ArticleFrontiers in physiology2021
Automatic Lung Segmentation and Quantification of Aeration in Computed Tomography of the Chest Using 3D Transfer Learning.
Article in Frontiers in physiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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7 citing papers in PubMed, 11 citations in OpenAlex.
- Quantitative computed tomography analysis of changes in airway volume after total laryngectomy.The Journal of laryngology and otology · 2026Article
- Comparison of multi-organ CT image segmentation tools for whole-body [European journal of nuclear medicine and molecular imaging · 2026Article
- Dual-energy computed tomography derived pulmonary blood volume: association with pulmonary blood flow.Respiratory research · 2025Article
- Advancements in 3D lung models for minimally invasive lung cancer surgery: from static to real-time dynamic modeling.Translational lung cancer research · 2025Review
- Advances in artificial intelligence applications for the management of chronic obstructive pulmonary disease.Frontiers in medicine · 2025Review
- 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
- Segmental Lung Recruitment in Patients with Bilateral COVID-19 Pneumonia Complicated by Acute Respiratory Distress Syndrome: A Case Report.Medicina (Kaunas, Lithuania) · 2023Article
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Authors and funding
9 authors at 6 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIdentification of lung parenchyma on computer tomographic (CT) scans in the research setting is done semi-automatically and requires cumbersome manual correction. This is especially true in pathological conditions, hindering the clinical application of aeration compartment (AC) analysis. Deep learning based algorithms have lately been shown to be reliable and time-efficient in segmenting pathologic lungs. In this contribution, we thus propose a novel 3D transfer learning based approach to quantify lung volumes, aeration compartments and lung recruitability.
methodsTwo convolutional neural networks developed for biomedical image segmentation (uNet), with different resolutions and fields of view, were implemented using Matlab. Training and evaluation was done on 180 scans of 18 pigs in experimental ARDS (
resultsOn the experimental data set,
conclusionCompared to manual segmentations, automatic uNet based 3D lung segmentation provides acceptable quality for both clinical and scientific purposes in the quantification of lung volumes, aeration compartments, and recruitability.
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