ArticleScientific reports2021
CT image segmentation for inflamed and fibrotic lungs using a multi-resolution convolutional neural network.
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 36 papers, 1 of them a synthesis that pooled it.
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Who cites it
36 citing papers in PubMed, 1 synthesis or guideline pooled it, 60 citations in OpenAlex.
- The Use of Computed Tomography Densitometry for the Assessment of Emphysema in Clinical Trials: A Position Paper from the Fleischner Society.American journal of respiratory and critical care medicine · 2025Pooled it
- Quantitative assessment of lung mechanical properties in ARDS using X-ray computed tomography.Intensive care medicine experimental · 2026Review
- Sex-related differences in lung injury distribution and outcomes in COVID-19 acute respiratory failure: insights from the CT-COVID19 multicenter study group.Intensive care medicine experimental · 2026Article
- Characterizing heterogeneity and subphenotyping acute respiratory distress syndrome with computed tomography.Intensive care medicine experimental · 2026Review
- Biomechanical implications of mass loading in a swine model of acute hypoxemic respiratory failure.Journal of applied physiology (Bethesda, Md. : 1985) · 2025Article
- Manual segmentation of opacities and consolidations on CT of long COVID patients from multiple annotators.Scientific data · 2025Article
- TriSwinUNETR lobe segmentation model for computing DIR-free CT-ventilation.Frontiers in oncology · 2025Article
- Pulmonary Fissure Segmentation in CT Images Using Image Filtering and Machine Learning.Tomography (Ann Arbor, Mich.) · 2024Article
- The pleural gradient does not reflect the superimposed pressure in patients with class III obesity.Critical care (London, England) · 2024Observational
- Phenotyping COVID-19 respiratory failure in spontaneously breathing patients with AI on lung CT-scan.Critical care (London, England) · 2024Observational
- Algor-ethics: charting the ethical path for AI in critical care.Journal of clinical monitoring and computing · 2024Article
- Imaging the Lung in ARDS: A Primer.Respiratory care · 2024Review
- Vessel and Airway Characteristics in One-Year Computed Tomography-defined Rapid Emphysema Progression: SPIROMICS.Annals of the American Thoracic Society · 2024Article
- Structural Predictors of Lung Function Decline in Young Smokers with Normal Spirometry.American journal of respiratory and critical care medicine · 2024Article
- Segmentation of lung lobes and lesions in chest CT for the classification of COVID-19 severity.Scientific reports · 2023Article
- Implementable Deep Learning for Multi-sequence Proton MRI Lung Segmentation: A Multi-center, Multi-vendor, and Multi-disease Study.Journal of magnetic resonance imaging : JMRI · 2023Article
- Direct estimation of regional lung volume change from paired and single CT images using residual regression neural network.Medical physics · 2023Article
- Quantitative Imaging Metrics for the Assessment of Pulmonary Pathophysiology: An Official American Thoracic Society and Fleischner Society Joint Workshop Report.Annals of the American Thoracic Society · 2023Article
- Review on the Evaluation and Development of Artificial Intelligence for COVID-19 Containment.Sensors (Basel, Switzerland) · 2023Review
- A specific combination of laboratory data is associated with overweight lungs in patients with COVID-19 pneumonia at hospital admission: secondary cross-sectional analysis of a randomized clinical trial.Frontiers in medicine · 2023Article
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Authors and funding
12 authors at 4 institutions in 2 countries.
Funding
Abstract
The purpose of this study was to develop a fully-automated segmentation algorithm, robust to various density enhancing lung abnormalities, to facilitate rapid quantitative analysis of computed tomography images. A polymorphic training approach is proposed, in which both specifically labeled left and right lungs of humans with COPD, and nonspecifically labeled lungs of animals with acute lung injury, were incorporated into training a single neural network. The resulting network is intended for predicting left and right lung regions in humans with or without diffuse opacification and consolidation. Performance of the proposed lung segmentation algorithm was extensively evaluated on CT scans of subjects with COPD, confirmed COVID-19, lung cancer, and IPF, despite no labeled training data of the latter three diseases. Lobar segmentations were obtained using the left and right lung segmentation as input to the LobeNet algorithm. Regional lobar analysis was performed using hierarchical clustering to identify radiographic subtypes of COVID-19. The proposed lung segmentation algorithm was quantitatively evaluated using semi-automated and manually-corrected segmentations in 87 COVID-19 CT images, achieving an average symmetric surface distance of [Formula: see text] mm and Dice coefficient of [Formula: see text]. Hierarchical clustering identified four radiographical phenotypes of COVID-19 based on lobar fractions of consolidated and poorly aerated tissue. Lower left and lower right lobes were consistently more afflicted with poor aeration and consolidation. However, the most severe cases demonstrated involvement of all lobes. The polymorphic training approach was able to accurately segment COVID-19 cases with diffuse consolidation without requiring COVID-19 cases for training.
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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.