ArticleScientific reports2021
Automatic airway segmentation from computed tomography using robust and efficient 3-D convolutional neural networks.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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Who cites it
29 citing papers in PubMed, 56 citations in OpenAlex.
- Characterizing chronic obstructive pulmonary disease using quantitative MRI biomarkers.The British journal of radiology · 2026Review
- Quantitative assessment of lung mechanical properties in ARDS using X-ray computed tomography.Intensive care medicine experimental · 2026Review
- Airway segmentation on CT - A systematic review of machine learning tools.European journal of radiology open · 2026Article
- AlveolEye: rapid and precise lung morphometry guided by computer vision.American journal of physiology. Lung cellular and molecular physiology · 2026Article
- Artificial Intelligence in Pulmonary Endoscopy: Current Evidence, Limitations, and Future Directions.Journal of imaging · 2026Review
- Advanced lung segmentation on chest HRCT: comprehensive pipeline for quantification of airways, vessels, and injury patterns.La Radiologia medica · 2026Observational
- Bronchovascular Tree-Guided Weakly Supervised Learning Method for Pulmonary Segment Segmentation.Journal of imaging informatics in medicine · 2026Article
- Automated 3D segmentation of human vagus nerve fascicles and epineurium from micro-computed tomography images using anatomy-aware neural networks.Journal of neural engineering · 2026Article
- Evaluation of the impact of cardiopulmonary rehabilitation exercise training on cardiopulmonary function in patients with chronic obstructive pulmonary disease complicated by unstable angina pectoris using a hierarchical deep learning CT image model.Frontiers in physiology · 2026Article
- Subject-specific modeling framework for particle deposition using computational fluid dynamics.Journal of aerosol science · 2025Article
- A Monte Carlo-Based 3D Whole Lung Model for Aerosol Deposition Studies: Implementation and Validation.Bioengineering (Basel, Switzerland) · 2025Article
- Skeleton-guided 3D convolutional neural network for tubular structure segmentation.International journal of computer assisted radiology and surgery · 2025Article
- Effects of acute-phase COVID-19-related indicators on pulmonary fibrosis and follow-up evaluation.European journal of medical research · 2024Article
- Detail-sensitive 3D-UNet for pulmonary airway segmentation from CT images.Medical & biological engineering & computing · 2024Article
- Computational multiphysics modeling of radioactive aerosol deposition in diverse human respiratory tract geometries.Communications engineering · 2024Article
- Modeling Realistic Geometries in Human Intrathoracic Airways.Diagnostics (Basel, Switzerland) · 2024Review
- Evolving and Novel Applications of Artificial Intelligence in Thoracic Imaging.Diagnostics (Basel, Switzerland) · 2024Review
- UNSUPERVISED AIRWAY TREE CLUSTERING WITH DEEP LEARNING: THE MULTI-ETHNIC STUDY OF ATHEROSCLEROSIS (MESA) LUNG STUDY.Proceedings. IEEE International Symposium on Biomedical Imaging · 2024Article
- Persistent mucus plugs in proximal airways are consequential for airflow limitation in asthma.JCI insight · 2024Observational
- Deep learning-based bronchial tree-guided semi-automatic segmentation of pulmonary segments in computed tomography images.Quantitative imaging in medicine and surgery · 2024Article
Corrections and comments
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Authors and funding
5 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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: (1) a dataset of pediatric patients including subjects with cystic fibrosis, (2) a subset of the Danish Lung Cancer Screening Trial, including subjects with chronic obstructive pulmonary disease, and (3) 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.