ArticleMedical image analysis2020
Multi-resolution convolutional neural networks for fully automated segmentation of acutely injured lungs in multiple species.
Article in Medical image analysis, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers.
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46 citing papers in PubMed, 86 citations in OpenAlex.
- Quantitative assessment of lung mechanical properties in ARDS using X-ray computed tomography.Intensive care medicine experimental · 2026Review
- A multi-fidelity poroelastic finite element and machine learning framework for characterizing respiratory mechanics in porcine lungs.Biomechanics and modeling in mechanobiology · 2026Article
- 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
- A lightweight multiscale attention network for 3D tumor segmentation in PET images.Scientific reports · 2025Article
- Pulmonary Biomechanics in COPD: Imaging Techniques and Clinical Applications.Journal of biomechanical engineering · 2025Review
- Biomechanical implications of mass loading in a swine model of acute hypoxemic respiratory failure.Journal of applied physiology (Bethesda, Md. : 1985) · 2025Article
- Structural and functional characteristics of healthy and injured porcine lungs during deflation: a quantitative CT imaging analysis.Journal of applied physiology (Bethesda, Md. : 1985) · 2025Article
- Role of Artificial Intelligence in Nanomedicine and Organ-specific Therapy: An Updated Review.Current drug targets · 2025Review
- Forward Computational Modeling of Respiratory Airflow.Applied sciences (Basel, Switzerland) · 2024Article
- The pleural gradient does not reflect the superimposed pressure in patients with class III obesity.Critical care (London, England) · 2024Observational
- Development and validation of a deep learning-based framework for automated lung CT segmentation and acute respiratory distress syndrome prediction: a multicenter cohort study.EClinicalMedicine · 2024Article
- Imaging the Lung in ARDS: A Primer.Respiratory care · 2024Review
- LungViT: Ensembling Cascade of Texture Sensitive Hierarchical Vision Transformers for Cross-Volume Chest CT Image-to-Image Translation.IEEE transactions on medical imaging · 2024Article
- Peripheral Blood Mononuclear Cell Gene Expression Associated with Pulmonary Microvascular Perfusion: The Multi-Ethnic Study of Atherosclerosis Chronic Obstructive Pulmonary Disease.Annals of the American Thoracic Society · 2024Article
- Electrical Impedance Tomography Identifies Evolution of Regional Perfusion in a Porcine Model of Acute Respiratory Distress Syndrome.Anesthesiology · 2023Article
- Direct estimation of regional lung volume change from paired and single CT images using residual regression neural network.Medical physics · 2023Article
- Improving pulmonary perfusion assessment by dynamic contrast-enhanced computed tomography in an experimental lung injury model.Journal of applied physiology (Bethesda, Md. : 1985) · 2023Article
- A Dual-Channel Deep Learning Approach for Lung Cavity Estimation From Hyperpolarized Gas and Proton MRI.Journal of magnetic resonance imaging : JMRI · 2023Article
- Precision of CT-derived alveolar recruitment assessed by human observers and a machine learning algorithm in moderate and severe ARDS.Intensive care medicine experimental · 2023Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 1 country.
Funding
Abstract
Segmentation of lungs with acute respiratory distress syndrome (ARDS) is a challenging task due to diffuse opacification in dependent regions which results in little to no contrast at the lung boundary. For segmentation of severely injured lungs, local intensity and texture information, as well as global contextual information, are important factors for consistent inclusion of intrapulmonary structures. In this study, we propose a deep learning framework which uses a novel multi-resolution convolutional neural network (ConvNet) for automated segmentation of lungs in multiple mammalian species with injury models similar to ARDS. The multi-resolution model eliminates the need to tradeoff between high-resolution and global context by using a cascade of low-resolution to high-resolution networks. Transfer learning is used to accommodate the limited number of training datasets. The model was initially pre-trained on human CT images, and subsequently fine-tuned on canine, porcine, and ovine CT images with lung injuries similar to ARDS. The multi-resolution model was compared to both high-resolution and low-resolution networks alone. The multi-resolution model outperformed both the low- and high-resolution models, achieving an overall mean Jacaard index of 0.963 ± 0.025 compared to 0.919 ± 0.027 and 0.950 ± 0.036, respectively, for the animal dataset (N=287). The multi-resolution model achieves an overall average symmetric surface distance of 0.438 ± 0.315 mm, compared to 0.971 ± 0.368 mm and 0.657 ± 0.519 mm for the low-resolution and high-resolution models, respectively. We conclude that the multi-resolution model produces accurate segmentations in severely injured lungs, which is attributed to the inclusion of both local and global features.
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