ArticleIEEE transactions on medical imaging2021
A CT-Based Automated Algorithm for Airway Segmentation Using Freeze-and-Grow Propagation and Deep Learning.
Article in IEEE transactions on medical imaging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed.
- Virtual Bronchoscopic Pathfinder (VBP): An Open-Source Web-Based System for Airway Segmentation, Cost-Field Path Planning, and Cross-Device 3D Navigation.Tomography (Ann Arbor, Mich.) · 2026Article
- Airway segmentation on CT - A systematic review of machine learning tools.European journal of radiology open · 2026Article
- Bronchovascular Tree-Guided Weakly Supervised Learning Method for Pulmonary Segment Segmentation.Journal of imaging informatics in medicine · 2026Article
- Deformable lung models for anatomical lung resections: The introduction of simulated reality for imaging guidance.JTCVS techniques · 2026Article
- Subject-specific modeling framework for particle deposition using computational fluid dynamics.Journal of aerosol science · 2025Article
- Pulmonary Biomechanics in COPD: Imaging Techniques and Clinical Applications.Journal of biomechanical engineering · 2025Review
- Performance Evaluation of Image Segmentation Using Dual-Energy Spectral CT Images with Deep Learning Image Reconstruction: A Phantom Study.Tomography (Ann Arbor, Mich.) · 2025Article
- Automated CT-based measurements of radial and longitudinal expansion of airways due to breathing-related lung volume change.Medical physics · 2025Article
- Advances in artificial intelligence applications for the management of chronic obstructive pulmonary disease.Frontiers in medicine · 2025Review
- Automated CT-based decoupling of the effects of airway narrowing and wall thinning on airway counts in chronic obstructive pulmonary disease.The British journal of radiology · 2025Article
- Detail-sensitive 3D-UNet for pulmonary airway segmentation from CT images.Medical & biological engineering & computing · 2024Article
- Ultra-low dose hip CT-based automated measurement of volumetric bone mineral density at proximal femoral subregions.Medical physics · 2024Article
- Modeling Realistic Geometries in Human Intrathoracic Airways.Diagnostics (Basel, Switzerland) · 2024Review
- Artificial intelligence in COPD CT images: identification, staging, and quantitation.Respiratory research · 2024Review
- A Precise Pulmonary Airway Tree Segmentation Method Using Quasi-Spherical Region Constraint and Tracheal Wall Gap Sealing.Sensors (Basel, Switzerland) · 2024Article
- Chest CT-based automated vertebral fracture assessment using artificial intelligence and morphologic features.Medical physics · 2024Article
- UNSUPERVISED AIRWAY TREE CLUSTERING WITH DEEP LEARNING: THE MULTI-ETHNIC STUDY OF ATHEROSCLEROSIS (MESA) LUNG STUDY.Proceedings. IEEE International Symposium on Biomedical Imaging · 2024Article
- AeroPath: An airway segmentation benchmark dataset with challenging pathology and baseline method.PloS one · 2024Article
- Reproducibility of a combined artificial intelligence and optimal-surface graph-cut method to automate bronchial parameter extraction.European radiology · 2023Article
- Airway Detection in COPD at Low-Dose CT Using Deep Learning and Multiparametric Freeze and Grow.Radiology. Cardiothoracic imaging · 2022Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Chronic obstructive pulmonary disease (COPD) is a common lung disease, and quantitative CT-based bronchial phenotypes are of increasing interest as a means of exploring COPD sub-phenotypes, establishing disease progression, and evaluating intervention outcomes. Reliable, fully automated, and accurate segmentation of pulmonary airway trees is critical to such exploration. We present a novel approach of multi-parametric freeze-and-grow (FG) propagation which starts with a conservative segmentation parameter and captures finer details through iterative parameter relaxation. First, a CT intensity-based FG algorithm is developed and applied for airway tree segmentation. A more efficient version is produced using deep learning methods generating airway lumen likelihood maps from CT images, which are input to the FG algorithm. Both CT intensity- and deep learning-based algorithms are fully automated, and their performance, in terms of repeat scan reproducibility, accuracy, and leakages, is evaluated and compared with results from several state-of-the-art methods including an industry-standard one, where segmentation results were manually reviewed and corrected. Both new algorithms show a reproducibility of 95% or higher for total lung capacity (TLC) repeat CT scans. Experiments on TLC CT scans from different imaging sites at standard and low radiation dosages show that both new algorithms outperform the other methods in terms of leakages and branch-level accuracy. Considering the performance and execution times, the deep learning-based FG algorithm is a fully automated option for large multi-site studies.
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