ArticleBioMed research international2019
Automatic Lung Segmentation Based on Texture and Deep Features of HRCT Images with Interstitial Lung Disease.
Article in BioMed research international, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.
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Who cites it
21 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A Systematic Review of Automated Segmentation Methods and Public Datasets for the Lung and its Lobes and Findings on Computed Tomography Images.Yearbook of medical informatics · 2022Pooled it
- Automatic assessment of lung involvement in systemic sclerosis using deep learning.Journal of research in medical sciences : the official journal of Isfahan University of Medical Sciences · 2026Article
- Artificial Intelligence Enabled Lung Sound Auscultation in the Early Diagnosis and Subtyping of Interstitial Lung Disease.Journal of clinical medicine · 2025Review
- An Efficient Dual-Sampling Approach for Chest CT Diagnosis.Journal of multidisciplinary healthcare · 2025Article
- A Novel Method for 3D Lung Tumor Reconstruction Using Generative Models.Diagnostics (Basel, Switzerland) · 2024Article
- Development of a radiomics nomogram to predict the treatment resistance of Chinese MPO-AAV patients with lung involvement: a two-center study.Frontiers in immunology · 2023Article
- Robust weakly supervised learning for COVID-19 recognition using multi-center CT images.Applied soft computing · 2022Article
- Enhanced lung image segmentation using deep learning.Neural computing & applications · 2022Article
- SegChaNet: A Novel Model for Lung Cancer Segmentation in CT Scans.Applied bionics and biomechanics · 2022Article
- Multi Level Approach for Segmentation of Interstitial Lung Disease (ILD) Patterns Classification Based on Superpixel Processing and Fusion ofComputational intelligence and neuroscience · 2022Article
- Three-stage segmentation of lung region from CT images using deep neural networks.BMC medical imaging · 2021Article
- Abnormal lung quantification in chest CT images of COVID-19 patients with deep learning and its application to severity prediction.Medical physics · 2021Article
- A narrative review on characterization of acute respiratory distress syndrome in COVID-19-infected lungs using artificial intelligence.Computers in biology and medicine · 2021Review
- A deep learning-based quantitative computed tomography model for predicting the severity of COVID-19: a retrospective study of 196 patients.Annals of translational medicine · 2021Article
- Modeling Respiratory Signals by Deformable Image Registration on 4DCT Lung Images.BioMed research international · 2021Article
- A Semi-Automatic Step-by-Step Expert-Guided LI-RADS Grading System Based on Gadoxetic Acid-Enhanced MRI.Journal of hepatocellular carcinoma · 2021Article
- A Novel Machine Learning-derived Radiomic Signature of the Whole Lung Differentiates Stable From Progressive COVID-19 Infection: A Retrospective Cohort Study.Journal of thoracic imaging · 2020Article
- Imaging Diagnostics and Pathology in SARS-CoV-2-Related Diseases.International journal of molecular sciences · 2020Review
- Approaches Based on Artificial Intelligence and the Internet of Intelligent Things to Prevent the Spread of COVID-19: Scoping Review.Journal of medical Internet research · 2020Article
- Longitudinal Assessment of COVID-19 Using a Deep Learning-based Quantitative CT Pipeline: Illustration of Two Cases.Radiology. Cardiothoracic imaging · 2020Article
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4 authors.
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Abstract
Lung segmentation in high-resolution computed tomography (HRCT) images is necessary before the computer-aided diagnosis (CAD) of interstitial lung disease (ILD). Traditional methods are less intelligent and have lower accuracy of segmentation. This paper develops a novel automatic segmentation model using radiomics with a combination of hand-crafted features and deep features. The study uses ILD Database-MedGIFT from 128 patients with 108 annotated image series and selects 1946 regions of interest (ROI) of lung tissue patterns for training and testing. First, images are denoised by Wiener filter. Then, segmentation is performed by fusion of features that are extracted from the gray-level co-occurrence matrix (GLCM) which is a classic texture analysis method and U-Net which is a standard convolutional neural network (CNN). The final experiment result for segmentation in terms of dice similarity coefficient (DSC) is 89.42%, which is comparable to the state-of-the-art methods. The training performance shows the effectiveness for a combination of texture and deep radiomics features in lung segmentation.
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