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Updated: Dec 18, 2025
08:05
Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
Published on: December 19, 2020
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Automated Quantification of CT Patterns Associated with COVID-19 from Chest CT
Arxiv
|June 19, 2020
Summary
A new automated method accurately segments and quantifies COVID-19 lung abnormalities on CT scans, providing rapid severity scores for ground glass opacities and consolidations.
Area of Science:
- Radiology and Medical Imaging
- Artificial Intelligence in Medicine
- Pulmonary Diseases
Background:
- Coronavirus disease 2019 (COVID-19) presents with characteristic lung abnormalities on CT scans, including ground glass opacities and consolidations.
- Accurate segmentation and quantification of these abnormalities are crucial for assessing disease severity and patient management.
- Manual analysis of CT scans is time-consuming and subjective.
Conclusions:
- A novel automated method effectively segments COVID-19 lung abnormalities and quantifies disease severity using CT scans.
- The developed scoring system (PO, PHO, LSS, LHOS) offers a rapid and objective assessment of COVID-19 lung involvement.
- This automated approach has the potential to streamline radiological workflow and improve patient care.
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