ArticleEuropean radiology2025
nnU-Net-based high-resolution CT features quantification for interstitial lung diseases.
Article in European radiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Low-dose vs standard-dose photon-counting CT for myopathy-associated interstitial lung disease: a prospective feasibility study.European radiology · 2026Article
- High-resolution computed tomography assessment of connective tissue disease-associated interstitial lung disease: Advances in diagnosis and prognostic evaluation.World journal of radiology · 2026Review
- Anticipating femoral shaft nonunion at 1-3 months: a multimodal framework with automated X‑ray segmentation despite metal implants.International orthopaedics · 2026Article
- Automated CT-Based Quantification of Pulmonary Fibrosis Using Deep Learning-Based Lung Segmentation.Diagnostics (Basel, Switzerland) · 2026Article
- Progression-guided spatiotemporal memory transformers for accurate and consistent longitudinal brain tumor segmentation.Scientific reports · 2026Article
- Advances in the Diagnosis of Rheumatoid Arthritis-Associated Interstitial Lung Disease: Integrating Conventional Tools and Emerging Biomarkers.International journal of molecular sciences · 2026Review
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Authors and funding
13 authors.
Funding
Abstract
objectivesTo develop a new high-resolution (HR)CT abnormalities quantification tool (CVILDES) for interstitial lung diseases (ILDs) based on the nnU-Net network structure and to determine whether the quantitative parameters derived from this new software could offer a reliable and precise assessment in a clinical setting that is in line with expert visual evaluation.
methodsHRCT scans from 83 cases of ILDs and 20 cases of other diffuse lung diseases were labeled section by section by multiple radiologists and were used as training data for developing a deep learning model based on nnU-Net, employing a supervised learning approach. For clinical validation, a cohort including 51 cases of interstitial pneumonia with autoimmune features (IPAF) and 14 cases of idiopathic pulmonary fibrosis (IPF) had CT parenchymal patterns evaluated quantitatively with CVILDES and by visual evaluation. Subsequently, we assessed the correlation of the two methodologies for ILD features quantification. Furthermore, the correlation between the quantitative results derived from the two methods and pulmonary function parameters (DL
resultsAll CT data were successfully quantified using CVILDES. CVILDES-quantified results (total ILD extent, ground-glass opacity, consolidation, reticular pattern and honeycombing) showed a strong correlation with visual evaluation and were numerically close to the visual evaluation results (r = 0.64-0.89, p < 0.0001), particularly for the extent of fibrosis (r = 0.82, p < 0.0001). As judged by correlation with pulmonary function parameters, CVILDES quantification was comparable or even superior to visual evaluation.
conclusionnnU-Net-based CVILDES was comparable to visual evaluation for ILD abnormalities quantification. KEY POINTS: Question Visual assessment of ILD on HRCT is time-consuming and exhibits poor inter-observer agreement, making it challenging to accurately evaluate the therapeutic efficacy. Findings nnU-Net-based Computer vision-based ILD evaluation system (CVILDES) accurately segmented and quantified the HRCT features of ILD, and results were comparable to visual evaluation. Clinical relevance This study developed a new tool that has the potential to be applied in the quantitative assessment of ILD.
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