ArticlePloS one2023
Artificially-generated consolidations and balanced augmentation increase performance of U-net for lung parenchyma segmentation on MR images.
Article in PloS one, 2023. 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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Who cites it
7 citing papers in PubMed.
- Phase-resolved functional lung MRI detects single-dose and sustained bronchodilator responses in COPD in a randomized crossover trial.European radiology · 2026Trial
- Phase-resolved functional lung magnetic resonance imaging for evaluation of lung perfusion and ventilation in fibrosing interstitial lung diseases.Insights into imaging · 2026Article
- What is the difference between pulmonary arterial hypertension and chronic thromboembolic pulmonary hypertension on phase-resolved functional lung MRI? A cross-sectional observational study.European radiology · 2026Observational
- Dual-Center Repeatability, Consistency, and Comparison of 3D Phase-Resolved Functional (PREFUL) Ventilation MRI at 3 and 1.5 T in Healthy Volunteers.NMR in biomedicine · 2025Article
- Assessment of ventilation heterogeneity in severe asthma using phase-resolved functional lung magnetic resonance imaging.Physiological reports · 2025Article
- Automated scoring of airway abnormalities and mucus plugging in chest magnetic resonance imaging of cystic fibrosis using artificial intelligence.Computational and structural biotechnology journal · 2025Article
- Automated lung segmentation on chest MRI in children with cystic fibrosis.Frontiers in medicine · 2024Article
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Authors and funding
12 authors.
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Abstract
purposeTo improve automated lung segmentation on 2D lung MR images using balanced augmentation and artificially-generated consolidations for training of a convolutional neural network (CNN). MATERIALS AND
methodsFrom 233 healthy volunteers and 100 patients, 1891 coronal MR images were acquired. Of these, 1666 images without consolidations were used to build a binary semantic CNN for lung segmentation and 225 images (187 without consolidations, 38 with consolidations) were used for testing. To increase CNN performance of segmenting lung parenchyma with consolidations, balanced augmentation was performed and artificially-generated consolidations were added to all training images. The proposed CNN (CNNBal/Cons) was compared to two other CNNs: CNNUnbal/NoCons-without balanced augmentation and artificially-generated consolidations and CNNBal/NoCons-with balanced augmentation but without artificially-generated consolidations. Segmentation results were assessed using Sørensen-Dice coefficient (SDC) and Hausdorff distance coefficient.
resultsRegarding the 187 MR test images without consolidations, the mean SDC of CNNUnbal/NoCons (92.1 ± 6% (mean ± standard deviation)) was significantly lower compared to CNNBal/NoCons (94.0 ± 5.3%, P = 0.0013) and CNNBal/Cons (94.3 ± 4.1%, P = 0.0001). No significant difference was found between SDC of CNNBal/Cons and CNNBal/NoCons (P = 0.54). For the 38 MR test images with consolidations, SDC of CNNUnbal/NoCons (89.0 ± 7.1%) was not significantly different compared to CNNBal/NoCons (90.2 ± 9.4%, P = 0.53). SDC of CNNBal/Cons (94.3 ± 3.7%) was significantly higher compared to CNNBal/NoCons (P = 0.0146) and CNNUnbal/NoCons (P = 0.001).
conclusionsExpanding training datasets via balanced augmentation and artificially-generated consolidations improved the accuracy of CNNBal/Cons, especially in datasets with parenchymal consolidations. This is an important step towards a robust automated postprocessing of lung MRI datasets in clinical routine.
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