ArticlePattern recognition2021
Automatic COVID-19 lung infected region segmentation and measurement using CT-scans images.
Article in Pattern recognition, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 76 papers, 3 of them syntheses that pooled it.
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Who cites it
76 citing papers in PubMed, 3 syntheses or guidelines pooled it, 199 citations in OpenAlex.
- 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
- Role of Artificial Intelligence in COVID-19 Detection.Sensors (Basel, Switzerland) · 2021Pooled it
- X-Ray Equipped with Artificial Intelligence: Changing the COVID-19 Diagnostic Paradigm during the Pandemic.BioMed research international · 2021Pooled it
- Multi-scale deformable attention fusion network with global context modeling for chest X-ray lesion segmentation.BMC medical imaging · 2026Article
- MedNeXt for accurate medical image classification and segmentation: A lightweight transformer-style convolutional neural network.PloS one · 2026Article
- CoviSwin: A Deep Vision Transformer for Automatic Segmentation of COVID-19 CT Scans.Bioengineering (Basel, Switzerland) · 2025Article
- Modeling of Severity Classification Algorithm Using Abdominal Aortic Aneurysm Computed Tomography Image Segmentation Based on U-Net with Improved Noise Reduction Performance.Sensors (Basel, Switzerland) · 2025Article
- A Novel Entropy-Based Approach for Thermal Image Segmentation Using Multilevel Thresholding.Entropy (Basel, Switzerland) · 2025Article
- Multi-scale input layers and dense decoder aggregation network for COVID-19 lesion segmentation from CT scans.Scientific reports · 2024Article
- An AI-Based Low-Risk Lung Health Image Visualization Framework Using LR-ULDCT.Journal of imaging informatics in medicine · 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
- Current Diagnostic Techniques for Pneumonia: A Scoping Review.Sensors (Basel, Switzerland) · 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
- Tumor segmentation via enhanced area growth algorithm for lung CT images.BMC medical imaging · 2023Article
- Fully feature fusion based neural network for COVID-19 lesion segmentation in CT images.Biomedical signal processing and control · 2023Article
- A COVID-19 medical image classification algorithm based on Transformer.Scientific reports · 2023Article
- Tumor Lung Visualization and Localization through Virtual Reality and Thermal Feedback Interface.Diagnostics (Basel, Switzerland) · 2023Article
- Automated diagnosis of COVID-19 using radiological modalities and Artificial Intelligence functionalities: A retrospective study based on chest HRCT database.Biomedical signal processing and control · 2023Article
- Review on the Evaluation and Development of Artificial Intelligence for COVID-19 Containment.Sensors (Basel, Switzerland) · 2023Review
- A teacher-student framework with Fourier Transform augmentation for COVID-19 infection segmentation in CT images.Biomedical signal processing and control · 2023Article
16 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
4 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
History shows that the infectious disease (COVID-19) can stun the world quickly, causing massive losses to health, resulting in a profound impact on the lives of billions of people, from both a safety and an economic perspective, for controlling the COVID-19 pandemic. The best strategy is to provide early intervention to stop the spread of the disease. In general, Computer Tomography (CT) is used to detect tumors in pneumonia, lungs, tuberculosis, emphysema, or other pleura (the membrane covering the lungs) diseases. Disadvantages of CT imaging system are: inferior soft tissue contrast compared to MRI as it is X-ray-based Radiation exposure. Lung CT image segmentation is a necessary initial step for lung image analysis. The main challenges of segmentation algorithms exaggerated due to intensity in-homogeneity, presence of artifacts, and closeness in the gray level of different soft tissue. The goal of this paper is to design and evaluate an automatic tool for automatic COVID-19 Lung Infection segmentation and measurement using chest CT images. The extensive computer simulations show better efficiency and flexibility of this end-to-end learning approach on CT image segmentation with image enhancement comparing to the state of the art segmentation approaches, namely GraphCut, Medical Image Segmentation (MIS), and Watershed. Experiments performed on COVID-CT-Dataset containing (275) CT scans that are positive for COVID-19 and new data acquired from the EL-BAYANE center for Radiology and Medical Imaging. The means of statistical measures obtained using the accuracy, sensitivity, F-measure, precision, MCC, Dice, Jacquard, and specificity are 0.98, 0.73, 0.71, 0.73, 0.71, 0.71, 0.57, 0.99 respectively; which is better than methods mentioned above. The achieved results prove that the proposed approach is more robust, accurate, and straightforward.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.