)
- [Alleviating Class-wise Gradient Imbalance for Pulmonary Airway Segmentation](https://mlsanity.com/paper/2011.11952/) (2020)
- [AirwayNet: A Voxel-Connectivity Aware Approach for Accurate Airway Segmentation Using Convolutional Neural Networks](https://mlsanity.com/paper/1907.06852/) (2019)
- [Learning Tubule-Sensitive CNNs for Pulmonary Airway and Artery-Vein Segmentation in CT](https://mlsanity.com/paper/2012.05767/) (2020)
- [A joint 3D UNet-Graph Neural Network-based method for Airway Segmentation from chest CTs](https://mlsanity.com/paper/1908.08588/) (2019)
- [Automatic Airway Segmentation in chest CT using Convolutional Neural Networks](https://mlsanity.com/paper/1808.04576/) (2018)
- [A Partially Reversible U-Net for Memory-Efficient Volumetric Image Segmentation](https://mlsanity.com/paper/1906.06148/) (2019)
- [Graph Refinement based Airway Extraction using Mean-Field Networks and Graph Neural Networks](https://mlsanity.com/paper/1811.08674/) (2018)

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

- [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)
- [Human Treelike Tubular Structure Segmentation: A Comprehensive Review and Future Perspectives](https://mlsanity.com/paper/2207.11203/) (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)
- [LTSP: Long-Term Slice Propagation for Accurate Airway Segmentation](https://mlsanity.com/paper/2202.06260/) (2022)
- [Topology Repairing of Disconnected Pulmonary Airways and Vessels: Baselines and a Dataset](https://mlsanity.com/paper/2306.07089/) (2023)
- [BCNet: Bronchus Classification via Structure Guided Representation Learning](https://mlsanity.com/paper/2205.06947/) (2022)
- [Label Refinement Network from Synthetic Error Augmentation for Medical Image Segmentation](https://mlsanity.com/paper/2209.06353/) (2022)
- [Extraction of Pulmonary Airway in CT Scans Using Deep Fully Convolutional Networks](https://mlsanity.com/paper/2208.07202/) (2022)
- [Bronchovascular Tree-Guided Weakly Supervised Learning Method for Pulmonary Segment Segmentation](https://mlsanity.com/paper/2505.13911/) (2025)
- [A CT-Based Airway Segmentation Using U2-net Trained by the Dice Loss Function](https://mlsanity.com/paper/2209.10796/) (2022)
- [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)
- [Few-Shot Airway-Tree Modeling using Data-Driven Sparse Priors](https://mlsanity.com/paper/2407.04507/) (2024)
- [Boundary-Emphasized Weight Maps for Distal Airway Segmentation](https://mlsanity.com/paper/2503.00242/) (2025)
- [RepAir: A Framework for Airway Segmentation and Discontinuity Correction in CT](https://mlsanity.com/paper/2511.14649/) (2025)
- [Can Unsupervised Methods Outperform Supervised Deep Learning When Ground Truth Is Sparse? A Case Study of Bronchovascular Bundle Segmentation in Low-Dose CT](https://mlsanity.com/paper/2608.16855/) (2026)

## Abstract (arXiv)

This paper presents a fully automatic and end-to-end optimised airway segmentation method for thoracic computed tomography, based on the U-Net architecture. We use a simple and low-memory 3D U-Net as backbone, which allows the method to process large 3D image patches, often comprising full lungs, in a single pass through the network. This makes the method simple, robust and efficient. We validated the proposed method on three datasets with very different characteristics and various airway abnormalities: i) a dataset of pediatric patients including subjects with cystic fibrosis, ii) a subset of the Danish Lung Cancer Screening Trial, including subjects with chronic obstructive pulmonary disease, and iii) the EXACT'09 public dataset. We compared our method with other state-of-the-art airway segmentation methods, including relevant learning-based methods in the literature evaluated on the EXACT'09 data. We show that our method can extract highly complete airway trees with few false positive errors, on scans from both healthy and diseased subjects, and also that the method generalizes well across different datasets. On the EXACT'09 test set, our method achieved the second highest sensitivity score among all methods that reported good specificity.