ArticleInternational journal of computer assisted radiology and surgery2022
Quantification of pulmonary involvement in COVID-19 pneumonia by means of a cascade of two U-nets: training and assessment on multiple datasets using different annotation criteria.
Article in International journal of computer assisted radiology and surgery, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed, 22 citations in OpenAlex.
- Effects of inspiratory muscle training on exercise capacity, muscle oxygenation and strength, physical activity, and dyspnea in patients with post-COVID-19 syndrome and pulmonary involvement: a randomized controlled triple-blinded study.BMC pulmonary medicine · 2026Trial
- Understanding the Impact of Evaluation Metrics in Kinetic Models for Consensus-Based Segmentation.Entropy (Basel, Switzerland) · 2025Article
- CompositIA: an open-source automated quantification tool for body composition scores from thoraco-abdominal CT scans.European radiology experimental · 2025Article
- The Evolution of Artificial Intelligence in Medical Imaging: From Computer Science to Machine and Deep Learning.Cancers · 2024Review
- COVLIAS 3.0: cloud-based quantized hybrid UNet3+ deep learning for COVID-19 lesion detection in lung computed tomography.Frontiers in artificial intelligence · 2024Article
- Self-paced Multi-view Learning for CT-based severity assessment of COVID-19.Biomedical signal processing and control · 2023Article
- A multicenter evaluation of a deep learning software (LungQuant) for lung parenchyma characterization in COVID-19 pneumonia.European radiology experimental · 2023Article
- Quantification of pulmonary involvement in COVID-19 pneumonia: an upgrade of theEuropean physical journal plus · 2023Article
- COVLIAS 1.0Diagnostics (Basel, Switzerland) · 2022Article
- Quantitative Analysis of Residual COVID-19 Lung CT Features: Consistency among Two Commercial Software.Journal of personalized medicine · 2021Article
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14 authors at 7 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
purposeThis study aims at exploiting artificial intelligence (AI) for the identification, segmentation and quantification of COVID-19 pulmonary lesions. The limited data availability and the annotation quality are relevant factors in training AI-methods. We investigated the effects of using multiple datasets, heterogeneously populated and annotated according to different criteria.
methodsWe developed an automated analysis pipeline, the LungQuant system, based on a cascade of two U-nets. The first one (U-net[Formula: see text]) is devoted to the identification of the lung parenchyma; the second one (U-net[Formula: see text]) acts on a bounding box enclosing the segmented lungs to identify the areas affected by COVID-19 lesions. Different public datasets were used to train the U-nets and to evaluate their segmentation performances, which have been quantified in terms of the Dice Similarity Coefficients. The accuracy in predicting the CT-Severity Score (CT-SS) of the LungQuant system has been also evaluated.
resultsBoth the volumetric DSC (vDSC) and the accuracy showed a dependency on the annotation quality of the released data samples. On an independent dataset (COVID-19-CT-Seg), both the vDSC and the surface DSC (sDSC) were measured between the masks predicted by LungQuant system and the reference ones. The vDSC (sDSC) values of 0.95±0.01 and 0.66±0.13 (0.95±0.02 and 0.76±0.18, with 5 mm tolerance) were obtained for the segmentation of lungs and COVID-19 lesions, respectively. The system achieved an accuracy of 90% in CT-SS identification on this benchmark dataset.
conclusionWe analysed the impact of using data samples with different annotation criteria in training an AI-based quantification system for pulmonary involvement in COVID-19 pneumonia. In terms of vDSC measures, the U-net segmentation strongly depends on the quality of the lesion annotations. Nevertheless, the CT-SS can be accurately predicted on independent test sets, demonstrating the satisfactory generalization ability of the LungQuant.
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