ArticleMedical physics2021
Abnormal lung quantification in chest CT images of COVID-19 patients with deep learning and its application to severity prediction.
Article in Medical physics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 82 papers.
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82 citing papers in PubMed.
- Monitoring pneumonia onset and severe respiratory infectious diseases using quantitative computed tomography, validation amidst the coronavirus disease 2019 outbreak at the end of 2022 in the Chinese mainland.Journal of thoracic disease · 2026Article
- Prediction of intravenous cyclophosphamide treatment efficacy in idiopathic inflammatory myopathies-associated interstitial lung disease patients: a nomogram based on radiomics and clinical factors.BMC pulmonary medicine · 2026Article
- Association of artificial intelligence-based high-resolution computed tomography parameters with all-cause mortality in patients with connective tissue disease-associated interstitial lung disease: a longitudinal cohort study.Journal of thoracic disease · 2026Article
- Prognostic value of deep learning-based coronary artery calcium score and quantitative pneumonia burden in patients hospitalized with COVID-19.BMC medical imaging · 2026Article
- CT-based radiomics models for predicting the prognosis of children with mycoplasma pneumonia.Frontiers in cellular and infection microbiology · 2026Article
- Chest computed tomography-based artificial intelligence-aided latent class analysis for diagnosis of severe pneumonia.Chinese medical journal · 2025Article
- Deep learning for classifying imaging patterns of interstitial lung disease associated with idiopathic inflammatory myopathies.Scientific reports · 2025Article
- Machine Learning Based Multi-Parameter Modeling for Prediction of Post-Inflammatory Lung Changes.Diagnostics (Basel, Switzerland) · 2025Article
- The relationship between different severity of COVID-19 pneumonia and arterial stiffness based on artificial intelligence analysis.Frontiers in medicine · 2025Article
- Multicenter study on CT-based Radiomics for predicting severity and delayed recovery inFrontiers in medicine · 2025Article
- Comparing Different Data Partitioning Strategies for Segmenting Areas Affected by COVID-19 in CT Scans.Diagnostics (Basel, Switzerland) · 2024Article
- Central artery pulse pressure, not central arterial stiffness impact on all-cause mortality in patients with viral pneumonia infection.BMC infectious diseases · 2024Article
- Predicting the severity of mycoplasma pneumoniae pneumonia in pediatric and adult patients: a multicenter study.Scientific reports · 2024Article
- Human-in-the-Loop-A Deep Learning Strategy in Combination with a Patient-Specific Gaussian Mixture Model Leads to the Fast Characterization of Volumetric Ground-Glass Opacity and Consolidation in the Computed Tomography Scans of COVID-19 Patients.Journal of clinical medicine · 2024Article
- Antiviral Effectiveness, Clinical Outcomes, and Artificial Intelligence Imaging Analysis for Hospitalized COVID-19 Patients Receiving Antivirals.Influenza and other respiratory viruses · 2024Article
- Segmentation and classification of lungs CT-scan for detecting COVID-19 abnormalities by deep learning technique: U-Net model.Journal of family medicine and primary care · 2024Article
- Quantitative radiomics analysis of imaging features in adults and children Mycoplasma pneumonia.Frontiers in medicine · 2024Article
- A Novel COVID-19 Diagnosis Approach Utilizing a Comprehensive Set of Diagnostic Information (CSDI).Journal of clinical medicine · 2023Article
- Augmentation strategies for an imbalanced learning problem on a novel COVID-19 severity dataset.Scientific reports · 2023Article
- Development and validation of a hybrid deep learning-machine learning approach for severity assessment of COVID-19 and other pneumonias.Scientific reports · 2023Article
22 more citing papers are in PubMed but not listed here.
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Abstract
objectiveComputed tomography (CT) provides rich diagnosis and severity information of COVID-19 in clinical practice. However, there is no computerized tool to automatically delineate COVID-19 infection regions in chest CT scans for quantitative assessment in advanced applications such as severity prediction. The aim of this study was to develop a deep learning (DL)-based method for automatic segmentation and quantification of infection regions as well as the entire lungs from chest CT scans.
methodsThe DL-based segmentation method employs the "VB-Net" neural network to segment COVID-19 infection regions in CT scans. The developed DL-based segmentation system is trained by CT scans from 249 COVID-19 patients, and further validated by CT scans from other 300 COVID-19 patients. To accelerate the manual delineation of CT scans for training, a human-involved-model-iterations (HIMI) strategy is also adopted to assist radiologists to refine automatic annotation of each training case. To evaluate the performance of the DL-based segmentation system, three metrics, that is, Dice similarity coefficient, the differences of volume, and percentage of infection (POI), are calculated between automatic and manual segmentations on the validation set. Then, a clinical study on severity prediction is reported based on the quantitative infection assessment.
resultsThe proposed DL-based segmentation system yielded Dice similarity coefficients of 91.6% ± 10.0% between automatic and manual segmentations, and a mean POI estimation error of 0.3% for the whole lung on the validation dataset. Moreover, compared with the cases with fully manual delineation that often takes hours, the proposed HIMI training strategy can dramatically reduce the delineation time to 4 min after three iterations of model updating. Besides, the best accuracy of severity prediction was 73.4% ± 1.3% when the mass of infection (MOI) of multiple lung lobes and bronchopulmonary segments were used as features for severity prediction, indicating the potential clinical application of our quantification technique on severity prediction.
conclusionsA DL-based segmentation system has been developed to automatically segment and quantify infection regions in CT scans of COVID-19 patients. Quantitative evaluation indicated high accuracy in automatic infection delineation and severity prediction.
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